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Go to Editorial ManagerThis paper presents the design of robust four parameters (two degree of freedom) PI-PD controller based on Kharitonov theorem for antilock braking system. The Particle Swarm Optimization (PSO) method is used to tune the parameters of the proposed controller based on Kharitonov theorem to achieve the robustness over a wide range of system parameters change. The proposed cost function combines the time response specifications represented by the model reference and the frequency response specifications represented by gain margin and phase margin and the control signal specifications. The model reference control is used because of the antilock braking system is originally nonlinear and has different operating points. The robust stability is guaranteed by applying the Kharitonov theorem. Three types of road conditions (dry asphalt, gravel and icy) are used to test the proposed controller.
In this paper, the design of a robust controller for two wheeled inverted pendulum (TWIP) system is presented. In the first stage of the design, a full state feedback H2 control is designed for stabilizing the inclination of (TWIP) system to upright position. The H? controller for the stabilized system is synthesized in the second stage. The mathematical model of the system based on the Newtonian approach is developed. The results verify that the proposed controller can compensate the system parameter uncertainty with a more desirable time response specifications.
A new robust control algorithm is proposed for a class of nonlinear systems represented by a Single Link Manipulator (SLM) system. This algorithm is based on new techniques and methods in order to obtain a controller for the SLM system. First of all, the system is simplified using Variable Transformation Technique (VTT) in order to fit the analysis procedure. Then, a new idea of designing a model reference for the multiple states (n=4) system is presented to correspond the control design. Next, the Lyapunov Stability Analysis (LSA) is used to figure out a proper controller that can compensate the stability and the performance of the SLM system. After that, the Most Valuable Player Algorithm (MVPA) is applied to find the optimal parameters of the proposed controller to accomplish the optimum performance improvement. Finally, it can be concluded that the proposed control algorithm has improved the stability and the performance of the SLM system. In addition, the simulation results show the remarkable effects of the proposed nonlinear controller on the SLM system.
In this paper, the H-infinity Sliding Mode Control (HSMC) is designed to produce a new dynamic output feedback controller for trajectory tracking of the nonlinear human swing leg system. The human swing leg system represents the support of human leg or the humanoid robot leg which is usually modeled as a double pendulum. The thigh and shank of a human leg is represented by two pendulum links and the hip joint will connect the upper body to the thigh and the knee joint will connect the thigh to the shank. The external torques (servo motors) are applied at the hip and knee joints to move the muscles of thigh and shank. The results show that the HSMC can robustly stabilize the system and achieve a desirable time response specification better than if only H-infinity or SMC is used. This controller achieves the following specifications: sec, for hip joint and sec, for knee joint.
In this work, the control of Translational Oscillations with a Rotational Actuator (TORA) system is presented in this paper. The optimal sliding mode controller is proposed to control the two DOF underactuated mechanical system. The nonlinear coupling from the rotational to the translational motion is the main problem that faces the controller design. The H2 sliding mode controller is designed to give a better performance if only sliding mode control is used. The results illustrate that the proposed H2 sliding mode controller can achieve the stabilization of the system with the variation in system parameters and disturbance.
In this work, the design procedure of a hybrid robust controller for crane system is presented. The proposed hybrid controller combines the linear quadratic regulator (LQR) properties with the sliding mode control (SMC) to obtain an optimal and robust LQR/SMC controller. The crane system which is represented by pendulum and cart is used to verify the effectiveness of the proposed controller. The crane system is considered one of the highly nonlinear and uncertain systems in addition to the under-actuating properties. The parameters of the proposed LQR/SMC are selected using Particle Swarm Optimization (PSO) method. The results show that the proposed LQR/SMC controller can achieve a better performance if only SMC controller is used. The robustness of the proposed controller is examined by considering a variation in system parameters with applying an external disturbance input. Finally, the superiority of the proposed LQR/SMC controller over the SMC controller is shown in this work.
In this paper, the robustness properties of sliding mode control (SMC) which is designed to produce a dynamic output feedback controller to achieve robustness for trajectory tracking of the nonlinear human swing leg system is presented. The human swing leg represents the support of human leg or the humanoid robot leg which is usually modeled as a double pendulum. The thigh and shank of a human leg will respect the pendulum links, hip and knee will connect the upper body to thigh and then shank respectively. The total moments required to move the muscles of thigh and shank are denoted by two external (servomotors) torques applied at the hip and knee joints. The mathematical model of the system is developed. The results show that the proposed controller can robustly stabilize the system and achieve a desirable time response specification.
In this paper, the derivation of optimal control using state derivative feedback to obtain a new control approach is presented. A control approach similar to linear quadratic regulator (LQR) is applied to find the optimal gain matrices that achieve the desired performance. The effectiveness and robustness of the proposed controller can be shown using the uncertain and under-actuated overhead crane system. The results show that the proposed controller can robustly stabilize the system in the presence of system parameters uncertainty. Further, a more desirable time response specifications can be obtained using state derivative feedback control in comparison to the state feedback control.