期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 5, 页码 3902-3913出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3020253
关键词
Computational modeling; Generators; Genetic algorithms; Sliding mode control; Optimization; Uncertainty; Stability analysis; Event-triggered mechanism; genetic algorithm (GA); model-based networked control; sliding-mode control (SMC)
类别
资金
- Research Grants Council of the Hong Kong Special Administrative Region, China [CityU 11202819]
- National Natural Science Foundation of China [61903143, 61673001, 61773162]
- Shanghai Chenguang Program [19CG33]
- Shanghai Sailing Program [19YF1412100]
- Natural Science Foundation of Shanghai [18ZR1416700]
- 111 Project from China [B17017]
This article focuses on the model-based event-triggered sliding-mode control issue for multi-input systems, where a scheme is designed to mitigate data communication burden and eliminate disturbances and uncertainties. An auxiliary disturbance input and genetic algorithm are used to ensure stability and solve optimization problems. The effectiveness of the proposed algorithm is demonstrated using a jet transport aircraft.
This article is concerned with the model-based event-triggered sliding-mode control (SMC) issue for multi-input systems, which is motivated by some existing results in a single-input case. A model-based event-triggered SMC scheme is first designed. In particular, a triggered condition is co-designed with SMC to achieve the reachability condition of a specified sliding surface. Thus, it can effectively mitigate the burden of data communication, and also eliminate the effect of the matched external disturbance and the model uncertainties in both system and input. For ensuring the stability of the model dynamics and the resulting sliding-mode dynamics simultaneously, an auxiliary disturbance input is introduced to the nominal model by compensating the switching term of the designed SMC law. Furthermore, the positive lower bound for the minimum interevent time is analyzed to ensure the feasibility of the proposed approach. To illustrate the proposed model-based event-triggered SMC approach from a practical viewpoint, two design problems to maximize the system robustness and performance are proposed, respectively. The nontrivial optimization problems are then solved by a genetic algorithm (GA). Finally, jet transport aircraft is utilized to demonstrate the effectiveness of the proposed results and algorithm.
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