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