Efficient robust model predictive control via convex optimization : Efficiently incorporating robustness using linear

ISBN-13: 9786202203906 / Angielski / Miękka / 2017 / 168 str.

Zhaoyang Wan; Mayuresh V. Kothare

Opis

Revision with unchanged content. This research monograph develops a systematic approach to synthesize efficient robust MPC for constrained LTV systems and nonlinear systems. Specifically, (a) by using the concept of invariant sets, robustness is achieved without online computation; (b) by using a two-level control structure, optimization is separated from stabilization; (c) by constructing a continuum of terminal sets, both large operating regions and local optimality can be achieved without large number of control decision variables; (d) by decomposing a nonlinear control problem into a sequence of linear control problems, a nonlinear non-convex optimization problem is reduced to a convex optimization problem. Algorithms developed in this monograph have been formulated into linear objective minimisations subject to linear matrix inequality constraints. This optimization is convex and can be solved efficiently using interior point methods. Since state and decision variables appear linearly in the objective function and the matrix inequality constraints, linear combination of off-line MPC solutions provides a feasible solution, which can potentially replace online optimization in MPC.

O autorze

Zhaoyang Wan is currently at GE Water Technology. He received hisBS and MS degrees at Tsinghua University, China in 1994 and 1997,and his PhD at Lehigh University, USA in 2003.Mayuresh V. Kothare is the R. L. McCann Professor of ChemicalEngineering at Lehigh University. He holds a PhD from Caltech(1997) and a BTech from IIT Bombay (1991).