This paper deals with the hybrid particle swarm optimization‐Cuckoo Search (PSO‐CS) algorithm which is capable of solving complicated nonlinear optimization problems. It combines the iterative scheme of the particle swarm optimization (PSO) algorithm and the searching strategy of the Cuckoo Search (CS) algorithm. Details of the PSO‐CS algorithm are introduced; furthermore its effectiveness is validated by several mathematical test functions. It is shown that Lévy flight significantly influences the algorithm’s convergence process. In the second part of this paper, the proposed PSO‐CS algorithm is applied to two different engineering problems. The first application is nonlinear parameter identification for the motor drive servo system. As a result, a precise nonlinear Hammerstein model is obtained. The second one is reactive power optimization for power systems, where the total loss of the researched IEEE 14‐bus system is minimized using PSO‐CS approach. Simulation and experimental results demonstrate that the hybrid optimal algorithm is capable of handling nonlinear optimization problems with multiconstraints and local optimal with better performance than PSO and CS algorithms.

In this paper, an enhanced dueling double deep Q network algorithm with mixed penalty function (EN-D3QN-MPF) for microgrid energy management control is developed. First, a novel microgrid model including PV, wind turbine generator, electric storage system, electric vehicle charging station, thermostatically controlled loads, and residential price-responsive loads are proposed. Then, by combining the mixed penalty function method with D3QN reinforcement learning together, a mixed penalty function method is implemented to balance the reward weightings. Accordingly, an EN-D3QN-MPF algorithm is presented to achieve low-carbon economic and EV users' charging satisfaction operation of the microgrid. The effectiveness of the proposed method is verified by the dataset collected from eastern China in 2019. Simulation results validate that our proposed method has superior energy management performance over the genetic algorithm (GA), Particle Swarm Optimization (PSO), dueling deep Q network (dueling DQN), double DQN (DDQN), and D3QN algorithms.

Multiobjective optimization (MOO) dispatch for microgrids (MGs) can achieve many benefits, such as minimized operation cost, greenhouse gas emission reduction, and enhanced reliability of service. In this paper, a MG with the PV-battery-diesel system is introduced to establish its characteristic and economic models. Based on the models and three objectives, the constrained MOO problem is formulated. Then, an advanced multiobjective particle swarm optimization (MOPSO) algorithm is proposed to obtain Pareto optimization dispatch for MGs. The combination of archive maintenance and Pareto selection enables the MOPSO algorithm to maintain enough nondominated solutions and seek Pareto frontiers. The final trade-off solutions are decided based on the fuzzy set. The benchmark function tests and simulation results demonstrate that the proposed MOPSO algorithm has better searching ability than nondominated sorting genetic algorithm-II (NSGA-II), which is widely used in generation dispatch for MGs. The proposed method can efficiently offer more Pareto solutions and find a trade-off one to simultaneously achieve three benefits: minimized operation cost, reduced environmental cost, and maximized reliability of service.

In this study, the identification and control scheme of the motor‐drive servo turntable are researched to achieve accurate tracking. Firstly, the input–output identification experiments are designed and realised for the global model of motor‐drive servo turntable. The order of the global controlled auto‐regressive model is determined by the Akaike information criterion, and the parameters are estimated by a particle swarm optimisation‐cuckoo search algorithm. Secondly, the switched friction model is built combining the LuGre structure and the Stribeck curve. The switching conditions and the parameters of the switched friction model are determined though the input–output and the friction experiments. A constrained multi‐objective optimisation problem is constructed for the parameter identification of the switched friction model. It is solved via a fuzzy comprehensive evaluation. Finally, the composite control strategy, in which the proportional–integral–derivative controller and model‐based friction observer are combined, is proposed in the azimuth angle system of the motor‐drive servo turntable. The trajectory tracking results in simulation and experiment illustrate the effectiveness of the proposed switched model, identification method, and composite control strategy.