Contention-resolving model predictive control for coupled control systems with a shared resource
| Autor: | Fumin Zhang, Michael Malisoff, Ningshi Yao |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Optimization problem Computer science 020208 electrical & electronic engineering 02 engineering and technology Networked control system Optimal control Scheduling (computing) Rendering (computer graphics) Shared resource Model predictive control 020901 industrial engineering & automation Control and Systems Engineering Control system 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering |
| Zdroj: | Automatica. 122:109219 |
| ISSN: | 0005-1098 |
| DOI: | 10.1016/j.automatica.2020.109219 |
| Popis: | Priority-based scheduling strategies are often used to resolve contentions in resource constrained control systems. Such scheduling strategies inevitably introduce time delays into controls and may degrade the performance of control systems. Considering the coupling between priority assignment and control, this paper presents a method to co-design priority assignments and control laws for each control system, which aims to minimize the overall performance degradation caused by contentions. The co-design problem is formulated as a mixed integer optimization problem with a very large search space, rendering difficulty in computing the optimal solution. To solve the problem, we develop a novel contention-resolving model predictive control method to dynamically assign priorities and compute an optimal control. The priority assignment can be determined using a sample-based approach without excessive demand on computing resources, and optimal controls can be computed iteratively following the order of the assigned priorities. We apply the proposed contention-resolving model predictive control to co-design scheduling and controls in networked control systems. We present simulation results to show the effectiveness of our proposed method. |
| Databáze: | OpenAIRE |
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