Abstract
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.
Highlights
Model predictive control (MPC) is a main concern for control design applied in different systems such as linear or nonlinear [1,2,3], continuous or discrete [4], and monovariable or multivariable [5]
An enhanced method able to perform accurate stability of constrained uncertain systems is presented. e main objective of this method is to compute a sequence of feedback control laws which stabilizes the closed-loop system. e 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
The proposed approach is split into two parts. e first is a method of enhanced maximized ellipsoidal invariant sets (EMES) based on a semidefinite programming problem. e 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
Summary
Model predictive control (MPC) is a main concern for control design applied in different systems such as linear or nonlinear [1,2,3], continuous or discrete [4], and monovariable or multivariable [5]. Erefore, a robust model predictive control (RMPC) has been introduced to guarantee robustness as well as constraint satisfaction against uncertainty. Model predictive control is an interesting approach to represent systems using fuzzy logic for designing controllers. Several works have focused on the use of fuzzy-model-based sliding mode control of nonlinear systems in combination with MPC algorithms [11,12,13,14]. The fuzzy logic technique is quite attractive in terms of time, simplicity of implementation, relatively low cost, and ability to rapidly model complex systems
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