In this paper, a nonlinear predictive generalized minimum variance (NPGMV) controller is proposed and explicitly formulated for a class of nonlinear systems modeled by autoregressive second-order Volterra series, applying the polynomial approach. Hence, a new benchmark controller for performance assessment is introduced to improve the achievable control performance. Furthermore, to have an efficient control assessment, a data-driven algorithm based on the NPGMV control is presented that uses only the closed-loop operating data. In the design procedure, a multi-step cost function is defined to incorporate predictive action. Exploiting the predictive control concept enables the control scheme to handle constrained problems. Also, the proposed control algorithm utilizes an inherent integrating effect, which is essential for practical purposes. Volterra series are employed for modeling and identification of the nonlinear processes, using conventional least-squares methods. To show the effectiveness of the proposed methodology, simulation results and comparison studies are provided on a cascade Wiener model and a continuous stirred tank reactor (CSTR) chemical pilot plant. Finally, an experimental study on a pressure pilot plant is used to demonstrate the applicability of the proposed control scheme. The simulation and experimental results indicate satisfactory performance of the proposed controller.

在本文中，提出了一种非线性预测广义最小方差 (NPGMV) 控制器，并为一类通过自回归二阶 Volterra 级数建模的非线性系统应用了多项式方法。因此，引入了一种用于性能评估的新基准控制器，以提高可实现的控制性能。此外，为了进行有效的控制评估，提出了一种基于 NPGMV 控制的数据驱动算法，该算法仅使用闭环运行数据。在设计过程中，定义了多步成本函数以包含预测动作。利用预测控制概念使控制方案能够处理受约束的问题。此外，所提出的控制算法利用了固有的积分效应，这对于实际目的是必不可少的。Volterra 级数用于非线性过程的建模和识别，使用传统的最小二乘法。为了显示所提出方法的有效性，提供了级联 Wiener 模型和连续搅拌釜反应器 (CSTR) 化学中试装置的模拟结果和比较研究。最后，对压力试验装置的实验研究用于证明所提出的控制方案的适用性。仿真和实验结果表明所提出的控制器具有令人满意的性能。提供了级联 Wiener 模型和连续搅拌釜反应器 (CSTR) 化学中试装置的模拟结果和比较研究。最后，对压力试验装置的实验研究用于证明所提出的控制方案的适用性。仿真和实验结果表明所提出的控制器具有令人满意的性能。提供了级联 Wiener 模型和连续搅拌釜反应器 (CSTR) 化学中试装置的模拟结果和比较研究。最后，对压力试验装置的实验研究用于证明所提出的控制方案的适用性。仿真和实验结果表明所提出的控制器具有令人满意的性能。