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Go to Editorial ManagerThree-phase induction motors (IM) are widely utilized in various applications such as fans, milling machines, conveyor systems, robotics, pumps, and heavy machinery. Tasks that require precise speed control often demand additional control systems to ensure efficient and accurate operation. Unfortunately, the speed control of induction motors is a difficult problem due to system nonlinearity, parameter variations, and external disturbances. Therefore, standard proportional-integral-differential (PID) control systems are among the various techniques available for motor speed regulation. In this paper, the PID controller is used as a criteria to assess the efficacy of the proposed approach. This work specifically addresses the use of a toolbox controller for active disturbance rejection control (ADRC) to control the speed of a three-phase IM. The ADRC toolbox is a software tool (often a MATLAB/Simulink toolbox or library) designed to help users implement ADRC methods in their systems without having to code the algorithms from scratch. The speed performance of the motor was compared between PID and ADRC controllers. The evaluation was performed under steady state operation, with the intention of establishing and maintaining the speed of 1400 rpm. By simulation, the performance of the controllers in respect of rise time, settling time and the current consumption in all the simulations was analyzed conducted through MATLAB/Simulink. Simulation data indicate that the ADRC system outperforms conventional PID controllers when used with induction motors. Performance indicators such as ITAE (14.3 vs. 19.7), ISE (234.01 vs. 251.13), and RMSE (644.21 vs. 660.62) demonstrate that the ADRC system provides higher accuracy, faster response, and more efficient speed regulation control compared to the PID system. The results reveal that ADRC has better performance, especially in disturbance suppression. starting current, which contributes to extending the motor’s operational lifespan. Additionally, ADRC ensures smooth acceleration of rotor speed with a lower rise time of 0.3s as well as settling time of 0.36s, allowing the system to reach steady-state speed more efficiently. ADRC is more adaptive to nonlinear and unpredictable settings than PID since it actively compensates for total disturbances and estimates them.
Dynamic modeling of a robot manipulator is a central problem in an accurate robot control. In this paper; the dynamic equations of motion were derived by using Eular-Lagrange method for a six degree of freedom articulated robot manipulator based on the geometrical jacobian construction for each link and actuator. In addition, friction effects beside the end effector forces that act the environment are considered. A Matlab Simulink plant is developed to embrace the theoretical work and simulate the dynamic response for a designed nonlinear controller Proportional Derivative plus Gravity (PD+G), also a modified controller is applied to reject the disturbances and the internal friction effect where the settling errors were 3.57E-6, 2.09E-7, -3.63E-6, 8.84E-6, -5.39E-8 and -4.39E-5 (deg) for joints one to six respectively. The presented approach can be applicable to solve the dynamic problem of other n-link robot manipulators and achieve a suitable solution for tracking trajectories.
Diabetes is one of the most critical diseases in the world which requires measuring the concentration of glucose also the injection of insulin to control the glucose rate in the body. The proposed controller is applied to the Bergman’s three-state minimal patient model, where the model is considered certain but with unknown meal. In the present work, a nonlinear controller is designed to control the concentration of glucose based on the Backstepping approached with a sliding mode for observing the disturbance meal. So will have estimated the meal and have canceled the effect that the glucose concentration has regulating to the basal level._x000D_ The effectiveness of the proposed controller, which represent the insulin dose, is proved via simulating the Bergman’s model with designed controller via MATLAB Simulink software. The result clarify the ability and the robustness of the proposed controller.
Transfer function characteristics of a DC machine are used in this paper to estimate speed and torque in four quadrant operation modes. Estimation speed and torque control based on a DC machine transfer function is implemented by measuring the DC chopper instantaneous average output voltage and current. MATLAB\SIMULINK is used to implement the DC drive circuit in the forward and reverse motoring and regenerative modes, respectively. The DC drive system is simulated at different speed and load torque values in steady state and dynamic operating conditions. Simulation results demonstrate success of the sensorless and PI controller systems, which gives satisfactory agreements between the estimated, actual and reference speed and torque values.
This paper proposes the design and simulation of Interval Type-2 Fuzzy Logic Control using MATLAB/Simulink to control the position of the bucket of the backhoe excavator robot during digging operations. In order to reach accurate position responses with minimum overshoot and minimum steady state error, Ant Colony Optimization (ACO) algorithm is used to tune the gains of the position and force parts for the force-position controllers to obtain the best position responses. The joints are actuated by the electro-hydraulic actuators. The force-position control incorporating two-Mamdani type-Proportional-Derivative-Interval Type-2 Fuzzy Logic Controllers for position control and 3-Proportional-Derivative Controllers for force control. The nonlinearity and uncertainty in the model that inherit in the electro hydraulic actuator system are also studied. The nonlinearity includes oil leakage and frictions in the joints. The friction model is represented as a Modified LuGre friction model in actuators. The excavator robot joints are subjected to Coulomb, viscous and stribeck friction. The uncertainty is represented by the variation of bulk modulus. It can be shown from the results that the ACO obtain the best gains of the controllers which enhances the position responses within the range of (19, 23 %) compared with the controllers tuned manually.
This paper proposes robust control for three models of the linear inverted pendulum (one mass linear inverted pendulum model, two masses linear inverted pendulum model and three masses linear inverted pendulum model) which represents the upper, middle and lower body of a bipedal walking robot. The bipedal walking robot is built of light-weight and hard Aluminum sheets with 2 mm thickness. The minimum phase system and non-minimum phase system are studied and investigated for inverted pendulum models. The bipedal walking robot is programmed by Arduino microcontroller UNO. A MATLAB Simulink system is built to embrace the theoretical work. The results showed that one linear inverted pendulum is the worst performance, worst noise rejection and the worst set point tracking to the zero moment point. But two masses linear inverted pendulum models and three masses linear inverted pendulum model have a better performance, a better high-frequency noise rejection characteristic and better set-point tracking to the zero moment point.
Photovoltaic (PV) systems have gained importance as one of the major renewable energy technologies because of its clean and sustainable characteristics; however, PV nonlinear current-voltage and power-voltage characteristics are a significant factor in limiting the maximum available power determined by the amount of available power under different environmental and load conditions. Maximum Power Point Tracking (MPPT) algorithms are one of the most important performance enhancements to help make PV energy conversion systems more efficient and reliable. Conventional MPPT techniques such as Perturb and Observe (P&O) have been widely used due to their simple and easy implementation in terms of control strategies, however, they exhibit steady-state oscillations, slow convergence, and poor dynamic performance under fast changing conditions of irradiation and load. To overcome these limitations, validation of intelligent and bio-inspired optimization and MPPT algorithms have attracted a great deal of interest. This paper includes a comprehensive comparative analysis of three MPPT techniques that are: conventional P&O algorithm, Grey Wolf Optimization algorithm (GWO), and Teaching-Learning-Based Optimization algorithm (TLBO). A detailed model of PV system coupled with dc-dc boost converter is built in Matlab/Simulink and the algorithms are tested with a constant irradiation and variable load and with simultaneous irradiation and load variation. Performance criteria like tracking performance, speed of convergence, steady state oscillations and robustness under dynamic conditions are analyzed. Based on the simulation results it is proven that both GWO and TLBO clearly outperforms the conventional P&O algorithm. Amongst all the optimization-based approaches, TLBO provides good performance with near-instantaneous convergence, minimum oscillations and consistent tracking efficiency is close to 100% for all the tested scenarios. The results prove that TLBO-based MPPT offers solid and computationally efficient solution for real world applications of PV systems especially in those with frequent and random operating condition variation environment.