Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, PR China; School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, PR China.
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, PR China.
ISA Trans. 2014 Jul;53(4):983-93. doi: 10.1016/j.isatra.2014.05.003. Epub 2014 Jun 16.
An adaptive control strategy combining neural network inverse controller (NNIC) with RBFN disturbance observer (RBFNDOB) is developed for a multi-input-multi-output (MIMO) system with non-minimum phase, internal and external disturbances in this paper. Since the inverse model of system is unstable due to the non-minimum phase, a pseudo-plant is constructed, then the RBFN is used to identify the inverse model of pseudo-plant, which can track the parameter variations of system. By copying the structure and parameters of the identifier, the NNIC is obtained. Cascading the NNIC with the original plant, the MIMO system can be decoupled and linearized into independent SISO systems. For the independent decoupled system, the RBFNDOB employs a RBFN to observe the external disturbances and this estimate value is used as a feed-forward compensation term in controller. The case study on ball mill grinding circuit is presented. The effectiveness of the proposed method is demonstrated by simulation results and comparisons.
本文针对多输入多输出（MIMO）系统中存在非最小相位、内部和外部干扰的情况，提出了一种将神经网络逆控制器（NNIC）与 RBFN 干扰观测器（RBFNDOB）相结合的自适应控制策略。由于非最小相位的原因，系统的逆模型不稳定，因此构建了一个伪 plant，然后使用 RBFN 来识别伪 plant 的逆模型，从而可以跟踪系统参数的变化。通过复制识别器的结构和参数，可以得到 NNIC。将 NNIC 与原始 plant 级联，MIMO 系统可以被解耦并线性化为独立的 SISO 系统。对于独立解耦系统，RBFNDOB 使用 RBFN 来观测外部干扰，并且该估计值被用作控制器中的前馈补偿项。通过仿真结果和比较，验证了所提出方法的有效性。
