Abstract
Particle swarm optimization (PSO) is a stochastic population-based algorithm based on the collaborative swarm behaviors of insects, birds, fish, and animals. The algorithms use swarm emulation to seek for solutions to optimization problems and are often used for the optimization of continuous nonlinear systems. This approach has recently succeeded in addressing many significant real-world optimization problems. In particular, this work integrates studies of PSOs and proportional–integral–derivative (PID) controllers, which are widely used for industrial control, in order to optimize PID controllers using PSO in a wide variety of multidisciplinary fields. This chapter introduces the PSO approach and algorithm and the theory of PID control, the use of PSO in optimizing the parameters of various controllers, and illustrates the findings by exploring several real-time control applications.
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Levy, D., Lu, Y., Yan, D., Zhou, M., Chen, S. (2019). Particle Swarm Optimization of Real-Time PID Controllers. In: Tian, YC., Levy, D. (eds) Handbook of Real-Time Computing. Springer, Singapore. https://doi.org/10.1007/978-981-4585-87-3_49-1
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DOI: https://doi.org/10.1007/978-981-4585-87-3_49-1
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