Systems with
Uncertainty
What are “Stochastic, Robust,
and Adaptive” Controllers?
Stochastic
Optimal
Control
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
Robust
Control
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 PreProcessor
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