CONSTRAINED PREDICTIVE CONTROL USING ORTHOGONAL EXPANSIONS

In this article, we approximate bounded operators by orthogonal expans ion. The rate of convergence depends on the choice of basis functions. Markov-Laguerre functions give rapid convergence for open-loop stable systems with long delay. The Markov-Kautz model can be used for light ly damped systems, and a more general orthogonal expansion is develope d for modeling multivariable systems with widely scattered poles. The finite impulse response model is a special case of these models. A-pri ori knowledge about dominant time constants, time delay and oscillator y modes is used to reduce the model complexity and to improve conditio ning of the parameter estimation algorithm. Algorithms for predictive control are developed, as well as conditions for constraint compatibil ity, closed-loop stability and constraint satisfaction for the ideal c ase. An H(infinity)-like design technique proposed guarantees robust s tability in the presence of input constraints; output constraints may give ''chatter.'' A chatter-free algorithm is proposed.