针对工业过程中被控对象往往具有复杂、强非线性、多变量的特性，提出一种基于多变量径向基函数（radial base function，RBF）神经网络的非线性内部迭代预测控制方法.采用多个RBF神经网络在线逼近多输入多输出的非线性系统，得到一个近似模型作为预测模型.同时为了减少求解系统控制律的计算量，将每个输出预测值沿着输入轨迹展开，从而把求解复杂非线性优化问题转化为求解简单的二次规划问题，解决了在线实时递推控制律时求解非线性微分方程的困难.最后通过t次内部迭代直至满足迭代条件，得到了最优的控制律.pH中和过程的仿真结果表明了该算法是有效而可行的.
For the complexity, strong nonlinearity and multi-variability in industrial processes, a nonlinear iterative predictive control algorithm based on radial base function (RBF) neural network is proposed. This algorithm employs multi-RBF neural network to approximate the nonlinear system, which can obtain an approximate model for the predictive model. Meanwhile in order to avoid increasing some computational burden, each predictive output along the future trajectory is expanded. Therefore the problem of nonlinear optimization is transformed into that of solving the easy quadratic programming,accordingly, thus the difficulty of solving the nonlinear differential equation in real-time online can be overcome, when deriving the predictive control law. Finally, the optimized control law can be obtained until the internal conditions are satisfied through internal iteration of t times. The simulation results for a pH process show that the proposed method is effective and feasible.
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