模糊逻辑控制与粒子群整定

Jeydson Lopes da Silva
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引用次数: 0

摘要

人工智能在动态系统控制领域的几个应用已被证明是过程改进的有效工具。在这种背景下,基于模糊逻辑的控制系统-模糊逻辑控制(FLC)是控制系统领域一系列进展的一部分。模糊控制是基于自然语言的,因此有能力使近似更接近问题的真实性质。使用元启发式算法,如粒子群优化(PSO),使其能够以优化的方式对模糊控制器进行适当的调整。这种技术允许调整FLC在一个简单的方式,根据设计者所期望的性能,而不需要进行长时间的常规测试。
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Fuzzy Logic Control with PSO Tuning
Several applications of artificial intelligence in the area of control of dynamic systems have proven to be an efficient tool for process improvement. In this context, control systems based on fuzzy logic - Fuzzy Logic Control (FLC) are part of a series of advances in the areas of control systems. Fuzzy control is based on natural language and therefore has the ability to make approximations closer to the real nature of the problems. The use of metaheuristic algorithms such as the particle swarm optimization (PSO) allows it to provide adequate adjustments to the fuzzy controller in an optimized manner. This technique allows to adjust the FLC in a simple way according to the performance desired by the designer, without the need for a long time of conventional tests.
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