Stochastic Models, Estimation, and ControlThis volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws. |
Contents
| 1 | |
Chapter 14 Linear stochastic controller design and performance analysis | 68 |
Chapter 15 Nonlinear stochastic controllers | 223 |
Common terms and phrases
adaptive controller applied asymptotically asymptotically stable Automat backward BIBO stability certainty equivalence closed-loop system command computational Consider constant constant-gain control function control law control system controlled variables controller based controller design corresponding cost function covariance defined desired developed discrete-time dynamic programming dynamic programming algorithm eigenvalues estimation evaluated Example extended Kalman filter feedback controller full-state feedback Gaussian noise IEEE IEEE Trans implementation incremental form input linear regulator LQ optimal LQG controller LQG synthesis matrix minimize noise-corrupted measurements nominal nonlinear nonzero open-loop optimal control optimal regulator optimal stochastic controller output performance PI controller position form positive definite pseudointegral quadratic cost regulation error result Riccati equation robustness sample period scalar Section setpoint solution solving stability steady stochastic optimal structure sufficient statistic system described t₁ ti+1 tn+1 truth model u(t₁ vector white Gaussian noise x(t₁ x(to x₁ ya(t yields z(t₁ zero zero-mean white Gaussian