This paper is focused on a comparison between classic Feedback LinearizationControl and a proposed Active Disturbance Rejection Control method. The proposed technique does not require a precise mathematical description of the system, since it is based on the online estimation and rejection of the unmodeled elements of the dynamics. Robustness of the closed-loop control system (against external perturbation and parameters uncertainty) is discussed here. A model of a SCARA robot manipulator is used in the conducted case study as an exemplary plant. Conclusions are supported with results obtained with numerical simulations.
The classical sliding mode control (SMC) is a robust control scheme widely used for dealing with nonlinear systems uncertainties and disturbances. However, the conventional SMC major drawback in real applications is the chattering phenomenon problem, which involves extremely high control activity due to the switched control input. To overcome this handicap, a pratical design method that combines an adaptive neural network and sliding mode control principles is proposed in this paper. The controller design is divided into two phases. First, the chattering phenomenon is removed by replacing the sign function included in the switched control by a continuous smooth function; basing on Lyapounov stability theorem. Then, an adaptive linear neural network, that has the role of online estimate the equivalent control in the neighborhood of the sliding manifold, is developed when the controlled plant is poorly modeled. Simulation results show clearly the satisfactory chattering free tracking performance of proposed controller when it is applied for the joints angular positions control of a 6-DOF PUMA 560 robot arm.
In this paper, a spatial cable-driven parallel robot with four cables is simulated by using a technique of Model Predictive Control (MPC). The main contribution of this work is firstly: a graphical user interface has been developed and implemented so as to control the position and the end effector's speed, takes into account the variation of mass of the end-effector (0.01-0.1 kg) and massless cables. Secondly, we study the response of differential equations for our system with MPC control for different trajectories in order to test the accurate tracking of the robot for a desired trajectory simulation using MATLAB/Simulink. The effectiveness of the proposed control strategy is demonstrated to improve the robot performance.
This paper is focused on the comparison between two robust control schemes applied to PUMA 560 robot arm. The inner control loop is designed basing on the so-called computed torque technique. Concerning the outer loop, we have first proposed a nonlinear controller; the robust stability and desired position tracking are guaranteed using Lyapunov formulation. In the second approach, we have developed an active disturbance rejection controller for the outer loop; this controller is based on a nonlinear observer used for estimating and then compensating the residual uncertainties of the linearized manipulator.
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