David Q. Mayne

Learn More
Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a "nite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and the "rst control in this sequence is applied to the plant. An(More)
Linear matrix inequality (LMI) based optimization methods are applied to the problem of designing a model predictive controller for an uncertain constrained linear system. The control signal is specified in terms of both feedback and feedforward components, where the feedback is designed to maintain the state within a prescribed ellipse in the presence of(More)
State estimator design for a nonlinear discrete-time system is a challenging problem, further complicated when additional physical insight is available in the form of inequality constraints on the state variables and disturbances. One strategy for constrained state estimation is to employ online optimization using a moving horizon approximation. In this(More)
Practical difficulties involved in implementing stabilizing model predictive control laws for nonlinear systems are well known. Stabilizing formulations of the method normally rely on the assumption that global and exact solutions of nonconvex, nonlinear optimization problems are possible in limited computational time. In this paper, we first establish(More)
The solution to the problem of optimal control of piecewise affine systems with a bounded disturbance is characterised. Results that allow one to compute the value function, its domain (robustly controllable set) and the optimal control law are presented. The tools that are employed include dynamic programming, polytopic set algebra and parametric(More)