Hooi Hung Tang’s research while affiliated with Universiti Sains Islam Malaysia and other places

Fuzzy logic controller (FLC) is renowned for its adaptability and intuitive decision-making capabilities in active suspension systems, which face challenges stemming from unpredictable disturbances and complex vehicle dynamics. In this study, we introduce a novel optimization approach termed WW-PSO, which merges particle swarm optimization (PSO) with water wave optimization (WWO), aiming to elevate the performance of an FLC-based active suspension system. WWO efficiently solves optimization problems by simulating natural water wave behaviors. The hybridization of PSO and WWO leverages their complementary exploration and exploitation capabilities, resulting in improved performance and robustness of the optimized controller. The performance of the proposed controller, which is augmented with a linear quadratic controller (LQR), is evaluated across three scenarios featuring different road profiles and compared against other recent optimization methods which include genetic algorithm, tent sparrow search algorithm (Tent-SSA), and ST-PS-SO which is a combination of PSO, sewing trainee-based optimization, and symbiotic organism search. Simulation results show that the proposed WW-PSO significantly improves integral time absolute error (ITAE) for both body and wheel displacements, overshoot/undershoot (OS/US), and settling time. Specifically, the proposed method achieves a 53.37% improvement in ITAE, a 56.44% reduction in OS/US, and a 13.09% decrease in settling time for body displacements. For wheel displacements, it achieves a 52.90% improvement in ITAE, a 48.72% reduction in OS/US, and a 14.15% decrease in settling time. These enhancements demonstrate the hybrid method’s effectiveness in improving vehicle stability and passenger comfort across a range of road conditions.

Improving trajectory tracking in Differential Wheeled Mobile Robots (DWMRs) is vital for enhancing their effectiveness in various applications, such as autonomous cleaning, mowing, and leader-following scenarios. These scenarios often involve navigating complex, nonlinear paths, requiring advanced control strategies for enhanced performance. This work presents the novel integration of a Backstepping Controller (BSC) and a Fractional-Order Proportional-Integral-Derivative (FOPID) controller within a cascade closed-loop structure for Differential Wheeled Mobile Robots (DWMRs). The proposed BSC-FOPID controller addresses velocity saturations and nonlinearities, ensuring system stability and precise trajectory tracking. A key contribution is the enhanced Grey Wolf Optimization strategy, termed GWO-SMA, which integrates Grey Wolf Optimization (GWO) with Slime Mould Algorithm (SMA). By leveraging opposition space and optimum cache concepts, GWO-SMA improves fitness optimization in each iteration, enhancing both exploration and exploitation efficiency. This hybrid approach optimizes controller parameters using a multi-metric cost function that incorporates Integral Absolute Error (IAE) and Integral Squared Error (ISE) to minimize long-term steady-state error and enhance responsiveness to larger deviations. Simulations demonstrate the superior performance of the proposed GWO-SMA algorithm compared to existing optimization techniques, such as Particle Swarm Optimization (PSO), Gazelle Optimization Algorithm (GOA), and its individual components, GWO and SMA, which have shown strong performance in recent literature for optimizing PID-type controllers. In addition, simulation results using three distinct reference paths, i.e. lemniscate, square, and cloverleaf; demonstrate that the GWO-SMA-optimized BSC-FOPID controller outperforms both adaptive dynamic compensation control (ADCC) and BSC-PID controller in position and posture tracking accuracy. Specifically, the BSC-FOPID controller achieves significant improvements, including average reductions of 55.65% in ISE and 38.25% in IAE for position control, as well as 62.12% and 38.95% improvements in ISE and IAE for posture control, respectively. These improvements highlight the controller's enhanced responsiveness and smoother error convergence, particularly during maneuvers involving sharp curves.

This paper presents a comprehensive review of the latest developments in fuzzy logic (FL) applications across critical domains which include energy harvesting (EH), ambient conditioning systems (ACS), and robotics and autonomous systems (RAS), highlighting FL's capability to address nonlinearities and uncertainties in diverse technological environments. Through a detailed comparative analysis of research trends over the past decade, it underscores the increasing significance of FL in EH and RAS, contrasting with the sustained interest in ACS. Furthermore, the evaluation of different fuzzy inference systems across domains provides valuable insights into their specific strengths and limitations, aiding researchers and practitioners in making informed decisions aligned with their application needs. Additionally, the paper explores advanced modifications and hybridizations of FL, such as swarm intelligence and integrations with other control strategies, emphasizing the necessity for robust and adaptive FL systems. The review also identifies key open problems and potential research directions, such as the demand for adaptive FL systems in EH and advanced optimization techniques in ACS and RAS. Overall, this state-of-the-art review not only summarizes the current state of FL applications but also outlines a roadmap for future research, offering valuable insights for advancing FL's role in handling uncertainties and nonlinearities in complex systems.