Ellipsoidal Set Based Command Governors for Constrained Linear Systems with Bounded Disturbances

This paper provides a solution for a linear command governor (CG) that employs invariant and constraint-admissible ellipsoid. The motivation is to substitute the typical polyhedral set used in almost all CG schemes with the ellipsoidal one, which is much easier to construct. However the price for this offline computational efficiency is that the size of the feasible set can be relatively small, and the online computational burden is heavier than that of polyhedral set based CGs. The proposed solution overcomes these two weaknesses and offers a very attractive alternative to polyhedral set based CG. Two numerical examples with comparison to earlier solutions from the literature illustrate the effectiveness of the proposed algorithm.

Ellipsoidal Set Based Command Governors for Constrained Linear Systems with Bounded Disturbances

This paper provides a solution for a linear command governor (CG) that employs invariant and constraint-admissible ellipsoid. The motivation is to substitute the typical polyhedral set used in almost all CG schemes with the ellipsoidal one, which is much easier to construct. However the price for this offline computational efficiency is that the size of the feasible set can be relatively small, and the online computational burden is heavier than that of polyhedral set based CGs. The proposed solution overcomes these two weaknesses and offers a very attractive alternative to polyhedral set based CG. Two numerical examples with comparison to earlier solutions from the literature illustrate the effectiveness of the proposed algorithm.

Constrained Uncertain System Stabilization with Enlargement of Invariant Sets

An enhanced method able to perform accurate stability of constrained uncertain systems is presented. The main objective of this method is to compute a sequence of feedback control laws which stabilizes the closed-loop system. The proposed approach is based on robust model predictive control (RMPC) and enhanced maximized sets algorithm (EMSA), which are applied to improve the performance of the closed-loop system and achieve less conservative results. In fact, the proposed approach is split into two parts. The first is a method of enhanced maximized ellipsoidal invariant sets (EMES) based on a semidefinite programming problem. The second is an enhanced maximized polyhedral set (EMPS) which consists of appending new vertices to their convex hull to minimize the distance between each new vertex and the polyhedral set vertices to ensure state constraints. Simulation results on two examples, an uncertain nonisothermal CSTR and an angular positioning system, demonstrate the effectiveness of the proposed methodology when compared to other works related to a similar subject. According to the performance evaluation, we recorded higher feedback gain provided by smallest maximized invariant sets compared to recently studied methods, which shows the best region of stability. Therefore, the proposed algorithm can achieve less conservative results.