Description: 采用将BP神经网络的学习算法应用于PID控制中，使BP神经网络与PID控制算法结合起来，通过吸收两者的优势，使系统具有自适应性。这样系统可自动调节控制参数，更好地适应输入变量的变化，提高控制性能和可靠性。本文从BP神经网络的基本构成原理、学习规则和学习算法出发，设计了基于BP神经网络的PID控制器，并对其进行了仿真分析,结果表明，该控制方案可行、有效。-We apply the learning algorithm of BP neural network to the PID control, making the BP neural network and the PID control algorithm combined, and we enable the system to auto-adapted through absorbs superiority of both, and then the system can make control parameters adjusted of itself to adapt the change of input variable, enhance the control performance and the reliability. This article embarked from the BP neural network s basic constitution principle, the study rule and the learning algorithm，designed PID controller that based on the BP neural network, and carried on the simulation analysis to it, the result of simulation indicated that the control plan is feasible and effective. Platform: |
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Author:大同小异 |
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Description: ：智能算法如粒子群算法已被应用于PID控制器的参数优化，以弥补传统优化方法容易产生振荡和较大超调量
的不足，但是粒子群算法存在易于早熟的缺点，在分析量子粒子群算法的基础上，提出了使用量子粒子群算法优化PID控
制器的参数。为了兼顾控制系统的各项性能指标，根据控制器的实际要求对各项指标进行加权作为算法的目标函数，对
PID控制器进行多目标寻优。通过2个传递函数实例，分别使用z—N、粒子群算法和量子粒子群算法进行了PID控制器
参数优化设计，并对结果进行了分析。-Abstract：Heuristics such as particle swarnl optimization is employed to enhance the capability of traditional techniques，
which is easy to produce surge and big overshoot，but PS0 may be trapped in the local optima of the objective and lead to poor performance．
This paper propesed the quantum-behaved particle 8wsl in optimization for the parameter optimization of PID controller．
A fitness function containing performance indexes Was defined and the algorithm Was used in multi-object optimization of PID controllers．
Two examples were given to illustrate the design procedure and exhibit the effectiveness of the proposed method via tomo
parison study with the existing Z—N and PSO approaches． Platform: |
Size: 380928 |
Author:dhskja |
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