基于全局正交配置的非线性预测控制算法

针对非线性预测控制(NMPC)在线优化计算量大这一关键问题，提出一种基于全局正交配置的非线性预测控制算法。该算法以高阶插值正交多项式为基函数同时配置优化时域内的状态变量和控制变量，将连续动态优化问题转化为非线性规划问题(NLP)求解。全局正交配置可以使用较少的配置点而获得较高的逼近精度，这样即使NMPC使用很长的优化时域，离散化后得到的NLP问题的规模也比较小，能够有效地降低在线优化计算量。最后，以连续聚合反应过程为例验证了算法的有效性。

Nonlinear model predictive control algorithm using global orthogonal collocation method

One of the critical open issues in the nonlinear model predictive control(NMPC)scheme is the computational burden associated with the solution of the optimization problem,since at every sampling time a nonlinear dynamic optimization problem must be solved in real-time.To alleviate the aforementioned problem,a new NMPC algorithm using the global orthogonal collocation method is proposed.Higher order interpolation polynomial is used to simultaneously discrete state variables and control variables over the optimization horizon and the original continuous dynamic optimization is transcribed to a nonlinear programming problem(NLP).The NLP problem has a fixed structure with certain computational advantages and can be solved by an appropriate numerical optimization algorithm.Taking full advantage of the features of the global orthogonal collocation,the proposed algorithm provides the potential to reduce the scale of NLP and thereby reduce computational burden efficiently,even it works with a long optimization horizon.The effectiveness of the proposed algorithm is demonstrated by its application to a continuous polymerization process.It is found the algorithm achieves a smooth transition for large-magnitude setpoint changes and behaves well in the presence of disturbances.