Jang J R
Dept. of Electr. Eng. and Comput. Sci., California Univ., Berkeley, CA.
IEEE Trans Neural Netw. 1992;3(5):714-23. doi: 10.1109/72.159060.
A generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner is presented. This methodology, termed temporal backpropagation, is model-sensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules of human experts or automatically derive the fuzzy if-then rules if human experts are not available. The inverted pendulum system is employed as a testbed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.
提出了一种通用控制策略，该策略通过增强具有自学习能力的模糊控制器，以近乎最优的方式实现规定的控制目标。这种方法称为时间反向传播，在某种意义上它对模型敏感，即它可以处理能够以分段可微格式表示的对象，如差分方程、神经网络、GMDH结构和模糊模型。无论所考虑对象的输入和输出数量如何，所提出的方法既可以完善人类专家的模糊if-then规则，也可以在没有人类专家的情况下自动推导模糊if-then规则。采用倒立摆系统作为测试平台，以证明所提出控制方案的有效性和所获得模糊控制器的鲁棒性。
