Two-Degree-of-Freedom Control Systems

Stability Riccati equation Parameter estimation Closed Loop Process Identification Robust Stability Static Model validation State Feedback Observer Linear Nonlinear models Optimization problems Diophantine equation State-space Representation Uncertainty Models Youla Parameterization Decoupling Adaptive dual control Adaptive regulator Adaptive triple control Asymptotic variances ∞Best achievable control ∞H Cascade model Complementary sensitivity function Deadbeat regulator Generalized minimum variance regulator Continuous-time and discrete-time models Decomposition of sensitivity function Design loss Identification in closed loop Directional forgetting IMC principle D-step ahead prediction of stochastic and deterministic processes Inverse unstable process Error properties LQ regulator Minimization of the norms form of MIMO systems Forgetting strategy Linear dynamic model MIMO process General polynomial method for regulator design Model types Modeling loss Nyquist stability measure Naïve 2 Hammerstein Model Gap metrics Noise Model Predictive regulator Redesign of reference model Pole cancellation pole placement Iterative tuning Regulator robustness Stability measures Simultaneous identification and control Stability criteria Sensitivity Function Kalman equation Two-degree-of-freedom control system Static model Keviczky-Banyasz parameterization Wiener Model Youla-KuÄ?era parameterization MFD representation MIMO regulator Minimum variance regulator Prediction error method Off-line identification Performance and robustness SISO and MIMO model identification Recursive parameter estimation Realizability loss Two-step design