It is usually difficult to design a controller for a nonlinear multiple-input and multiple-output (MIMO) system. The methodological approach taken in this study is a mixed methodology based on a PID-type internal model control (IMC) method and neural network (NN) optimization algorithm. The NN controller is designed for adjusting the sole parameter in IMCPID and compensating the characteristic changes and non-linearity in stepless flow control. In this study, a simulation of a nonlinear MIMO system with strong coupling is carried out. The simulation results indicate that the proposed control method has a better performance in settle time, overshoot, robustness and set-point tracking accuracy compared with other considered methods.

中文翻译：

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往复式机械无级流量控制的IMCPID优化神经网络设计

通常很难为非线性多输入多输出 (MIMO) 系统设计控制器。本研究中采用的方法论是一种基于 PID 型内部模型控制 (IMC) 方法和神经网络 (NN) 优化算法的混合方法论。NN 控制器设计用于调整 IMCPID 中的唯一参数并补偿无级流量控制中的特性变化和非线性。在本研究中，对具有强耦合的非线性 MIMO 系统进行了仿真。仿真结果表明，与其他考虑的方法相比，所提出的控制方法在稳定时间、超调量、鲁棒性和设定点跟踪精度方面具有更好的性能。