A New Self-Learning Controller Based on CMAC Neural Network
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摘要: 现有的基于CMAC的自学习控制器能够有效地减小跟踪误差,但是在跟踪连续变 化信号如正弦波时,由于累积误差的影响会产生过学习现象,进而导致系统的不稳定.为此, 提出一种新的基于CMAC的自学习控制器,它以系统的动态误差作为CMAC的激励信号, 从而避免了累积误差的影响.仿真结果表明,该控制器不仅是有效的,而且具有很强的鲁棒 性.此外,它可以使用较高的学习速率,实时性强.Abstract: The conventional learning controller based on CMAC neural network can effectively reduce tracking error, but can also destabilize a control system which is otherwise stable due to the influence of accumulative errors when tracking continuous variable signals such as sinusoidal wave. Hence, a new self-learning controller based on CMAC neural network is proposed in this paper. It uses the dynamic errors of the system as input parameters to the CMAC neural network. This feature helps the controller to avoid the influence of the accumulative errors and then the stability of the system is ensured. The simulation results demonstrate that the proposed controller is not only effective but also of good robustness. Moreover, it has a high learning rate, which is important to online learning.
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Key words:
- CMAC neural network /
- self-learning control /
- stability
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