线性规划实现动态优化的模型预测控制策略

Model predictive control strategies to realize dynamic optimization based on linear programming

为减小模型预测控制算法中动态优化部分的计算复杂度,提出了用线性规划而非二次规划解决模型预测控制动态优化方法。对单输入单输出和多输入多输出模型预测控制的情形,以控制增量、输出增量和偏移变量作为优化变量,建立线性等式约束和不等式约束,并引入线性目标函数,形成线性规划问题。通过加入多种软约束,可改善动态过程的性能指标,达到平稳控制的目的。最后通过一个实例验证了方法的有效性。

To reduce the computational complexity of the dynamic optimization section in model predictive control algorithm,a new method based on linear programming instead of quadratic programming was proposed.For both single-input single-output and multi-input multi-output model predictive control,the linear programming problem to describe the dynamic optimization is constructed by treating control increment,output increments and some deviation variables as optimization variables,inducing equality or inequality linear constraints and choosing a linear objective function.Moreover,soft constraints can be considered as a part of the linear programming problem to improve the performance indicators of dynamic process and to achieve the purpose of smooth control.Finally,a simulation example illustrates the effectiveness of the presented approach.