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A novel control algorithm combining the linear quadratic regulator (LQR) control and trajectory planning (TP) is proposed for the control of an underactuated crane system, targeting position adjustment and swing suppression. The TP is employed to control the swing angle within certain constraints, and the LQR is applied to achieve anti-disturbance. In order to improve the accuracy of the position control, a differential-integral control loop is applied. The weighted LQR matrices representing priorities of the state variables for the bridge crane motion are searched by the multi-objective genetic algorithm (MOGA). The stability proof is provided in order to validate the effectiveness of the proposed algorithm. Numerous simulation and experimental validations justify the feasibility of the proposed method.

This paper proposes a comparison study of energy management methods for a parallel plug-in hybrid electric vehicle (PHEV). Based on detailed analysis of the vehicle driveline, quadratic convex functions are presented to describe the nonlinear relationship between engine fuel-rate and battery charging power at different vehicle speed and driveline power demand. The engine-on power threshold is estimated by the simulated annealing (SA) algorithm, and the battery power command is achieved by convex optimization with target of improving fuel economy, compared with the dynamic programming (DP) based method and the charging depleting–charging sustaining (CD/CS) method. In addition, the proposed control methods are discussed at different initial battery state of charge (SOC) values to extend the application. Simulation results validate that the proposed strategy based on convex optimization can save the fuel consumption and reduce the computation burden obviously.

This paper proposes an optimal grouping method for battery packs of electric vehicles (EVs). Based on modeling the vehicle powertrain, analyzing the battery degradation performance and setting up the driving cycle of an EV, a genetic algorithm (GA) is applied to optimize the battery grouping topology with the objective of minimizing the total cost of ownership (TCO). The battery capacity and the serial and parallel amounts of the pack can thus be determined considering the influence of battery degradation. The results show that the optimized pack grouping can be solved by GA within around 9 min. Compared with the results of maximum discharge efficiency within a fixed lifetime, the proposed method can not only achieve a higher discharge efficiency, but also reduce the TCO by 2.29%. To enlarge the applications of the proposed method, the sensitivity to driving conditions is also analyzed to further prove the feasibility of the proposed method.