The problem of adaptive multi-objective optimization(AMOO) has received extensive attention due to its practical significance.An important issue in optimizing a multi-objective system is adjusting the weighting coefficients of multiple objectives so as to keep track of various conditions.In this paper,a feedback structure for AMOO is designed.Moreover,the reinforcement learning combined with hidden biasing information is applied to online tuning weighting coefficients of objective functions.Finally,the proposed approach is applied to the optimization design problem of an elevator group control system.Simulation results show that AMOO has the best average performance at up-peak traffic profile,and its average waiting time reaches 22 s.AMOO is suitable for various traffic patterns,and it is also superior to the majority of algorithms at down-peak traffic profile.
An iterative identification and control design method based on v-gap is given to ensure the stability of closed-loop system and control performance improvement. The whole iterative procedure includes three parts:the optimal excitation signals design,the uncertainty model set identification and the stable controller design. Firstly the worst case v-gap is used as the criterion of the optimal excitation signals design,and the design is performed via the power spectrum optimization. And then,an uncertainty model set is attained by system identification on the basis of the measure signals. The controller is designed to ensure the stability of closed-loop system and the closed-loop performance improvement. Simulation result shows that the proposed method has good convergence and closed-loop control performance.
