Adaptive control optimization using NLTA algorithms for mechatronic systems.

The Non-Linear Threshold Accepting (NLTA) algorithm has been successfully applied in electrical and electronic systems, particularly in optimizing power distribution and voltage regulation. However, its application in mechatronic systems remains largely unexplored. Given that most Unmanned Ground Vehicles (UGVs) and robotic systems used in logistics and industrial environments integrate electrical, electronic, and mechanical subsystems, an advanced adaptive control strategy is essential to ensure optimal performance in dynamic environment be it in engineering/manufacturing or last-mile delivery environments. Traditional Proportional-Integral-Derivative (PID) controllers, while widely adopted, can still be improve for real-time adaptability, leading to improved efficiency in trajectory control, response time, and energy consumption. These possibilities necessitate needs for a more robust control framework with better and improved capabilities of dynamically adjusting to operational uncertainties.

In this research a comparative performance evaluation was conducted using Mathematical models and MATLAB/Simulink simulations, benchmarking the NLTA algorithm against conventional PID controllers and other heuristic optimization techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Differential Evolution (DE). Unlike some of the traditional PID controllers that require manual tuning and often fail to adapt to non-linear system variations, NLTA employs an adaptive threshold mechanism to iteratively optimize control gains, ensuring improved operational stability, energy efficiency, and trajectory accuracy. The NLTA algorithm’s ability to better self-adjust in real time provides a significant advantage over existing control methods, making it a better and more viable alternative for enhancing the performance and reliability of mechatronics or other systems in need of optimization. 

A comparative analysis was conducted using MATLAB/Simulink simulations to benchmark NLTA against conventional PID controllers and other heuristic-based optimization methods, including Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Differential Evolution (DE). The results demonstrate that NLTA outperforms traditional control strategies by achieving faster response times, reduced settling time, enhanced robustness against environmental disturbances, and improved overall system efficiency. While the NLTA model also holds promise for warehouse layout optimization where dynamic reconfiguration could enhance operational efficiency this research focuses solely on evaluating its effectiveness as a control strategy for mechatronic systems compared to existing PID-based approaches.

The findings reinforce the potential of NLTA as an advanced control framework for system and operational optimization also, bridging the gap between electrical, electronic, and mechanical control integration. Future work will explore real-world deployment, AI-driven predictive modeling for enhanced adaptability, and the extension of NLTA’s capabilities to logistics facility layout optimization. By validating NLTA’s effectiveness against traditional PID controllers, this research is aimed to contribute to the ongoing evolution of intelligent control mechanisms in logistics engineering especially autonomous last-mile delivery systems.