Design and Implementation of Welding Mobile Robot Using a Proposed Control Scheme Based On Its Developed Dynamic Modeling for Tracking Desired Welding Trajectory

This paper presents a proposed control scheme that makes the combination of a kinematic controller (KC) and an integral sliding mode controller (ISMC) for a welding mobile robot (WMR) to track a desired welding path. First, a posture tracking error vector is defined and a kinematic controller is designed based on kinematic modeling to make the tracking error vector go to zero asymptotically. Second, a sliding surface vector is defined based on the velocity tracking error vector and its integral term. And then, an integral sliding mode dynamic controller is designed based on developed dynamic modeling to make velocity tracking error vector also go to zero asymptotically. The above controllers are obtained by backstepping method. The stability of system is proved based on the Lyapunov stability theory. To implement the designed tracking controller, a control system is developed based on DSP F28355 and ATmega328. A scheme for measuring the posture tracking error vector using torch sensor is presented. The simulation and experiment results are shown to illustrate effectiveness and the applicability to the welding industry field of the proposed controller

Modelling and Determining Parameters of a Solar Photovoltaic Cell based on Voltage and Current Measurements

An appropriate model of a solar photovoltaic (SPV) cell is essential for control, operation, and prediction of SPV systems. Simultaneously, it is equally vital for determining as accurately as possible the parameters of that model. There are currently single-diode (SD), double-diode (DD) and triple-diode (TD) SPV cell models needing to be determined for various applications. A simple and effective approach is proposed for determining the parameters of SPV cell models through voltage and current measurements; as well as the transformation of the estimation problem into the optimization problem. Then, stochastic fractal search (SFS) algorithms with the benefits of finding the global optimal solution in a few generations and avoiding getting stuck in locally optimal solutions are proposed to apply for the above one. The achievements are compared to those by other existing algorithms such as a particle swarm optimization (PSO) and Chaos PSO algorithms to validate the proposals.</p

Water Wave Optimization Algorithm-Based Dynamic Optimal Dispatch Considering a Day-Ahead Load Forecasting in a Microgrid

A novel strategy is proposed to tackle an optimal dispatch of a microgrid in response to dynamic conditions, utilizing a water wave optimization (WWO) algorithm and considering a day-ahead load forecasting. Amongst meta-heuristic algorithms, the WWO algorithm stands out in terms of population size, parameter tuning, exploitation and exploration, convergence speed, as well as optimization mechanism. It leverages its ability to efficiently explore solution spaces and adapt to changing conditions. It is applied to the dynamic optimal dispatch of a microgrid with the uncertainty of load power considered and solved by day-ahead load forecasting. It dynamically adjusts the microgrid operation in response to these inputs, ensuring optimal decision-making in the face of varying load scenarios. With the competition of various day-ahead load forecasting techniques in the microgrid, a multi-variate linear regression (MLR) model shows its advantage features, being more transparent, more effective, and more robust than other techniques, especially transparent explainability, as well as simple and fast in model training. These are requirements to achieve the result of day-ahead load forecasting. Thus, the MLR model is proposed to forecast day-ahead load in the microgrid in this paper. The simulation results show that the percentage error (&lt;italic&gt;PE&lt;/italic&gt;) between the MLR model-based forecasted and actual load powers is always less than 4.42%, the mean absolute percentage error (&lt;italic&gt;MAPE&lt;/italic&gt;) of the forecasting result is 3.33%, and the execution time is 49 (s). These achievements meet the accurate and fast requirements. They are completely competitive with the results of using other techniques such as convolutional neural networks (CNN) and long short-term memory (LSTM), especially in the execution time. This has contributed to improving the efficiency of the dynamic optimal dispatch in the microgrid. Then, the diesel generation, battery energy storage, and total microgrid generation costs are 68.76 (&amp;#x0024;), 5.09 (&amp;#x0024;), and 73.85 (&amp;#x0024;) respectively by using the WWO algorithm which are better than those by using a genetic algorithm (GA), a non-dominated sorting genetic algorithm-II (NSGA-II), a particle swarm optimization (PSO) algorithm, and a transient search optimization (TSO) algorithm in the microgrid. The findings offer valuable insights for microgrid operators, energy planners, and policymakers seeking sustainable and cost-effective solutions for distributed energy resource management.</p