Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
The removal of rust from large equipment such as trains and ship hulls poses a significant challenge. Traditional methods, such as chemical cleaning, flame rust removal, and laser rust removal, suffer from drawbacks such as high energy consumption, operational complexity, and poor mobility. Sandblasting and high-pressure water jet rust removal face issues such as high consumable costs and environmental pollution. Existing robotic grinding systems often rely on precise measurement of the workpiece surface geometry to perform deburring and polishing tasks; however, they lack the sufficient adaptability and robustness required for rust removal operations. To address these limitations, this study proposes a floating grinding actuator scheme based on compound force-position fuzzy control. By implementing simplified path-point planning, continuous grinding and rust removal can be achieved without requiring the pre-measurement of workpiece geometry data. This solution integrates force and laser displacement sensors to provide real-time compensation for path deviations and ensures adaptability to complex surfaces. A fuzzy derivative-leading PID algorithm was employed to control the grinding force, enabling adaptive force regulation and enhancing the control precision. Rust removal test results demonstrate that under varying advancing speeds, fuzzy derivative-leading PID control can significantly reduce fluctuations in both the grinding force and average error compared to traditional PID control. At a speed of 40 mm/s, excellent control performance was maintained, achieving a rust removal rate of 99.73%. This solution provides an efficient, environmentally friendly, and high-precision automated approach to rust removal using large-scale equipment....
Advances in industrial 5G communication technologies and robotics create new possibilities while also increasing the complexity and variability of networked control systems. The additional throughput and lower latency provided by 5G networks enable applications such as teleoperation of machinery, flexible reconfigurable robotic manufacturing cells, or automated guided vehicles. These use cases are set up in dynamic network environments where communication latency and jitter become critical factors that must be managed. Despite the advancements in 5G technologies, such as ultra-reliable low-latency communication (URLLC), adaptive control strategies such as reinforcement learning (RL) remain critical to handle unpredictable network conditions and ensure optimal system performance in real-world industrial applications. In this paper, we investigate the potential of RL in scenarios with communication latency similar to a public 5G deployment. Our study includes an incremental improvement by utilizing long short-term memory-based neural networks in combination with proximal policy optimization in this scenario. Our findings indicate that incorporating latency into the training environment enhances the robustness and efficiency of RL controllers, especially in scenarios characterized by variable network delays. This exploration provides insights into the feasibility of using RL for networked control systems and underscores the importance of incorporating realistic network conditions into the training phase....
Greenhouse farming has brought a revolution in agriculture as it provides a climate favorable to crops all year round. Besides securing the production of foods of higher quality, it also extends the growing seasons and protects crops from pests and harsh weather. The greenhouse is centrally controlled by the user due to the technological advancements of devices such as cell phones and a control system of temperature, which is important for the plant. To realize remote real-time automated monitoring of the greenhouse based on the user settings, an Android app was developed in this study....
This paper introduces an Evolutionary Computing Control Strategy (ECCS) for the motion control of nonholonomic robots, and integrates an ordinary differential equation (ODE)-based kinematics model with a nonlinear model predictive control (NMPC) strategy and a particle-based evolutionary computing (PEC) algorithm. The ECCS addresses the key challenges of traditional NMPC controllers, such as their tendency to fall into local optima when solving nonlinear optimization problems, by leveraging the global optimization capabilities of evolutionary computation. Experiment results on the MATLAB Simulink platform demonstrate that the proposed ECCS significantly improves motion control accuracy and reduces control errors compared to linearized MPC (LMPC) strategies. Specifically, the ECCS reduces the maximum error by 90.6% and 94.5%, the mean square error by 67.8% and 92.6%, and the root mean square error by 43.5% and 70.3% in velocity control and steering angle control, respectively. Furthermore, experiments are separately implemented on the CarSim platform and the physical environment to verify the availability of the proposed ECCS. Furthermore, experiments are separately implemented on the CarSim platform and the physical environment to verify the availability of the proposed ECCS. These results validate the effectiveness of embedding ODE kinematics into the evolutionary computing framework for robust and efficient motion control of nonholonomic robots....
This paper presents the simulation and controller optimization of a quadrotor Unmanned Aerial Vehicle (UAV) system. The quadrotor model is derived adopting the Newton-Euler approach, and is intended to be constituted by four three-phase Permanent Magnet Synchronous Motors (PMSM) controlled with a velocity control loop-based Field Oriented Control (FOC) technique. The Particle Swarm Optimization (PSO) algorithm is used to tune the parameters of the PID controllers of quadrotor height, quadrotor attitude angles, and PMSMs’ rotational speeds, which represent the eight critical parameters of the PMSM-quadrotor UAV system. The PSO algorithm is designed to optimize eight Square Error (SE) cost functions which quantify the error dynamics of the controlled variables. For each stabilization task, the PID tuning is divided in two phases. Firstly, the PSO optimizes the error dynamics of altitude and attitude angles of the quadrotor UAV. Secondly, the desired steady-state rotational speeds of the PMSMs are derived, and the PSO is used to optimize the motors’ dynamics. Finally, the complete PMSM-Quadrotor UAV system is simulated for stabilization during the target task. The study is carried out by means of simulations in MATLAB/Simulink®....
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