In model predictive control applications, the static nonlinear function of the Wiener model is an obstacle to forming a quadratic cost function. That case results in suboptimal control actions. As is known, a Wiener model predictive controller makes predictions employing the linear block of the Wiener model, and so its control solution is optimal due to the quadratic cost function. If the actual process output is used as a parameter in the empirical model, the inverse of the nonlinear function cannot be obtained as the roots of the nonlinear function in an offline manner, and the Wiener model predictive controller cannot be developed. Having regard to those situations, this paper introduces an offset-free Wiener model-based predictive controller with optimized inverse nonlinear function for the perturbed neutralization processes. Here, to facilitate accounts in the prediction horizon, an autoregressive with an external input model is preferred as the linear element of the Wiener model, and the least-squares-based optimization method is proposed to get the inverse of the nonlinear function. In this way, the model correction term can be employed to achieve an offset-free controller just as employed in the linear model predictive controller. Finally, the control performance of the developed controller is confirmed via comparative MATLAB/Simulink studies.

在模型预测控制应用中，维纳模型的静态非线性函数是形成二次成本函数的障碍。这种情况会导致控制动作欠佳。众所周知，维纳模型预测控制器使用维纳模型的线性块进行预测，因此由于二次成本函数，其控制解决方案是最佳的。如果将实际过程输出用作经验模型中的参数，则无法以离线方式获得非线性函数的逆作为非线性函数的根，因此无法开发Wiener模型预测控制器。考虑到这些情况，本文介绍了一种基于无偏移Wiener模型的预测控制器，该控制器具有优化的逆非线性函数，用于扰动的中和过程。这里，为了便于在预测范围内进行核算，最好使用具有外部输入模型的自回归作为Wiener模型的线性元素，并提出了基于最小二乘的优化方法来获得非线性函数的逆函数。以此方式，可以像线性模型预测控制器中所采用的那样，采用模型校正项来实现无偏移控制器。最后，通过比较性的MATLAB / Simulink研究确认了所开发控制器的控制性能。正如线性模型预测控制器中所采用的那样，模型校正项可用于实现无偏移控制器。最后，通过比较性的MATLAB / Simulink研究确认了所开发控制器的控制性能。正如线性模型预测控制器中所采用的那样，模型校正项可用于实现无偏移控制器。最后，通过比较性的MATLAB / Simulink研究确认了所开发控制器的控制性能。