Grain drying control is a challenging task owing to the complex heat and mass exchange process. To precisely control the outlet grain moisture content (MC) of a continuous mixed‐flow grain dryer, in this paper, we proposed a genetically optimized inverse model proportional–integral–derivative (PID) controller based on support vector machines for regression algorithm which is named the GO‐SVR‐IMCPID controller. The structure of the GO‐SVR‐IMCPID controller consists of a genetic optimization algorithm, an indirect inverse model predictive controller, and a PID controller. In addition, to verify the control performances of the proposed controller in the simulation study, we have established a nonlinear mathematical model for the mixed‐flow grain dryer to represent the nonlinear grain drying process. Finally, the control performance and the robustness of the GO‐SVR‐IMCPID controller were simulated and compared with the other controllers. By the simulation results, it is shown that this proposed algorithm can track the target value precisely and has fewer steady errors and strong ability of anti‐interference. Furthermore, it has further confirmed the superiority of the proposed grain drying controller by comparing it with the other controllers.

中文翻译：

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谷物干燥机智能控制器的设计：支持向量机，用于回归逆模型比例-积分-微分控制器

由于复杂的热质交换过程，谷物干燥控制是一项艰巨的任务。为了精确控制连续混合流式谷物干燥机的出口谷物水分含量，我们提出了一种基于支持向量机的遗传优化逆模型比例-积分-微分（PID）控制器，用于回归算法。名为GO‐SVR‐IMCPID控制器。GO‐SVR‐IMCPID控制器的结构包括遗传优化算法，间接逆模型预测控制器和PID控制器。此外，为了在仿真研究中验证所提出控制器的控制性能，我们建立了混合流式谷物干燥机的非线性数学模型，以表示非线性谷物干燥过程。最后，模拟了GO‐SVR‐IMCPID控制器的控制性能和鲁棒性，并与其他控制器进行了比较。仿真结果表明，该算法能够精确跟踪目标值，稳定误差小，抗干扰能力强。此外，通过与其他控制器进行比较，进一步证实了所提出的谷物干燥控制器的优越性。