This paper proposes a super-twisting sliding-mode control (STSMC) strategy to enhance the efficiency and stability of a heaving point absorber wave energy converter (PAWEC) system equipped with a permanent magnet synchronous generator (PMSG). In particular, the STSMC is designed to address both generator-side and grid-side control challenges by ensuring precise regulation under varying wave conditions. A dynamical model of the PAWEC is developed to describe system responses, while the power take-off (PTO) mechanism is tailored to maintain consistent generator speed and efficient energy conversion. Lyapunov stability theory is employed to verify the stability of the proposed controller. Simulation studies and tests on a small-scale experimental setup with a 500 W PAWEC model under regular and irregular waves demonstrate that STSMC improves generator speed regulation and power output by more than 30% compared to field-oriented control (FOC), nonlinear adaptive backstepping (NAB), and first-order sliding-mode control (FOSMC). The proposed approach also manages grid-side total harmonic distortion (THD) effectively, keeping it below 5%. These results indicate that STSMC can substantially improve the dynamic performance and energy efficiency of wave energy systems.

Coal blending in thermal power plants is a complex multi-objective challenge involving economic, operational and environmental considerations. This study presents a Q-learning-enhanced NSGA-II (QLNSGA-II) algorithm that integrates the adaptive policy optimization of Q-learning with the elitist selection of NSGA-II to dynamically adjust crossover and mutation rates based on real-time performance metrics. A physics-based objective function takes into account the thermodynamics of ash fusion and the kinetics of pollutant emission, ensuring compliance with combustion efficiency and NO_x limits. Benchmark tests on the Walking Fish Group (WFG) and Unconstrained Function (UF) suites show that QLNSGA-II achieves a 12.7% improvement in Inverted Generational Distance (IGD) and a 9.3% improvement in Hypervolume (HV) compared to prevailing algorithms. Industrial validation at the Huaneng Yingkou power plant confirms a 14.7% reduction in fuel cost and a 41% reduction in slagging incidence over conventional blending methods, backed by 12 months of operational data. Other benefits include a 24.8% reduction in sulphur content, a 6.9% increase in the plant’s net heat rate and annual savings of RMB 12.3 million, 2,150 tonnes of limestone and 38,500 tonnes of CO₂-equivalent emissions. These results highlight QLNSGA-II as a scalable, robust solution for multi-objective coal blending, offering a promising way to improve the efficiency and sustainability of coal-fired power generation.

Reliable wave-to-wire regulation of a heaving point-absorber wave energy converter (PAWEC) requires coordinated control of the coupled hydrodynamic, mechanical, electromagnetic, and grid-connected stages, while wave excitation and radiation memory introduce persistent disturbances into the conversion chain. This study develops a fractional-memory super-twisting sliding mode control (FOSTSMC) framework for grid-connected PAWEC regulation. A control-oriented wave-to-wire model combines Cummins-based heave dynamics, a reduced-order PTO–generator interface, a permanent magnet synchronous generator (PMSG), a DC link, and a grid-side converter (GSC). The physical plant remains integer order, while fractional dynamics are introduced only through a memory state in the feedback manifold. The super-twisting mechanism establishes finite-time reaching under bounded perturbation-rate conditions, followed by asymptotic tracking-error convergence governed by Mittag–Leffler-stable fractional-memory dynamics. FOSTSMC is compared with nonlinear adaptive backstepping control (NAB), conventional sliding mode control (SMC), and super-twisting sliding mode control (STSMC) under regular-wave excitation and a 100 s fixed-seed JONSWAP irregular-wave realization. The principal benefit of FOSTSMC is improved generator-speed disturbance regulation. Over the 20–100 s irregular-wave analysis interval, FOSTSMC reduces generator-speed root-mean-square error (RMSE) and mean absolute error (MAE) by approximately 29.5% and 59.7%, respectively, relative to STSMC. Electromagnetic-torque tracking and electrical-power responses remain broadly comparable among the four controllers, while grid-current harmonic performance depends on the operating condition. The results indicate that fractional-memory feedback can improve generator-speed regulation without requiring a fractional-order model of the physical PAWEC plant.

Robotic manipulators operating in cluttered environments require collision-free trajectories that remain executable under kinematic and dynamic constraints. This paper proposes a reinforcement learning (RL)-guided multi-objective trajectory planning framework, termed RL-MOP-HNE, for a 6-DOF UR5 manipulator. The planning model simultaneously minimizes path length, energy consumption, and execution time while satisfying collision-avoidance, kinematic, and dynamic constraints. A tabular SARSA agent is embedded into the evolutionary search to adaptively select search behaviours according to the current optimization state. To improve the balance between exploration and exploitation, a Gaussian-perturbation adaptive hybrid crossover operator is integrated with a hierarchical neighborhood evolution (HNE) strategy, enabling progressive population refinement throughout the search process. The proposed method is evaluated in three representative environments with increasing planning complexity, including single-obstacle, narrow three-obstacle, and irregular five-obstacle scenarios, and is compared with MOEA/D, MOPSO, MSCLPSO, NSGA-II, and RL-NSGA-II. Experimental results show that RL-MOP-HNE generates feasible trajectories in all test cases and achieves the lowest dynamic-stability-prioritized composite scores among the compared algorithms. The planned trajectories exhibit smoother joint motion and lower velocity fluctuations, although these improvements are generally accompanied by longer execution times. Complementary analyses, including time scaling, manipulability, clearance evaluation, statistical significance tests, and ablation studies, further explain the performance characteristics of the proposed framework and quantify the contribution of its key components. The proposed framework is therefore well suited to robotic applications where motion stability and dynamic executability are of greater importance than minimum-time operation.