This chapter addresses control system design via H2 (quadratic) optimization. A unifying framework based on the concept of a generalized plant and weighted optimization permits designers to address state feedback, state estimation, dynamic output feedback, and more general structures in a similar fashion. The framework permits one to easily incorporate design parameters and/or weighting functions that may be used to influence the outcome of the optimization, satisfy desired design specifications, and systematize the design process. Optimal solutions are obtained via well-known Riccati equations; for example, Control Algebraic Riccati Equation (CARE) and Filter Algebraic Riccati Equation (FARE). While dynamic weighting functions increase the dimension of the Riccati equations being solved, solutions are readily obtained using today's computer-aided design software (e.g., MATLAB, robust control toolbox, μ-synthesis toolbox, etc.).
ASJC Scopus subject areas
- Computer Science(all)