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CHAPTER 2

LITERATURE REVIEW

2.1 GENERAL

This chapter summarizes the literature survey that was conducted

as a part of the research reported in this thesis. It covers pertinent established

concepts and techniques related to multiobjective cascade control system

design for servo and regulatory processes. Simulation and real time

implementation results for servo and regulatory process using conventional

and intelligent control techniques employed in various literature are analyzed

and discussed. Literature survey was conducted on various optimization

techniques involved in optimizing the gains of different controllers with

single and multiple conflicting objectives. A detailed survey was conducted

on various evolutionary multiobjective optimization techniques and the

performances of the algorithms in optimizing the controller structure in both

simple feedback and cascade modes of operation are discussed.

2.2 LIQUID LEVEL CONTROL SYSTEMS

Liquid level loops are commonly encountered in process industries.

Since the desired production rates and inventories are achieved through the

proper control of flows and levels, level control is quite important for the

successful operation of many chemical plants as proposed by Marlin (1995).

The industrial importance of level loops has led to extensive research interest

to achieve the enhanced control performance of the level loop.

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2.2.1 Simple Feedback Control

A lot of literature focuses on control of liquid level as a simple

feedback loop. The research activities done in these areas reflect the interest

in improving the operation and control of highly nonlinear process in single

loop mode.

Literature based on control of liquid level with a single objective

function is presented and discussed. Both conventional and intelligent tuning

techniques proposed in various literature are discussed in this section.

2.2.1.1 Conventional methods

Luyben and Buckley (1977) have proposed a Proportional-Lag (PL)

control as a potentially good solution to the problem of liquid level control

systems with feed forward compensation from the inlet flow. Proportional-

Lag control is a potentially good solution for liquid level control systems with

feed forward compensation. In PL control, the proportional control based

feedback loop provides flow smoothing while the feed forward compensation

eliminates the steady state offset in liquid level. The limitation of such feed

forward control schemes is that they require an additional measurement which

may be unavailable.

Cheung and Luyben (1979) have studied the liquid level control

system with P-only and PI feedback controllers. They adopted a tuning

procedure of the Proportional-Lag (PL) controller with a design chart using

specifications for the maximum level deviation and maximum rate of change

of the manipulated flow to achieve optimal filtering for inlet flow step

changes. It is concluded that PL controller does not offer much of an

improvement compared to a standard PI Controller.

21

Rivera et al (1986) have proposed the P-only controller using the

Internal Model Control (IMC) principle for the critically damped closed loop

response of a liquid level control system to determine the PID parameters to

ensure a desired closed loop response to the step change in set point. IMC

method employs the desired closed loop response as first order lag system

with dead time without overshooting, while providing adequate suppression

of output disturbances, do a poor job of suppressing the load disturbances

when the process dynamics are significantly slower than the desired closed

loop dynamics.

McDonald et al (1986) have proposed an interesting method of

deriving an averaging level control algorithm to minimize the maximum rate

of change of the manipulated flow. The continuous time optimal level

controller, which minimizes the maximum rate of change of the outlet flow,

was derived and is found to achieve good results even in the presence of

disturbances. A limitation is that they are hard to tune, and also this method

allows excessive volume changes.

Wu et al (2001) have proposed a two degree of freedom control

structure to address both the regulatory and servo problems in level control. A

two degree-of-freedom scheme is proposed for the level control systems. It is

shown by the author that the scheme gives satisfactory responses for both the

systems without dead time and with small dead time and also suggested that

the controllers should be designed according to operational specifications

namely maximum rate of change in outflow and maximum peak height for a

given inlet flow variation. In spite of its industrial and economic importance,

the constrained optimal control strategy has rarely been employed in practical

level loops. One of the main reasons is that most industrial level loops use

simple P-only or PI controllers, which are generally accepted as being too

simple to implement any sophisticated control strategy. The main obstacle to

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achieving optimal control is that it normally requires an optimization package

to find the optimal control action, and many practitioners are not familiar with

the use of these complicated packages. Limitation is that, for large dead times

involved in the process, performance of the system becomes unsatisfactory.

Using a sophisticated advanced controller such as a model predictive

controller might be a solution for the constraint control of the level loop, but it

cannot be considered as a practical approach.

2.2.1.2 Intelligent techniques

Conventional control approaches are not convenient to solve the

complexities. Fuzzy logic and neural networks control have emerged over the

years and become one of the most active and fruitful areas of the research in

the intelligent control applications. Fuzzy logic, neural networks and genetic

algorithms are three popular artificial intelligence techniques that are widely

used in many applications. Due to their distinct properties and advantages,

they are currently being investigated and integrated to form new models or

strategies in the areas of system control.

Seng et al (1998) have proposed a neuro-fuzzy controller to a

coupled-tank liquid-level laboratory process based on the radial basis function

neural network tuned automatically using genetic algorithms (GA). They have

used a linear mapping method to encode the GA chromosome, which consists

of the width and centre of the membership functions, and also the weights of

the controller. Dynamic crossover and mutation probabilistic rates are also

applied for faster convergence of the GA evolution. Compared to a manually

tuned conventional fuzzy logic controller and a Proportional-Integral-

Derivative (PID) controller which are applied to the same process, the

proposed controller shows considerable robustness and advantages. Human

expert intelligence in framing the rule base of fuzzy logic controller is a major

limitation.

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Naman et al (2000) have presented an adaptive model reference

fuzzy controller (AMRFC) for controlling the water level in a water tank.

Performance is compared with conventional methods of proportional-integral

(PI) control and Model Reference Adaptive Control (MRAC). Unlike most of

the literature reviewed which use the error and error change as inputs to the

fuzzy system, this method uses the theoretical background developed for

MRAC in choosing these inputs. Although the controller uses many inference

rules (441 rules), it is shown that the required mathematical calculations are

not much, making implementation on a low-end microcontroller feasible. The

control algorithms are implemented in simulation and real-time on an 8-bit

microcontroller. It was found that the MRAC proved to be better compared to

the PI controller. Limitation is the similarity in performance due to the

linearity of the plant.

Han (2006) have proposed an adaptive neural network control

strategy based on fuzzy self-tuning to control the drum water level of coal-

fired power plant. Fuzzy Inference Engine (FIE) is used to train neural

network online. The control strategy possesses feed forward compensation

ability for steam flow disturbance by introducing the steam flow signal to

neural network controller. Robust controller is constructed to guarantee good

regulating performance while dynamic behaviour of the controlled plant

changes or external steam flow disturbances exists. In contrast to

conventional cascade PID control, simulation results show the efficiency and

superiority of the proposed strategy.

Yazdizadeh et al (2009) have proposed two novel adaptive PID-

like controllers such as Neural network PID and Neural network PID with

internal dynamic feedbacks for controlling multivariable, nonlinear Multiple-

Input Multiple-Output (MIMO) systems. Comparative analyses of the novel

adaptive controllers are tested with conventional methods and results confirm

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that the algorithm exhibits excellent performance. It has been applied to

control the water level of tanks in water refinement process, which is highly

non linear and good performance and stability was achieved. Limitation is the

learning rate and the system disturbances are not taken into consideration for

the design.

Hasan et al (2011) have investigated and found a solution by

designing the intelligent controller such as neural network for controlling the

water level system. The controller also can be specifically run under the

circumstance of system disturbances. To achieve these objectives, a prototype

of water level control system has been built and implementations of both PID

and neural network control algorithms are performed. In PID control, Ziegler

Nichols tuning method is used to control the system. In neural network

control, the approach of Model Reference Adaptive Neural Network

(MRANN) control based on the back propagation algorithm is applied on

training the system. Both control algorithms are developed to embed into a

standalone DSP-based micro-controller and their performances are compared.

Limitation is that the non linear process characteristics are not preserved.

Jun et al (2011) has introduced a Modified Particle Swarm

Optimization (MPSO) for tuning the parameters of PID controller on boiler

drum water level. Compared with Basic Particle Swarm Optimization

(BPSO), MPSO enhances the searching ability, prevents particles falling into

the local minimum and makes the searching of global optimal solution more

efficiently by adopting solution sharing and searching range sharing. A new

error criterion was proposed to validate the performance of PID controller

based on MPSO (MPSO-PID). The experimental results show that the MPSO-

PID achieves better time response than the PID controller based on BPSO

(BPSO-PID). A major drawback is the increase in execution time.

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2.2.2 Cascade Control

Literature survey on liquid level control system with level as

primary process and flow as secondary process has been carried out and the

outcomes of the various literature are presented and discussed. Literature on

cascade control of liquid level process, based on single objective optimization

using conventional and intelligent techniques are presented in this section.

2.2.2.1 Conventional methods

Sadasivarao and Chidambaram (2006) have applied a simple

genetic algorithm for tuning PID controllers of the cascade control systems. A

methodology for selecting the search region is proposed using Ziegler–

Nichols tuning method. Stability and robustness criteria are ensured in the

selection of the search region, enabling the method to be applicable to online

tuning. The inner and outer loops are tuned simultaneously, making the

method applicable without disturbing the control strategy and ensuring overall

optimal solution. Integral Absolute Error (IAE) values of the regulatory

response is used as the objective function. The results show the superiority of

genetic algorithm (GA) over the other methods. However, this algorithm fails

to yield better results in terms of time domain specifications.

Chen et al (2008) have proposed a cascade integral sliding mode

control for a water tank level control system to realize level position

regulating and tracking control. The key feature of this control scheme is the

use of cascade back-step design method for the cascade nonlinear water tank

level system to improve the control performance. The validity of the proposed

control scheme was verified through practical testing on an experimental tank

level system device. In the cases of step, multi step, and sinusoidal level

position command inputs, the test results show that the proposed control

scheme is capable of improving the tracking precision.

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The limitation (Sadasivarao and Chidambaram 2006 and Chen et al

2008) of the above literature is that the control is based on single objective,

even if the process has multiple objectives.

2.2.2.2 Intelligent techniques

Tunyasrirut and Wangnipparnto (2007) have developed a cascade

control scheme with fuzzy logic controller in primary (level) loop and

conventional PID in secondary (flow) loop in a liquid level control system to

control the level of horizontal tank that has diameter 300 mm and 480 mm

long. Interface card module in computer and LabVIEW software program is

used for building the cascade controller. The inner loop uses a PID controller

for regulating the flow rate of the system and outer loop uses a fuzzy logic

controller to control the level. The response time, steady state error, load

disturbance and control valve action of cascade control system are tested and

compared with the simple controller. The experimental results shows that for

the same water level of 50% set point, the rising time noticed with the cascade

controller was less than the simple controller about 1750 ms, and has a steady

state error less than simple controller of about ±1%. The load disturbance on

the plant has no affect when using the cascade controller. The cascade

controller that comprises of the PID and the fuzzy logic control improves the

dynamic characteristics of the liquid level control system. Limitation is that

an increase in settling time of 100 sec is noticed with this method.

Kumar et al (2008) have proposed a cascade control strategy with

combined Fuzzy PI and Fuzzy PD in primary loop and conventional PI in

secondary loop in a liquid level control system and achieved a settling time of

194 sec and overshoot of 4.5%. A comparative study was carried out to

evaluate the real time performance of Fuzzy Proportional-Integral plus Fuzzy

Proportional-Derivative (Fuzzy PI + Fuzzy PD) controller with the real time

performance of conventional PI controller for a liquid level process. The

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process considered for this experiment shows highly nonlinear behaviour due

to equal percentage pneumatic control valve. National Instruments

based hardware and software tools (LabVIEW) were used for precise

and accurate acquisition, measurement and control. The real time

implementation of the Fuzzy PI + Fuzzy PD controller was carried out in two

configurations namely, feedback and cascade. In cascade control

configuration, Fuzzy PI + Fuzzy PD controller was implemented in the

primary loop. The secondary loop was tuned using the conventional PI

controller and the results illustrate that that fuzzy controller perform better in

comparison with conventional controller in both the feedback and cascade

control configurations. Limitation is that the tuning of controllers becomes a

difficult task when fuzzy logic controllers are combined with PI and PD

controllers.

Sangeetha et al (2012) have proposed a PID based cascade control

scheme for the regulation of level in level control systems through

Supervisory Control and Data Acquisition System (SCADA) – Programmable

Logic Controller (PLC) – Open Process Control (OPC) interface and a

network architecture with no overshoot. The cascade control system was the

combination of level (primary process) and flow (secondary) processes. The

SCADA was developed with PID controller. The characteristics of the

cascade control system have been analyzed through the performance indices,

such as peak time, rise time and settling time. The error values such as

Integral Square Error (ISE) and Integral Absolute Error (IAE) are also

calculated. The performances of cascade control system are validated through

number of architectures, such as SCADA, PLC, OPC and internet. The

introduction of PLC and National Instruments NI-OPC server has

significantly improved the performance of conventional processes such as

level, flow and cascade control systems. However a huge settling time of

107seconds is noticed with this method.

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2.3 DC SERVO MOTOR CONTROL SYSTEM

DC servo motors play a vital role in all the process industries.

Development of accurate controllers for such processes becomes a difficult

task. Literature that focus on DC servo motor control systems are discussed in

this section.

2.3.1 Simple Feedback Control

A lot of literature focuses on speed control of dc servo motor as

simple feedback loop. The research activities done in these areas reflect the

interest in improving the capability of control for servo process. Literature

based on speed control with single objective function in single feedback loop

are presented and discussed. Both conventional and intelligent tuning

techniques proposed in various literature are discussed in this section.

2.3.1.1 Conventional methods

Weber (1965) have proposed a Pulse Width Modulation (PWM)

based DC motor control to control the speed of a DC series field motor at

different required torque levels by adjusting the voltage applied to the motor.

For any particular constant voltage, the motor speed was determined solely by

the torque requirements and top speed is reached under minimum torque

conditions. A series motor was used as a traction drive for vehicles. Voltage

to the motor is controlled to fit the various torque requirements of grades,

speed and load. The common method of varying the speed of the motor is by

inserting resistance R in series with motor to reduce the supplied power. This

type of motor speed control is very inefficient due to the wasteful of battery

power due to the I2R loss, especially under high current and high torque

conditions.

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Chevrel et al (1996) have presented a methodical approach based

on switched quadratic regulators for designing an efficient DC motor speed

controller. This control strategy is compared with two cascade control design

methods in terms of performances, robustness and complexity. Switched LQ

(Linear Quadratic) regulators were particularly well suited to manage the

performance-robustness trade off. The performance robustness has been

proved for the strategies, in spite of large parametric uncertainties. Stability

analysis of the system was not carried out to demonstrate whether the system

is stable or not.

Chevrel and Siala (1997) have proposed the speed control of a DC

servo motor when the current is the only one measurement. A LQG (Linear

Quadratic Gaussian) controller is designed by assembling an integral LQ state

feedback and a Kalman observer to estimate the speed and the load torque.

Its robustness and performances are discussed and experimentally tested. The

current is limited by switching from the speed regulator to a current one when

necessary. A control design was presented that performs well the compromise

between speed control performances, robustness and current limitation of a

dc-motor as no mechanical sensor is available. Especially good results were

experimentally obtained when the motor parameters were identified at the

starting of the motor. It is also shown that a variation on the resistance

produces a non zero steady state error and demonstrated that no linear

regulator can solve this problem. Limitation is that, accurate speed control

without mechanical sensor includes on-line identification of the resistance

which is slowly varying and also the use of adaptive control.

Praesomboon et al (2009) have proposed a speed sensorless DC

motor control using Kalman filter. Kalman filter considers the DC motor

mathematical model. The model will start from a continuous state space into a

discrete state space form. Inputs of the system are the armature voltage and

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the armature current with noises. The output of the system is the estimated

speed. Kalman filter was used to estimate the speed without noise

interference. The result from the estimated speed was compared with the

reference speed and the speed error will be used by the controller to control

the linear amplifier. The final output was the constant speed of the DC motor.

Even though the load of the DC motor changed, the speed of the DC motor

remains constant under the control. The results illustrate that the Kalman filter

can reject the noise from the system and estimate the speed of the DC motor

with a high accuracy. The estimated speed will be feedback to control the

system for constant speed. Sampling rate is a major limitation while

converting continuous signal to discrete signal.

Bindu and Namboothiripad (2012) have proposed the position

control of DC servo motors since they are extensively deployed in various

servomechanisms. Normally PID controllers are used to improve the transient

response of DC servo motors. At present, most tuning methods are designed

to provide workable initial values, which are then further manually optimized

for a specific requirement. They have presented a flexible and fast tuning

method based on Genetic Algorithm (GA) to determine the optimal

parameters of the PID controller for the desired system specifications.

Simulation results show that a wide range of requirements with respect to PID

parameters are satisfied with the proposed tuning method. Limitation is on the

range of requirements and can be widened by increasing the range of initial

population but the number of generations required to converge to optimal

value may increase.

2.3.1.2 Intelligent techniques

Lin (1994) employed an inference method to develop the control

law and applied it to the speed control of a DC servo motor system. Stability,

robustness and other behaviours of the fuzzy controller are compared with the

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classical PI controller. A mathematical model is developed, and used in the

computer simulation to aid the selection of the control parameters. Also, the

feasibility of the fuzzy controller is validated by laboratory experiments.

Domain expert’s knowledge on framing the rule base of fuzzy logic controller

is a major limitation.

Allaoua et al (2001) have proposed an Adaptive Neuro-Fuzzy

Inference System (ANFIS) for the speed control of DC servo motor optimized

with swarm collective intelligence. Initially, controller is designed based on

fuzzy rules and then an adaptive neuro-fuzzy mechanism is adopted and

ANFIS is optimized by Swarm Intelligence. ANFIS has the advantage of

expert knowledge of the Fuzzy inference system and the learning capability of

neural networks. Simulation results demonstrate that the deigned ANFIS-

Swarm speed controller realize a good dynamic behaviour of the DC motor, a

perfect speed tracking with no overshoot, gives better performance and high

robustness than those obtained by the ANFIS alone. Adequate knowledge of

experts required to design fuzzy logic rules for servo motor control is a major

drawback.

Kang and Kim (2001) have designed a neuro-fuzzy controller to

improve some problems that occur when the non linear system

is controlled by a fuzzy logic controller. Their model obtains fast time

response, maximized learning effect and shortened settling time. To prove the

capability of the designed neuro-fuzzy controller, the neuro-fuzzy model is

applied to a DC servomotor. As a result, this controller does not produce

overshoot, which occurs in the PID controller, and also does not produce the

steady state error of FLC. Also, it shortens the settling time by about 10%.

The model has only about 60% of the value of current peak of the PID

controller. Limitation is that the training algorithm and the learning rate of

neural networks have to be properly selected.

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Liu et al (2003) have proposed a non linear Multiple-Input

Multiple- Output (MIMO) feedback linearization technique to a separately

excited DC motor system that is operated in high speed field-weakening

regime. Load adaptive and sensorless control techniques are adopted to

improve dynamic speed performance. Based on non linear MIMO feedback

linearization, several non linear control techniques have been investigated for

speed tracking performances. Under unknown load torque disturbance, the

design with the linear control law obtained yields the steady-state errors as

well as the degraded system responses as a result of the incomplete

linearization. This problem cannot be effectively tackled by introducing an

integrator. To overcome this limitation, load adaptive control design has been

developed to improve the dynamic control performance. The main advantage

of the scheme is that it uses only current measurements, eliminating costly

speed sensors. Because of the decoupling and linearization, control

implementation of the motor speed and back emf is achieved independently

and the designs are able to assure speed tracking at any desired field point.

High dynamic tracking performance is achieved even when the system with

unknown load torque is operated in wide dynamic regimes of field

weakening. Design of reference model for unknown load torque disturbance

requires adequate knowledge is a major limitation.

Thepsatorn et al (2006) have implemented the speed control of a

separately excited DC motor using fuzzy logic control (FLC) based on

LabVIEW. The results of experiment on the real plant demonstrate that the

fuzzy logic controller is sensitive to variations of the reference speed

attention. It was shown that the controller gains optimal performance and

overcomes the disadvantage of the conventional control of sensitiveness to

inertia variation and variation of the speed with drive system of DC motor. A

limitation is that the load disturbances are not taken into account while

designing the controller.

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Gui et al (2006) have implemented a three-phase full control

rectification circuit controller which is widely used in the system of speed

regulation of DC motor. Conventional PID controller with double closed-

loops has been used in speed control of separately excited DC Motor. Under

normal operation, it is difficult to achieve high performance, because of the

low robustness of conventional PID controller. In order to overcome these

problems, fuzzy self adaptive PID control method under fuzzy control theory

and DSP (Digital Signal Processing) based cascade control system is designed

to regulate the speed of DC motor. The practicality and validity of the control

method are confirmed from the simulation results. The robustness of the

system is improved and fuzzy self-adaptive PID controller has better tracking

and anti-jamming performance than conventional PID controller. Limitation

is the lack of domain expert’s knowledge to develop fuzzy rule base.

Shaker and Khashab (2010) have designed and tested an integrated

electronic system that utilizes an interface card through the parallel port in

addition to some auxiliary circuits to perform fuzzy control operations for DC

motor speed control with load and no load. Software is constructed and the

parameters for fuzzy logic controller (membership function, rules and scaling

factor) and evaluation of the gain factors (Kp, Ki and Kd) by Ziegler-Nichols

method are performed. The design dramatically reduced the hardware to the

least possible. It is capable of enhancing the system performance by altering

the Membership Functions (MF’s) and the fuzzy rules in order to obtain the

optimal result by monitoring the speed response of the motor. In Fuzzy Logic

Controller (FLC), the corresponding values of all parameters (overshoot,

settling time, rise time, steady state) are very less in comparison with the

parameters of the conventional control and has higher stability, reaching the

desired speed in a shorter time, but requires more tuning than conventional

control, because FLC requires more computation parameters (MF’s, rules

and additional scaling factor in the inputs and output) unlike conventional

34

control (gain factors for inputs only). To achieve better performance and less

utilization of available hardware resources, the work had been built to reduce

the total number of used rules and to eliminate those which are not effective

on the system. From experience, accurate results can be achieved if the

relationship between the FLC & conventional control parameters were

computed. Limitation is that the system software was more complex.

Verma and Jain (2011) have proposed a new approach to control

the speed of linear brushless DC motor and presented an overview of

Performance Dependant Particle Swarm Optimization (PDPSO), as an

alternative to evolutionary algorithm. Performance Dependent Particle Swarm

Optimization(PDPSO) is used to determine the optimal gains of Proportional-

Integral- Derivative controller (PID). Robustness of the system under critical

conditions is improved when conventional optimization methods fail. PDPSO

method is used to obtain optimum gains of PID controller for DC motor.

PDPSO is new variant of PSO with faster speed because of strong selection

principle. In simple PSO, after certain iterations, the populations set are

almost identical and no further improvement is observed. PDPSO utilizes the

global and local best value to search optimal setting of the state variable by

considering security constraints. Like any other algorithms, the positive

aspect of this method is its reliability and the number of required generation

for convergence decreases with increase of population size and the

performance is found to better than simple PSO-PID controller. Limitation of

this method is that it produces sluggish response.

2.3.1.3 Multiobjective approach

Literature related to multiobjective approaches to cascade control

system design and its advantages and limitations are discussed and presented.

35

Gammel and Samahy (2009) have presented a control scheme

based on MultiObjective Particle Swarm optimization (MOPSO), which is

able to tune the PID controller parameters simultaneously in order to obtain

the set of trade off optimal solutions called pareto set optimization solution

for the conflicting objective functions of DC motor drive system. Multi

Objective Particle Swarm Optimization (MOPSO) is implemented to tackle a

number of conflicting goals that define the optimality problem. Five

conflicting objective functions considered are minimization of maximum

overshoot, rise time, speed tracking error, steady state error and settling time.

Limitation is a good trade off between overshoot and settling time is not

obtained due to the lack of non dominated sorting procedure in MOPSO.

2.3.2 Cascade Control

Literature survey on DC servo motor control system with speed in

the primary loop and armature current in the secondary loop has been carried

out and the outcomes of the various literature are presented and discussed.

Most of the literature on cascade control of DC servo motor are

based on single objective optimization techniques. Conventional and

intelligent techniques applied to the servo process in various literature are

presented in this section.

2.3.2.1 Conventional methods

Stephan et al (1988) have proposed a cascade speed control strategy

for a thyristor driven DC motor subject to parameter variations. A dual mode

adaptive inner current loop is cascaded with a model reference adaptive speed

control loop to achieve better performance and this approach guarantees a

stable and predefined performance at all the operating conditions.

Comparisons with conventional analog speed control in continuous and

36

discontinuous current mode are made. Variations of the moment of inertia,

field excitation and load torque were investigated. Model Reference Adaptive

Control uses a reference model and an expertise in the field is required for

design which becomes a major limitation.

Alfaro et al (2008) have proposed a design approach for Two-

Degree-of-Freedom (2-DOF) PID controllers within a cascade control

configuration that guarantees smooth control. The rationale of operation

associated to both the inner and outer controllers, determines the need of good

performance for disturbance attenuation (regulation) as well as set-point

following (tracking). It provides the complete set of tuning parameters for the

inner (2-DOF PI) controller and the outer (2-DOF PID) controller and the

design equations are formulated in such a way that a non oscillatory response

is specified for both the inner and outer loop. The advantage of providing the

complete set of parameters is that it avoids the need for the usual

identification experiment for the tuning of the outer controller. However the

use of 2-DOF controllers introduces additional parameters that need to be

tuned appropriately which becomes a major limitation.

2.3.2.2 Intelligent techniques

Pisano et al (2008) have presented a novel scheme for the speed

and position control of Permanent Magnet (PM) DC motor drives. A cascade

control scheme, based on multiple instances of a second order sliding mode

control (2-SMC) algorithm is suggested, which provides accurate tracking

performance under large uncertainty about the motor and load parameters.

The overall control scheme is composed of three main blocks, a 2-SMC based

velocity observer which uses only position measurements, a 2-SMC based

velocity control loop that provides a reference command current and a

2-SMC-based current control loop generating the reference voltage. The

scheme has been implemented and tested experimentally on a commercial

37

PMDC (Permanent Magnet Direct Current) motor drive. The experimental

results confirm the precise and robust performance and the ease of tuning and

implementation. A major limitation is the oscillations about the set point.

2.3.2.3 Multiobjective approach

Literature related to cascade control of DC servo motor involving

multiple objective functions are discussed and presented.

Wang et al (2004) have proposed a genetic algorithm (GA) based

multiobjective optimization evolutionary algorithm (MOEA) approach to take

care of load disturbances. In traditional PI-type control strategies, the current

controller is tuned first and then the speed controller is tuned. This often leads

to a set of non optimal solutions. This control algorithm simultaneously tunes

both the controllers in order to obtain the globally optimum control systems.

Using the genetic algorithm based MOEA, the Pareto-set optimization

solutions are evolved successfully. Simulation results verify the effectiveness

of the proposed controller design approach. This control scheme can also be

applied to ac drives such as vector controlled asynchronous drives without

significant modifications. Multiple conflicting objectives and time domain

specifications were not taken into consideration and hence the design

approach failed to produce better results in terms of overshoot and settling

time.

Bottura and Serra (2004) have proposed a neural gain scheduling

multiobjective genetic fuzzy PI control for DC servo motor systems. A

discrete time version of a fuzzy PI controller is developed and the data bases

as well as the constant PI control gains are optimally designed by using a

genetic algorithm for simultaneously satisfying the following specifications,

overshoot and settling time minimizations and output response smoothing.

Hence, the optimization problem is a multiobjective one, from which results

38

an optimal fuzzy PI controller. A neural gain scheduler is designed, by the

hack propagation algorithm, to tune the optimal parameters of the fuzzy PI

controller at some operating points. Simulation results are shown to

demonstrate the efficiency of the proposed structure for a DC servomotor

adaptive speed control system used as an actuator of robotic manipulators. In

spite of the advantages of the proposed approach, the limitation is that, since

all the intelligent techniques are incorporated in the design, the control task

becomes more complicated.

2.4 MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS

A detailed survey was conducted on various multiobjective

evolutionary algorithms used for multiobjective optimization and some of the

literature are discussed and presented.

Kuhn and Tucker (1951) have developed the first technique for the

generation of non inferior solutions for multiobjective optimization. The main

strength of this method is its efficiency and its suitability to generate a

strongly non dominated solution that can be used as an initial solution for

other techniques. Its main weakness is the difficulty to determine the

appropriate weights that can appropriately scale the objectives when enough

information about the problem is not available, particularly if it is considered

that any optimal point obtained will be a function of such weights. Still more

important is the fact that this approach does not generate proper Pareto

optimal solutions in the presence of non-convex search spaces regardless of

the weights used.

Schaffer (1985) have developed an approach to use an extension of

the Simple Genetic Algorithm (SGA) called the Vector Evaluated Genetic

Algorithm (VEGA) that differed from SGA only in the way in which

selection was performed. This operator was modified so that at each

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generation a number of sub populations were generated by performing

proportional selection according to each objective function in turn. Limitation

is that the generated solutions are non dominated in a local sense but their

non-dominance was limited to the current population. Also, problem of

genetics known as “speciation” i.e. the evolution of “species” within the

population which excel on different aspects of performance arises because

this technique selects individuals who excel in one dimension of performance,

without looking at the other dimensions.

Fonseca and Fleming (1993) have proposed a scheme in which the

rank of a certain individual corresponds to the number of chromosomes in the

current population by which it is dominated. The main advantage is that

population sorting is done according to their rank. Fitness assignment is likely

to produce a large selection pressure that might produce premature

convergence was the major drawback.

Srinivas and Deb (1995) have proposed the Non dominated Sorting

Genetic Algorithm (NSGA) based on several layers of classifications of the

individuals. Before selection is performed, the population is ranked on the

basis of non domination and all non dominated individuals are classified into

one category with a dummy fitness value, which is proportional to the

population size, to provide an equal reproductive potential for these

individuals. To maintain the diversity of the population, these classified

individuals are shared with their dummy fitness values. This group of

classified individuals is ignored and another layer of non-dominated

individuals is considered. The process continues until all individuals in the

population are classified. A stochastic remainder proportionate selection was

used for this approach. Since individuals in the first front have the maximum

fitness value, they always get more copies than the rest of the population. This

allows searching for non dominated regions, and results in quick convergence

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of the population toward such regions. The efficiency of NSGA lies in the

way in which multiple objectives are reduced to a dummy fitness function

using a non dominated sorting procedure. Both maximization and

minimization problems can be handled using this approach. High

computational complexity, non elitism approach and the need for specifying a

sharing parameter was the major limitation.

Deb et al (2002) have suggested a non dominated sorting based

MultiObjective EA (MOEA), called Non dominated Sorting Genetic

Algorithm-II (NSGA-II), a fast non-dominated sorting approach with low

computational complexity which alleviates all the difficulties in NSGA. It

uses a selection operator that creates a mating pool by combining the parent

and offspring populations and selecting the best (with respect to fitness and

spread) solutions. Simulation results on difficult test problems show that the

proposed NSGA-II, in most problems is able to find much better spread of

solutions and better convergence near the true Pareto-optimal front compared

to Pareto-archived evolution strategy and strength-Pareto EA, two other elitist

MOEAs that pay special attention to creating a diverse Pareto-optimal front.

Simulation results of the constrained NSGA-II on a number of test problems

including a five objective seven constraint nonlinear problem are compared

with another constrained multiobjective optimizer and much better

performance of NSGA-II is observed.

Coello and Lechuga (2002) have introduced a proposal to extend

the heuristic called "Particle Swarm Optimization" (PSO) to deal with

multiobjective optimization problems. This approach uses the concept of

Pareto dominance to determine the flight direction of a particle and it

maintains previously found non dominated vectors in a global repository that

is later used by other particles to guide their own flight. The approach is

validated using several standard test functions from the specialized literature.

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The results indicate that this approach is highly competitive with current

evolutionary multiobjective optimization techniques as NSGA-II and other

techniques.

Liu (2008) developed a new non-dominated sorting particle swarm

optimization (NSPSO) that combines the operations (fast ranking of non-

dominated solutions, crowding distance ranking and elitist strategy of

combining parent population and offspring population together) of a known

MOGA, NSGA-II and the other advanced operations (selection and mutation

operations) with a single Particle Swarm Optimizer (PSO). The efficiency of

this algorithm is demonstrated on two test functions and the comparison is

made with the NSGA-II and MultiObjective Particle Swarm Optimization

(MOPSO). The simulation results suggest that the proposed optimisation

framework is able to achieve good solutions as well diversity compared to

NSGA-II and MOPSO optimisation framework.

2.5 SUMMARY

Various literature on conventional and intelligent control of liquid

level control system in case of simple feedback and cascade control system

with single and multiple objectives are discussed. Literature that focus on

speed control of DC servo motor using conventional and intelligent

approaches with single and multiple objectives are presented. A brief survey

of the state of art multiobjective evolutionary algorithms are reviewed and

discussed. The review of the literature shows that there is still considerable

challenges in the application of multiobjective evolutionary algorithms for

servo and regulatory processes and remains an interesting research activity,

and hence the research work. This work focuses on applying the evolutionary

multiobjective algorithms such as NSGA-II and NSPSO to cascade control of

servo and regulatory processes to provide better response in terms of various

time domain specifications in comparison with the existing techniques.