ABSTRACT
This chapter discusses the advanced topics and issues involved in the design and analysis of control systems. It presents the subjects of discrete-time estimation both state-space and information space, optimal stochastic control, and nonlinear control systems. The chapter also discusses the principles and concepts of estimation. An estimator is a decision rule that takes as an argument a sequence of observations and computes a value for the parameter or state of interest. The Kalman filter is a recursive linear estimator that successively calculates a minimum variance estimate for a state that evolves over time, on the basis of periodic observations that are linearly related to this state. The development of linear estimators can be extended to the problem of estimation for nonlinear systems. The Kalman filter has found extensive applications in such fields as aerospace navigation, robotics and process control.
