著者

鈴木 脩平 田地 宏一

出版者

公益社団法人 計測自動制御学会

雑誌

計測自動制御学会論文集 (ISSN:04534654)

巻号頁・発行日

vol.50, no.4, pp.348-355, 2014 (Released:2014-04-16)

参考文献数

15

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In model predictive control (MPC), an optimal control problem is solved at each time steps to determine control input. To realize on-line control of MPC, reducing computational time is requisite. In this paper, we apply a semismooth Newton method for MPC with simple bounds. The semismooth Newton method is one of iterative methods and is used to solve a complementarity problem and a KKT system of optimization problems. The semismooth Newton method has an advantage over other QP solvers, such as interior point methods and so on, that the initial point can be chosen arbitrarily, and this enables hot start. We show that the proposed method is globally convergent. We also show the condition guaranteeing the nonsingularity of the generalized Jacobian at a solution, which is closely related to the quadratic convergence of the algorithm. This is the first result to clarify the reason why constraints on state variables make MPC computationally expensive from the algorithmic perspective. Some numerical examples show that the proposed method is practically efficient.