Sliding Mode Control Of Robotic With Adaptive Algorithm

By switzerlandersing On Sep 14, 2025

(PDF) Adaptive Sliding Mode Control Design With Genetic Algorithm For A 2-DOF Robotic ...

(PDF) Adaptive Sliding Mode Control Design With Genetic Algorithm For A 2-DOF Robotic ... The simulation results illustrate that the proposed method can effectively achieve better tracking performance compared with two other control methods, demonstrating the effectiveness of the proposed approach. This paper proposes a new adaptive super twisting global integral terminal sliding mode control algorithm for the trajectory tracking of autonomous robotic manipulators with uncertain parameters, unknown disturbances, and actuator faults.

| The Structure Figure Of Adaptive Sliding Mode Controller. | Download Scientific Diagram

| The Structure Figure Of Adaptive Sliding Mode Controller. | Download Scientific Diagram This study presents a novel adaptive sliding mode control with stochastic gradient descent (asmcsgd) approach for 2 dof robot arm manipulators, demonstrating superior robustness, reduced chattering, and precise trajectory tracking compared with conventional smc and sta controllers. This paper introduces an adaptive sliding mode control (asmc) law for a robot manipulator with a nonlinear uncertain model. sliding mode control using a combined adaptive algorithm, is also studied. In the work, an adaptive fuzzy sliding mode control algorithm is proposed for tracking control of robot manipulators. the fuzzy system uses a set of fuzzy rules, the parameters of which are modified in real time by adaptive laws, to approximate unknown nonlinearities. This paper presents the design of an adaptive sliding mode control based on a genetic algorithm for a 2 dof robotic manipulator under the presence of uncertainties.

(PDF) Validation Of A Classical Sliding Mode Control Applied To A Physical Robotic Arm With Six ...

(PDF) Validation Of A Classical Sliding Mode Control Applied To A Physical Robotic Arm With Six ... In the work, an adaptive fuzzy sliding mode control algorithm is proposed for tracking control of robot manipulators. the fuzzy system uses a set of fuzzy rules, the parameters of which are modified in real time by adaptive laws, to approximate unknown nonlinearities. This paper presents the design of an adaptive sliding mode control based on a genetic algorithm for a 2 dof robotic manipulator under the presence of uncertainties. Capability enhancement. the proposed control method employs an adaptive integral terminal mbined with an policy optimization (ppo), a type of deep reinforcement learning (drl). the ppo system incorporates an attention mechanism and long short term memory (lstm) neural networks,. The paper investigates a modified adaptive super twisting sliding mode control (astsmc) for robotic manipulators with input saturation. to avoid singular perturbation while increasing the convergence rate, a modified sliding mode surface (sms) is developed in this method. This paper proposes a self learning sliding mode control (slsmc) strategy with stability guarantee for the trajectory tracking of nonholonomic mobile robots (nmrs) under matched uncertainties, which improves the control performance of nmrs by optimizing the reaching law and the sliding mode surface of smc as well as retaining the finite time. Abstract robotic manipulators usually exhibit time‐varying, nonlinear, and coupled dynamics due to the parameter perturbations, disturbances, and other uncertainties. traditional control algorithms typically do not possess parameters' self‐adaptive learning ability, limiting the tracking performance of the robot.

Design And Implementation Of Sliding Mode Algorithm: Applied To Robot Manipulator-A Review | PDF

Design And Implementation Of Sliding Mode Algorithm: Applied To Robot Manipulator-A Review | PDF Capability enhancement. the proposed control method employs an adaptive integral terminal mbined with an policy optimization (ppo), a type of deep reinforcement learning (drl). the ppo system incorporates an attention mechanism and long short term memory (lstm) neural networks,. The paper investigates a modified adaptive super twisting sliding mode control (astsmc) for robotic manipulators with input saturation. to avoid singular perturbation while increasing the convergence rate, a modified sliding mode surface (sms) is developed in this method. This paper proposes a self learning sliding mode control (slsmc) strategy with stability guarantee for the trajectory tracking of nonholonomic mobile robots (nmrs) under matched uncertainties, which improves the control performance of nmrs by optimizing the reaching law and the sliding mode surface of smc as well as retaining the finite time. Abstract robotic manipulators usually exhibit time‐varying, nonlinear, and coupled dynamics due to the parameter perturbations, disturbances, and other uncertainties. traditional control algorithms typically do not possess parameters' self‐adaptive learning ability, limiting the tracking performance of the robot.

(PDF) INTEGRAL SLIDING MODE CONTROL FOR TRAJECTORY TRACKING CONTROL OF ROBOTIC MANIPULATORS ...

(PDF) INTEGRAL SLIDING MODE CONTROL FOR TRAJECTORY TRACKING CONTROL OF ROBOTIC MANIPULATORS ... This paper proposes a self learning sliding mode control (slsmc) strategy with stability guarantee for the trajectory tracking of nonholonomic mobile robots (nmrs) under matched uncertainties, which improves the control performance of nmrs by optimizing the reaching law and the sliding mode surface of smc as well as retaining the finite time. Abstract robotic manipulators usually exhibit time‐varying, nonlinear, and coupled dynamics due to the parameter perturbations, disturbances, and other uncertainties. traditional control algorithms typically do not possess parameters' self‐adaptive learning ability, limiting the tracking performance of the robot.

The Framework Of The Adaptive Sliding Mode Controller | Download Scientific Diagram