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