Nur Syazreen Ahmad’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.

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.

This study introduces an enhanced algorithm for global path planning of Differential Wheeled Mobile Robots (DWMRs) that merges the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). This hybrid strategy, termed HWPSO, is designed to leverage WOA's exploration strength with PSO's efficient exploitation, specifically targeting the challenges of non-holonomic constraints in complex terrains. To validate the effectiveness of the proposed algorithm, its performance is evaluated across five diverse environments and compared against PSO, WOA, and Grey Wolf Optimization which is widely used for mobile robot path planning. Moreover, the comparison broadens to encompass four established environments from the literature where algorithms based on firefly, ant colony, A*, and other PSO variants have previously exhibited optimal performance. Additionally, a new environment is introduced to analyze the efficacy of the proposed approach for path planning for two DWMRs. Simulation results consistently demonstrate the superiority of the proposed HWPSO, manifesting performance improvements of up to 19.3% for path length reduction and up to 12.7% for DWMR travel duration reduction when compared to other methods. This underscores the efficacy of the proposed hybrid approach in achieving enhanced path planning outcomes for DWMRs in diverse scenarios.

This paper shows how two microphones in an endfire array configuration was used to perform beamforming. The setup uses two condenser microphones and a sound card to allow multiple sources to be input to the computer at the same time. A cross-correlation calculation was used to determine the time shift between the two mics. Using the Delay and Sum algorithm, the time shift can be corrected, and the mic signals can be added to a superposition.