Zhi-Chang Qin

This paper proposes two kinds of dual-loop nonlinear robust control strategies that are implemented on an open-loop unstable two-degree-of-freedom (2-DOF) helicopter system with unmodeled dynamics and uncertainties. The inner feedback loop is considered as a nominal controller realized by an existing “intelligent” proportional differential controller (iPD) while the outer layer feedback control is regarded as a compensation loop. We study two different forms of outer loop in this paper. One is model-free sliding mode compensator (MFSMC) and another is model-free data-driven compensator (MFDDC). The combination of the shared inner loop and either of the outer loops forms two different kinds of model-free robust control strategies, i.e., iPD-MFSMC and iPD-MFDDC. Both robust control approaches are validated experimentally on the attitude tracking control of a 2-DOF laboratory helicopter, whose control objective is to have the helicopter attitudes, i.e., pitch and yaw motions, track specified trajectories. To demonstrate the utility of the two control approaches, we compare them with linear quadratic regulator (LQR), optimal feedback linearization control (OFLC) and iPD, respectively. The extensive comparison of the simulation and experimental results shows that the dual-loop robust control approaches are quite promising in controlling the systems with unknown dynamical models.

As the core component of in-wheel motor-driven electric vehicles, the in-wheel motor (IWM) directly affects the driving/braking performance of each driving wheel and the driving performance of the vehicle. The IWM operation involves a coupling of multi-fields, including the electromagnetic, temperature, flow, and mechanical fields, which influence each other. It is necessary to study coupling analysis methods to obtain accurate and consistent results. In this paper, a 15 kW in-wheel motor is taken as the research object. Based on the finite element model of the IWM, the coupling factors between the electromagnetic and temperature field, and the influence trend of coupling factors on the two fields are investigated. On this basis, considering the strong coupling factors obtained from the above analysis, the unidirectional coupling and bidirectional coupling analysis methods are used to analyze the electromagnetic–temperature characteristics of the IWM, and the comparative results between the two methods are discussed. It was found that the results showed the temperature of the IWM calculated by the bidirectional coupling method was higher than that obtained by the unidirectional coupling analysis method. The maximum temperature of stator windings calculated by bidirectional coupling was 7.1% higher than that calculated by unidirectional coupling analysis, and the effect on the relative difference of torque could reach 7.4%. Bidirectional coupling can more accurately reflect the variation of variables in the fields and the prediction of motor performance in the process of motor operation. The progress made in the electromagnetic–temperature coupled analysis method can provide a theoretical basis and useful ideas for the multi-fields coupling analysis of IWMs.

The multi-objective optimal control design usually generates hundreds or thousands of Pareto optimal solutions. How to assist a user to select an appropriate controller to implement is a postprocessing issue. In this paper, we develop a method of cluster analysis of the Pareto optimal designs to discover the similarity of the optimal controllers. After we identify the clusters of optimal controllers, we develop a switching strategy to select controls from different clusters to improve the performance. Numerical and experimental results show that the switching control algorithm is quite promising.