Optimization of Model-free Adaptive Controller Using Differential Evolution Method
نویسنده
چکیده
It is well-known that conventional control theories are widely suited for applications where the processes can be reasonably described in advance. However, when the plant’s dynamics are hard to characterize precisely or are subject to environmental uncertainties, one may encounter difficulties in applying the conventional controller design methodologies. Despite the difficulty in achieving high control performance, the fine tuning of controller parameters is a tedious task that always requires experts with knowledge in both control theory and process information. Nowadays, more and more studies have focused on the development of adaptive control algorithms that can be directly applied to complex processes whose dynamics are poorly modeled and/or have severe nonlinearities. In this context, the design of a Model-Free Learning Adaptive Control (MFLAC) based on pseudo-gradient concepts and optimization procedure by Differential Evolution (DE) is presented in this paper. DE algorithms are evolutionary algorithms that have already shown appealing features as efficient methods for the optimization of continuous space functions. Motivation for application of DE approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trialand-error by user. Numerical results of the MFLAC with particle swarm optimization for a nonlinear control valve are showed.
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