James A. Primbs

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Two well known approaches to nonlinear control involve the use of control Lyapunov functions (CLFs) and receding horizon control (RHC), also known as model predictive control (MPC). The on-line Euler-Lagrange computation of receding horizon control is naturally viewed in terms of optimal control, whereas researchers in CLF methods have emphasized such(More)
A control design method for nonlinear systems based on control Lyapunov functions and inverse optimality is analyzed. This method is shown to recover the LQ optimal control when applied to linear systems. More generally, it is shown to recover the optimal control whenever the level sets of the control Lyapunov function match those of the optimal value(More)
Many control techniques employ on-line optimization in the determination of a control policy. We develop a framework which provides suucient convex conditions, in the form of Linear Matrix Inequalities (LMIs), for the analysis of constrained quadratic based optimization schemes. These results encompass standard robustness analysis problems for a wide(More)
Control Lyapunov functions (CLF’s) are used in conjunction with receding horizon control (RHC) to develop a new class of receding horizon control schemes. In the process, strong connections between the seemingly disparate approaches are revealed, leading to a unified picture that ties together the notions of pointwise min-norm, receding horizon, and optimal(More)
We present a new approach to the stability analysis of nite receding horizon control applied to constrained linear systems. By relating the nal predicted state to the current state through a bound on the terminal cost, it is shown that knowledge of upper and lower bounds for the nite horizon costs are suucient to determine the stability of a receding(More)
Issues of feasibility, stability and performance are considered for a finite horizon formulation of receding horizon control (RHC) for linear systems under mixed linear state and control constraints. It is shown that for a sufficiently long horizon, a receding horizon policy will remain feasible and result in stability, even when no end constraint is(More)
Issues of feasibility and stability are considered for a nite horizon formulation of receding horizon control for linear systems under mixed linear state and control constraints. We prove that given any compact set of initial conditions that is feasible for the innnite horizon problem, there exists a nite horizon length above which a receding horizon policy(More)
Extending the concept of solving the Hamilton-Jacobi-Bellman (HJB) optimization equation backwards [2], the so called converse constrained optimal control problem is introduced, and used to create various classes of nonlinear systems for which the optimal controller subject to constraints is known. In this way a systematic method for the testing, validation(More)
In this session, popular nonlinear control methodologies are compared using benchmark examples generated with a "converse Hamilton-Jacobi-Bellman" method (CoHJB). Starting with the cost and optimal value function V, CoHJB solves HJB PDEs "backwards" algebraically to produce nonlinear dynamics and optimal controllers and disturbances. Although useless for(More)