In this paper, we propose an adaptive sliding mode control strategy for a 3D cable-driven parallel robot. The proposed control technique is widely used for dealing with nonlinear systems uncertainties and for improving the robot performance in terms of tracking a desired path. The main contribution of this work is firstly: the graphical user interface (GUI) witch presents a point-to-point command, thus by the visualization of the end-effector position. Secondly, the sliding mode control is modeling for applied to the dynamic model for different trajectories in order to test the accurate tracking of the robot to a desired path. The effectiveness of the proposed control strategy is demonstrated through different simulation results.
A parallel optimization of Proportional, Integral and Derivative (PID) controller and a sixth order phase lead-lag compensator of a high order naturally oscillatory hydraulic actuator are proposed in this paper. The PID controller parameters (proportional, integral and derivative) and the compensator parameters (gain, poles and zeros) are obtained by minimizing the Integral of Time Absolute Error (ITAE) criterion. The proposed methods are demonstrated through a realistic numerical synthesis example of a hydraulic actuator dedicated to a semi-active suspension modeled by an eighth order transfer function. A simulation comparison is investigated for both controllers to compare their performances.
Working with high-order transfer functions needs a lot of work and leads to major difficulties in analysis, simulation, and control design. Model reduction studies the large-scale system properties and helps to reduce these difficulties. In this paper, the genetic algorithms (GA) optimization method is used to calculate the second reduced order model (ROM) of the original high order model (HOM) of the actuator. Here, the studied hydraulic actuator is a single input, single output (SISO), and linear time invariant (LTI) system that can be modeled by an eight-order transfer function with uncontrollable modes. The genetic algorithms are successfully applied to reduce the original model order using MATLAB software. Thus, the proposed approach is applied to both the original and suggested reduced order models to check the effectiveness of the reduction method. Finally, a digital RST roll control based on the robust pole placement is applied for the two models, and simulations are carried out to show the effectiveness of the control strategy
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