Systems with Uncertainty. What are “Stochastic, Robust, and Adaptive” Controllers?
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Transcript of Systems with Uncertainty. What are “Stochastic, Robust, and Adaptive” Controllers?
Systems with Uncertainty
What are “Stochastic, Robust, and Adaptive” Controllers?
Stochastic OptimalControl
Deterministic versus Stochastic Optimization
Linear-Quadratic Gaussian (LQG)Optimal Control Law
Linear-Quadratic-Gaussian Control of a Dynamic Process
H
LQG Rolling Mill Control System Design Example
Stochastic RobustControl
Robust Control System Design
Probabilistic Robust Control Design
Representation of Uncertainty
Root Localizations for an Uncertain System
Probability of Satisfying a Design Metric
Design Control System to Minimize Probability of Instability
Control Design Example *
Uncertain Plant *
Parameter Uncertainties, Root Locus, and Control Law
Monte Carlo Evaluation of Probability of Satisfying a Design Metric
Stabilization Requires Compensation
Search-and-Sweep Design of Family of Robust Feedback Compensators
Search-and-Sweep Design of Family of Robust Feedback Compensators
Design Cost and Probabilities for Optimal 2nd – to 5th –Order Compensators
System Identification
Parameter-Dependent Linear System
Dynamic Model for Parameter Estimation
System Identification Using an Extended Kalman-Bucy Filter
Multiple-Model Testing for System Identification
Adaptive Control
Adaptive Control System Design
Operating Points Within a Flight Envelope
Gain Scheduling
Cerebellar Model Articulation Controller (CMAC)
CMAC Output and Training
CMAC Control of a Fuel-Cell Pre-Processor
Summary of CMAC Characteristic
Flow Rate and Hydrogen Conversion of CMAC/PID Controller
Comparison of PrOx Controllers on FUDS
Reinforcement Learning
Dynamic Models for the Parameter Vector
Inputs for System Identification
