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基于神经网络误差修正的广义预测控制 总被引:24,自引:0,他引:24
本文基于BP结构神经网络,对系统的建模误差进行预测,并将其与模型预测相结合构成广义预测控制算法,目的在于抑制模型失配的影响,增强广义预测控制的鲁棒性;仿真结果表明了这一算法的有效性。 相似文献
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Nonlinear one-step-ahead control using neural networks: Control strategy and stability design 总被引:13,自引:0,他引:13
A nonlinear one-step-ahead control strategy based on a neural network model is proposed for nonlinear SISO processes. The neural network used for controller design is a feedforward network with external recurrent terms. The training of the neural network model is implemented by using a recursive least-squares (RLS)-based algorithm. Considering the case of the nonlinear processes with time delay, the extension of the mentioned neural control scheme to d-step-ahead predictive neural control is proposed to compensate the influence of the time-delay. Then the stability analysis of the neural-network-based one-step-ahead control system is presented based on Lyapunov theory. From the stability investigation, the stability condition for the neural control system is obtained. The method is illustrated with some simulated examples, including the control of a continuous stirred tank reactor (CSTR). 相似文献
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基于径向基函数网络的一步超前预测控制研究 总被引:7,自引:0,他引:7
提出一种基于径向基函数(RBF)神经网络的一步超前预测控制算法。该方法只用于一个网络,控制量的获取只求几步迭代,算法简单并有较好的实用性。通过对离散非线性系统的仿真证明了算法的有效性。 相似文献
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基于Tank—Hopfield神经网络的有约束多变量广义预测控制器 总被引:3,自引:0,他引:3
通过对系统的信号约束,构成有约束多变量广义预测控制问题,并运用T-H优化神经网络来求解这一复杂的优化问题。在求解过程中,有约束广义预测控制的求解被转化为一个T-H优化电路网络的稳态解。因此可以通过硬件电路或龙格-库塔数值方法进行求取。在一个工业过程模型上的仿真研究证明这一方法是非常有效的。 相似文献
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