This article presents the design of a hybrid fuzzy sliding mode loss-minimisation control for the speed of a permanent magnet synchronous generator (PMSG) and a high-performance on-line training recurrent neural network (RNN) for the turbine pitch angle control. The back-propagation learning algorithm is used to regulate the RNN controller. The PMSG speed uses maximum power point tracking below the rated speed, which corresponds to low- and high-wind speeds, and the maximum energy can be captured from the wind. The sliding mode controller with an integral-operation switching surface is designed, in which a fuzzy inference mechanism is utilised to estimate the upper bound of uncertainties.

In this article, the characteristics of the wave energy converter are considered and a novel dynamic controller (NDC) for a permanent magnet synchronous generator (PMSG) is proposed for Wells turbine applications. The proposed NDC includes a recursive cerebellum model articulation controller (RCMAC) with a grey predictor and innovative particle swarm optimization (IPSO). IPSO is developed to adjust the learning speed and improve learning capability. Based on the supervised learning method, online adjustment law of RCMAC parameters is derived to ensure the system’s stability. The NDC scheme is designed to maintain a supply–demand balance between intermittent power generation and grid power supply. The proposed NDC exhibits an improved power regulation and dynamic performance of the wave energy system under various operation conditions. Furthermore, better results are obtained when the RCMAC is used with the grey predictive model method.

In recent years, based on the rising of global personal safety demand and human resource cost considerations, development of unmanned vehicles to replace manpower requirement to perform high-risk operations is increasing. In order to acquire useful resources under the marine environment, a large boat as an unmanned surface vehicle (USV) was implemented. The USV is equipped with automatic navigation features and a complete substitute artificial manipulation. This USV system for exploring the marine environment has more carrying capacity and that measurement system can also be self-designed through a modular approach in accordance with the needs for various types of environmental conditions. The investigation work becomes more flexible. A catamaran hull is adopted as automatic navigation test with CompactRIO embedded system. Through GPS and direction sensor we not only can know the current location of the boat, but also can calculate the distance with a predetermined position and the angle difference immediately. In this paper, the design of automatic navigation is calculated in accordance with improved Elman neural network (ENN) algorithms. Takagi-Sugeno-Kang (TSK) fuzzy and improved ENN control are applied to adjust required power and steering, which allows the hull to move straight forward to a predetermined target position. The route will be free from outside influence and realize automatic navigation purpose.

This study proposes a novel sensorless maximum power capture (MPC) control strategy for variable-speed wind energy conversion systems employing a permanent magnet synchronous generator (PMSG). The proposed method integrates a fuzzy probabilistic neural network (FPNN) with an improved particle swarm optimization (IPSO) algorithm to enable adaptive learning capabilities. Additionally, support vector regression (SVR) is employed to estimate wind speed without the use of mechanical sensors, thereby enhancing system reliability and reducing maintenance requirements. A vanadium redox battery (VRB) is integrated to enhance power stability under fluctuating wind conditions. Simulation results demonstrate that the proposed FPNN-IPSO-based controller achieves superior performance compared to conventional Takagi–Sugeno–Kang (TSK) fuzzy and proportional–integral (PI) controllers. Specifically, the FPNN-IPSO controller exhibits notable improvements in average power output, tracking accuracy, and overall system efficiency. The proposed method increases power output by 9.71% over the PI controller and supports Plug-and-Play operation, making it suitable for intelligent microgrid integration. This work demonstrates an effective approach for intelligent, sensorless MPC control in hybrid wind–battery microgrids.

Purpose: This paper aims to present the analysis, design and implementation of functional link-based recurrent fuzzy neural network (FLRFNN) for the control of variable-speed switched reluctance generator (SRG). Design/methodology/approach: The node connecting weights of the FLRFNN are trained online by back-propagation (BP) algorithms. The proposed estimator requires less processing time than traditional methods and can be fully implemented using a low-cost digital signal processor (DSP) with MATLAB toolboxes. The DSP-based hybrid sensor presented in this paper can be applied to a wind energy-conversion system where the SRG is used as a variable-speed generator. The current transducer is used to monitor the energized current and proximity sensors for rotor salient. Findings: The authors have found that optimal based on FLRFNN with Grey controller can resolve the regulation of the system with uncertainty model and unknown disturbances. This technique can maintain the system stability and reach the desired performance even with parameter uncertainties. Originality/value: This design will improve the performance of SRG to operate more smoothly. This application is currently being studied because the SRG has well-known advantages such as robustness, low manufacturing cost and good size-to-power ratio. Performance of the proposed controller can offer better stability characteristics. Finally, the SRG has a very good efficiency in the whole operating range.