This study presents a self‐evolving probabilistic fuzzy (PF) neural network with asymmetric membership function (SPFNN‐AMF) controller for the position servo control of a permanent magnet linear synchronous motor (PMLSM) servo drive system. In the beginning, the dynamic model for the PMLSM is analysed on the basis of field‐oriented control. Subsequently, an SPFNN‐AMF control system, which integrates the advantages of self‐evolving NN, PF logic system, and AMF, is proposed to handle vagueness, randomness, and time‐varying uncertainties of the PMLSM servo drive system during the control process. For the SPFNN‐AMF, the proposed learning algorithm consists of the structure learning and parameter learning in which the former is used to grow and prune the fuzzy rules automatically, whereas the latter is utilised to train the network parameters dynamically. Finally, detailed experimental results of two position commands tracking at different operation conditions demonstrate the validity and robustness of the proposed SPFNN‐AMF for controlling the PMLSM servo drive system.

This paper proposes an evolutionary algorithm-based energy-saving control strategy to control a highly nonlinear and time-varying active magnetic bearing (AMB) positioning system that considers energy efficiency and control performance simultaneously. Most AMB control schemes use a bias current with a superimposed control current to improve the linearity and dynamic performance of the system. However, the bias current causes power losses even if no electromagnetic force is required. As such, a recurrent wavelet fuzzy neural network with adaptive differential evolution (RWFNN-ADE)-based dynamic bias current control strategy is proposed in this paper so as to minimize the energy consumed by an AMB without altering its positioning performance and robustness. To begin with, this paper analyzes the operation principle of the AMB positioning system with a differential driving mode. Subsequently, the proposed RWFNN-ADE control scheme, in which the control current and bias current are controlled by the RWFNN and ADE, respectively, is introduced in detail. Finally, the experimental results demonstrate the high-accuracy control and significant energy-saving performances of the proposed RWFNN-ADE-controlled AMB positioning system. In the tests corresponding to operation periods of 10 and 50 s, the energy improvements compared to the baseline values were 20.24% and 17.65%, respectively, in nominal cases, and 18.89% and 18.68%, respectively, in parameter variation cases, for the proposed control strategy.

This article presents a home energy management system (HEMS) based on the proposed adaptive artificial bee colony (AABC) optimization method, of managing a residential microgrid connected to the main grid. The studied microgrid consists of a photovoltaic system (PVS), a battery storage system (BSS), and electricity loads. The proposed AABC-based HEMS optimizes power flow ratio among PVS, BSS, and the main grid with respect to the load demand and electricity rate in order to minimize electricity consumption cost. Three dc-dc converters are actively controlled with an active dual-loop control architecture to dominate the power distribution among multi-power sources. The performances of the proposed optimal power flows control (PFC) under different electricity load levels and PVS generation conditions are examined. In the PFC experiments, the designed HEMS is implemented on a digital signal processor. The experimental results demonstrate the effectiveness and superiority of the proposed AABC-based HEMS compared with the rule-based control strategy and the equivalent consumption minimization strategy in terms of electricity consumption cost.

This study presents a current sharing method to actively balance the output currents of a parallel DC– DC converters (PDCC) system regarding the demanded power. First, the operating principle of the PDCC system with parallel-connected bidirectional converters is studied. To regulate the output voltage in DC bus and share the output currents of the individual converters, a dual loop control architecture comprising an outer voltage control loop and multiple inner current control loops is designed based on the automatic master-slave control scheme. Moreover, a feedback type two degree-of-freedom proportional-integral-derivative (FB2PID) controller is introduced to obtain the pulse-width modulation control signals for the converters. In order to improve the dynamic response and robustness of the active current-sharing control performances of the FB2PID controlled PDCC system, a bat algorithm (BA) optimized FB2PID control system is further proposed to concurrently and dynamically optimize the control parameters of the FB2PID controller in current control loop. Thus, the output current of each converter can be controlled to share the demand power equally in the presence of uncertainties. Finally, simulation and experimental results reveal that the proposed BA-optimized FB2PID control system outperforms conventional PID and FB2PID control systems with regard to the voltage regulation and current sharing performances under time-varying electric load condition.