Fuzzy logic control with genetic membership function parameters optimization for the output regulation of a servomechanism with nonlinear backlash

Cázarez-Castro, Nohé R.; Aguilar, Luis T.; Castillo, Oscar

doi:10.1016/j.eswa.2009.11.091

Cited by 91 publications

(36 citation statements)

References 22 publications

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“…Optimization algorithms, such as genetic algorithms (GAs), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), have been used to find the optimal intelligent controller for WMR [11][12][13][14][15]. However, the proposed control scheme only ensures the stability of the closedloop system and the satisfactory tracking of the output to the given reference signal.…”

Section: Introductionmentioning

confidence: 99%

Near-Optimal Tracking Control of a Nonholonomic Mobile Robot with Uncertainties

Wang

2012

International Journal of Advanced Robotic Systems

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A combined kinematic/torque control law is developed by using a backstepping design approach for a nonholonomic mobile robot with two driving wheels mounted on the same axis to track a reference trajectory. The auxiliary velocity control inputs are designed for the kinematic steering system to make the posture error asymptotically stable. Next, a computed-torque controller is designed such that the mobile robot's velocities converge on the given velocity inputs in an optimal manner by converting the tracking control problem into the regulation problem whereby the uncertainties in the dynamics of mobile robots are considered. The proposed online and forward-in-time policy iteration (PI) algorithm based on approximate dynamic programming (ADP) is used to solve the optimal control problem with unknown internal dynamics by using single neural networks (NNs) to approximate the cost function. Afterwards, the nearoptimal control policy can be computed directly according to the cost function, which removes the action network appearing in the ordinary ADP method. The stability of the dynamical extension system is demonstrated using Lyapunov methods. The simulation results are provided to demonstrate the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Near-Optimal Tracking Control of a Nonholonomic Mobile Robot with Uncertainties

Wang

2012

International Journal of Advanced Robotic Systems

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“…Here, not only the coupled dynamics of the two robots but also the dynamic and parametric uncertainty of the control system is taken into account. Other robust control strategies based on fuzzy systems are applied to holonomic and non-holonomic systems in [12], where robustness conditions are obtained while taking both parametric uncertainty and external disturbances in the control system into account; other important results related to this topic are presented in [20] and [21], where a fuzzy logic controller is designed to solve the problem of output regulation in a servomechanism with nonlinear backlash. Results on PD controller tuning for rigid robots, taking into account the dynamics of the actuators (DC motors), are presented in [13], showing improvements on previous results.…”

Section: Introductionmentioning

confidence: 99%

New Method for Tuning Robust Controllers Applied to Robot Manipulators

Romero

Alcorta

Lara

et al. 2012

International Journal of Advanced Robotic Systems

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This paper presents a methodology to select the parameters of a nonlinear controller using Linear Matrix Inequalities (LMI). The controller is applied to a robotic manipulator to improve its robustness. This type of dynamic system enables the robust control law to be applied because it largely depends on the mathematical model of the system; however, in most cases it is impossible to be completely precise. The discrepancy between the dynamic behaviour of the robot and its mathematical model is taken into account by including a nonlinear term that represents the model´s uncertainty. The controller´s parameters are selected with two purposes: to guarantee the asymptotic stability of the closed-loop system while taking into account the uncertainty, and to increase its robustness margin. The results are validated with numerical simulations for a particular case study; these are then compared with previously published results to prove a better controller performance.

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“…GA has been used in tuning both the fuzzy MFs and the fuzzy rule bases [22] [23] in the areas of mobile robotics. The optimization of the MF parameters of a type-2 Mamdani FLS was presented in [24] based on fuzzy genetic architecture. Recently, the swarm optimization techniques, particle swarm optimization (PSO) and Ant Colony (AC) techniques have been proposed to automatically tune the FLC parameters and the membership function [25]- [33].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion

El-Gammal¹

2014

JILSA

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This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generator (SEIG). The aim of the developed control method is to automatically tune and optimize the scaling factors and the membership functions of the Fuzzy Logic Controllers (FLC) using Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO). Two Pulse Width Modulated voltage source PWM converters with a carrier-based Sinusoidal PWM modulation for both Generator-and Grid-side converters have been connected back to back between the generator terminals and utility grid via common DC link. The indirect vector control scheme is implemented to maintain balance between generated power and power supplied to the grid and maintain the terminal voltage of the generator and the DC bus voltage constant for variable rotor speed and load. Simulation study has been carried out using the MATLAB/Simulink environment to verify the robustness of the power electronics converters and the effectiveness of proposed control method under steady state and transient conditions and also machine parameters mismatches. The proposed control scheme has improved the voltage regulation and the transient performance of the wave energy scheme over a wide range of operating conditions.

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Fuzzy logic control with genetic membership function parameters optimization for the output regulation of a servomechanism with nonlinear backlash

Cited by 91 publications

References 22 publications

Near-Optimal Tracking Control of a Nonholonomic Mobile Robot with Uncertainties

Near-Optimal Tracking Control of a Nonholonomic Mobile Robot with Uncertainties

New Method for Tuning Robust Controllers Applied to Robot Manipulators

Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion

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