Adaptive Control of Ball and Beam System Using Knowledge-Based Particle Swarm Optimization
| Autor: | Yunyi Jiang, Jingyu Li, Yuxuan Lv, Runsen Wang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Fitness function Adaptive control Computer science media_common.quotation_subject Computer Science::Neural and Evolutionary Computation MathematicsofComputing_NUMERICALANALYSIS Particle swarm optimization PID controller 02 engineering and technology Inertia Ball and beam Nonlinear system 020901 industrial engineering & automation Control theory Approximation error 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing media_common |
| Zdroj: | ICARA |
| Popis: | A knowledge-based Particle Swarm Optimization (PSO) algorithm is used to achieve more optimized control of Ball and Beam System (BBS) adaptively. It adopts an improved nonlinear inertia weight, an adaptive strategy and a fitness function combining prior knowledge and one traditional performance criterion. Comparing four classic performance criteria, the simulation results indicate that Integral of Time multiply Absolute Error (ITAE) is better, and it is combined with prior knowledge. Based on the response curve of advanced correction, Ziegler-Nichols, basic PSO algorithm and knowledge-based PSO algorithm through experimental simulation, knowledge-based PSO algorithm is more effective to BBS. |
| Databáze: | OpenAIRE |
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