Inverse optimal adaptive control-The interplay between update laws, control laws, and Lyapunov functions

• Published in: 2009 American Control Conference pp. 1250 - 1255
• Main Author: Krstic, M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2009
• Subjects: Adaptive control; Automatic control; Convergence; Error correction; Lyapunov method; Optimal control; Parameter estimation; Programmable control; Riccati equations; State estimation
• ISBN: 142444523X; 9781424445233
• ISSN: 0743-1619
• DOI: 10.1109/ACC.2009.5159800

Approaching the problem of optimal adaptive control as ldquooptimal control made adaptive,rdquo namely, as a certainty equivalence combination of linear quadratic optimal control and standard parameter estimation, fails on two counts: numerical (as it requires a solution to a Riccati equation at each time step) and conceptual (as the combination actually does not possess any optimality property). In this note we present a particular form of optimality achievable in Lyapunov-based adaptive control. State and control are subject to positive definite penalties, whereas the parameter estimation error is penalized through an exponential of its square, which means that no attempt is made to enforce the parameter convergence, but the estimation transients are penalized simultaneously with the state and control transients. The form of optimality we reveal here is different from our work in [Z. H. Li and M. Krstic, ldquoOptimal design of adaptive tracking controllers for nonlinear systems,rdquo Automatica, vol. 33, pp. 1459-1473, 1997] where only the terminal value of the parameter error was penalized. We present our optimality concept on a PDE example-boundary control of a particular parabolic PDE with an unknown reaction coefficient. Two technical ideas are central to the developments in the note: a non-quadratic Lyapunov function and a normalization in the Lyapunov-based update law. The optimal adaptive control problem is fundamentally nonlinear and we explore this aspect through several examples that highlight the interplay between the non-quadratic cost and value functions.