Design of Prisoner’s dilemma based fuzzy logic computed torque controller with Lyapunov synthesis linguistic model for PUMA-560 robot manipulator

Wang

Advances in Mechanical Engineering

2019

This article presents a computed-torque controller plus adaptive fuzzy trajectory feedforward compensator suitable for the trajectory tracking control of uncertain underwater vehicle. To address the issue of unavailable normalization factor, an adaptive fuzzy trajectory feedforward compensator is proposed and assembled at the input trajectory level of the computed-torque controller rather than at the joint drive torque position. The compensator serving as a low-pass filter is implemented outside the inner control loop by adjusting the desired characteristic depth. Due to the nearly unchanged internal control algorithm, the adaptive fuzzy compensator is feasible to implement and is robust when varying the feedback gain in the inner control loop. Moreover, an adaptive dead zone fuzzy compensator is designed to reduce the effect of the dead zone on the actuators of underwater vehicles according to the unknown input dead zone characteristics. To validate the effectiveness of the proposed controller, simulations are conducted for a desired characteristic depth, and the performance of the proposed controller has been compared with conventional controllers to illustrate the usefulness and efficiency of the proposed controller.

Section: Convergence Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy depth control with trajectory feedforward compensator for autonomous underwater vehicles

Wang

Advances in Mechanical Engineering

2019

“…There are two main problems with CTC: first, it is not robust against uncertainties, and second, it needs exact dynamical information about the plant which is not possible in practical conditions. However, some researchers made improvements in CTC performance by combining this approach with some advanced control techniques (Chen et al , 2016; Kumar et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

Robotic arm tracking control through smooth switching LPV controller based on LPV modeling and torque approximation

Fazli,

Kazemi

2024

Purpose This paper aims to propose a new linear parameter varying (LPV) controller for the robot tracking control problem. Using the identification of the robot dynamics in different work space points about modeling trajectory based on the least square of error algorithm, an LPV model for the robotic arm is extracted. Design/methodology/approach Parameter set mapping based on parameter component analysis results in a reduced polytopic LPV model that reduces the complexity of the implementation. An approximation of the required torque is computed based on the reduced LPV models. The state-feedback gain of each zone is computed by solving some linear matrix inequalities (LMIs) to sufficiently decrease the time derivative of a Lyapunov function. A novel smoothing method is used for the proposed controller to switch properly in the borders of the zones. Findings The polytopic set of the resulting gains creates the smooth switching polytopic LPV (SS-LPV) controller which is applied to the trajectory tracking problem of the six-degree-of-freedom PUMA 560 robotic arm. A sufficient condition ensures that the proposed controller stabilizes the polytopic LPV system against the torque estimation error. Practical implications Smoothing of the switching LPV controller is performed by defining some tolerances and creating some quasi-zones in the borders of the main zones leading to the compressed main zones. The proposed torque estimation is not a model-based technique; so the model variation and other disturbances cannot destroy the performance of the suggested controller. The proposed control scheme does not have any considerable computational load, because the control gains are obtained offline by solving some LMIs, and the torque computation is done online by a simple polytopic-based equation. Originality/value In this paper, a new SS-LPV controller is addressed for the trajectory tracking problem of robotic arms. Robot workspace is zoned into some main zones in such a way that the number of models in each zone is almost equal. Data obtained from the modeling trajectory is used to design the state-feedback control gain.

Iran J Sci Technol Trans Electr Eng

“…However, the plant uncertainties, such as parameter perturbations, errors in modeling, and exogenous disturbances, can quickly destroy the control efficiency (Piltan et al 2012a, b, c). Research on CTC is significantly growing by using some advanced control techniques (Gang et al 2013;Zhao and JI, 2012;Li and Sun 2009;Chen et al 2016;Kumar et al 2016;Conway and Horowitz 2010;Mu ¨ller and Hufnagel 2012;Rahideh et al 2012;Jiang et al 2020). Although it is very efficient to use advanced adaptive skills in dealing with the mentioned uncertainties, they cannot successfully reject the unstructured uncertainty outcomes (Wu et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Manipulator Dynamic Nonlinearity Approximation Based on Polytopic LPV Modeling for Robot Tracking Control Problem

Fazli

Kazemi

2022

This article proposes a novel robot control strategy to compensate the robotic manipulator nonlinearity effects by applying the linear parameter-varying (LPV) modeling technique. A polytopic LPV description of the robot is created by the usual Lagrangian linearizing about an arbitrary complex full-scope trajectory. The principal component analysis is implemented for the parameter set mapping and generating a reduced polytopic LPV model to decrease the implementation complexity. An estimation of the required torque is calculated based on the constructed polytopic model to neutralize the nonlinear terms of the robot dynamics. The control gain matrix is involved in a set of linear matrix inequalities to sufficiently decrease the time derivative of a Lyapunov function. A sufficient condition ensures that the proposed controller stabilizes the polytopic LPV system against the torque estimation error. Finally, the proposed scheme is applied to the six-degree-offreedom PUMA560 manipulator for the tracking control problem.