System Identification & Adaptive Control

Course content

System models: internal and external representations. Volterra and Wiener characterizations for nonlinear systems. Explicit and implicit system identification. Use of periodic test signals, binary m-sequences. On-line parameter identification; stochastic approximation, random search algorithm and the extended Kalman filter. The linear quadratic Gaussian optimal control problem. Various adaptive control strategies. Stability considerations. Learning and hierarchical intelligent control systems, bionic systems, man-machine control systems.