Fuzzy logic PD controller for trajectory tracking of an autonomous differential drive mobile robot (i.e. Quanser Qbot)

2018 ◽  
Vol 45 (1) ◽  
pp. 23-33 ◽  
Author(s):  
Ali Alouache ◽  
Qinghe Wu
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Trajectory Tracking ◽  
Obstacle Detection ◽  
Pd Controller ◽  
Content Type ◽  
Control Approach ◽  
Differential Drive ◽  
Hybrid Techniques ◽  
Fuzzy Pd

Purpose The aim of this paper is to propose a robust robot fuzzy logic proportional-derivative (PD) controller for trajectory tracking of autonomous nonholonomic differential drive wheeled mobile robot (WMR) of the type Quanser Qbot. Design/methodology/approach Fuzzy robot control approach is used for developing a robust fuzzy PD controller for trajectory tracking of a nonholonomic differential drive WMR. The linear/angular velocity of the differential drive mobile robot are formulated such that the tracking errors between the robot’s trajectory and the reference path converge asymptotically to zero. Here, a new controller zero-order Takagy–Sugeno trajectory tracking (ZTS-TT) controller is deduced for robot’s speed regulation based on the fuzzy PD controller. The WMR used for the experimental implementation is Quanser Qbot which has two differential drive wheels; therefore, the right/left wheel velocity of the differential wheels of the robot are worked out using inverse kinematics model. The controller is implemented using MATLAB Simulink with QUARC framework, downloaded and compiled into executable (.exe) on the robot based on the WIFI TCP/IP connection. Findings Compared to other fuzzy proportional-integral-derivative (PID) controllers, the proposed fuzzy PD controller was found to be robust, stable and consuming less resources on the robot. The comparative results of the proposed ZTS-TT controller and the conventional PD controller demonstrated clearly that the proposed ZTS-TT controller provides better tracking performances, flexibility, robustness and stability for the WMR. Practical implications The proposed fuzzy PD controller can be improved using hybrid techniques. The proposed approach can be developed for obstacle detection and collision avoidance in combination with trajectory tracking for use in environments with obstacles. Originality/value A robust fuzzy logic PD is developed and its performances are compared to the existing fuzzy PID controller. A ZTS-TT controller is deduced for trajectory tracking of an autonomous nonholonomic differential drive mobile robot (i.e. Quanser Qbot).

Real-time trajectory tracking control of Stewart platform using fractional order fuzzy PID controller optimized by particle swarm algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zafer Bingul ◽  
Oguzhan Karahan
Keyword(s):  
Dynamic Model ◽  
Fractional Order ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Fuzzy Pid ◽  
Simulation Environment ◽  
Trajectory Tracking Control ◽  
Content Type ◽  
Control Approach ◽  
Coupling Dynamic

Purpose The purpose of this paper is to address a fractional order fuzzy PID (FOFPID) control approach for solving the problem of enhancing high precision tracking performance and robustness against to different reference trajectories of a 6-DOF Stewart Platform (SP) in joint space. Design/methodology/approach For the optimal design of the proposed control approach, tuning of the controller parameters including membership functions and input-output scaling factors along with the fractional order rate of error and fractional order integral of control signal is tuned with off-line by using particle swarm optimization (PSO) algorithm. For achieving this off-line optimization in the simulation environment, very accurate dynamic model of SP which has more complicated dynamical characteristics is required. Therefore, the coupling dynamic model of multi-rigid-body system is developed by Lagrange-Euler approach. For completeness, the mathematical model of the actuators is established and integrated with the dynamic model of SP mechanical system to state electromechanical coupling dynamic model. To study the validness of the proposed FOFPID controller, using this accurate dynamic model of the SP, other published control approaches such as the PID control, FOPID control and fuzzy PID control are also optimized with PSO in simulation environment. To compare trajectory tracking performance and effectiveness of the tuned controllers, the real time validation trajectory tracking experiments are conducted using the experimental setup of the SP by applying the optimum parameters of the controllers. The credibility of the results obtained with the controllers tuned in simulation environment is examined using statistical analysis. Findings The experimental results clearly demonstrate that the proposed optimal FOFPID controller can improve the control performance and reduce reference trajectory tracking errors of the SP. Also, the proposed PSO optimized FOFPID control strategy outperforms other control schemes in terms of the different difficulty levels of the given trajectories. Originality/value To the best of the authors’ knowledge, such a motion controller incorporating the fractional order approach to the fuzzy is first time applied in trajectory tracking control of SP.

Performance improvement of direct torque control for induction motor drive via fuzzy logic-feedback linearization

2019 ◽  
Vol 38 (2) ◽  
pp. 672-692 ◽  
Author(s):  
Abdelkarim Ammar
Keyword(s):  
Fuzzy Logic ◽  
Robust Control ◽  
Feedback Linearization ◽  
Control Algorithm ◽  
Direct Torque Control ◽  
Torque Control ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Content Type ◽  
Control Approach

Purpose This paper aims to propose an improved direct torque control (DTC) for the induction motor’s performance enhancement using dual nonlinear techniques. The exact feedback linearization is implemented to create a linear decoupled control. Besides, the fuzzy logic control approach has been inserted to generate the auxiliary control input for the feedback linearization controller. Design/methodology/approach To improve the DTC for induction motor drive, this work suggests the incorporation of two nonlinear approaches. As the classical feedback linearization suffers while the presence of uncertainties and modeling inaccuracy, it is recommended to be associated to another robust control approach to compensate the uncertainties of the model and make a robust control versus the variations of the machine parameters. Therefore, fuzzy logic controllers will be integrated as auxiliary inputs to the feedback linearization control law. Findings The simulation and the experimental validation of the proposed control algorithm show that the association of dual techniques can effectively achieve high dynamic behavior and improve the robustness against parameters variation and external disturbances. Moreover, the space vector modulation is used to preserve a fixed switching frequency, reduce ripples and low switching losses. Practical implications The theoretical, simulation and experimental studies prove that the proposed control algorithm can be used on different AC machines for variable speed drive applications such as oil drilling, traction systems and wind energy conversion systems. Originality/value The proposed DTC strategy has been developed theoretically and realized through simulation and experimental implementation. Different operation conditions have been conducted to check the ability and robustness of the control strategy, such as steady state, speed reversal maneuver, low-speed operation and parameters variation test with load application.

Position Control of Direct-Drive Robot Manipulators with PMAC Motors Using Enhanced Fuzzy PD Control

2004 ◽  
Vol 8 (3) ◽  
pp. 324-331
Author(s):  
Dong Sun ◽  
◽  
Y. X. Su ◽  
James K. Mills ◽  
Keyword(s):  
Position Control ◽  
Robot Manipulators ◽  
Pd Controller ◽  
Control Performance ◽  
Direct Drive ◽  
Control Approach ◽  
Single Link ◽  
Nonlinear Tracking ◽  
Control Methodology ◽  
Fuzzy Pd

A position control approach for direct-drive robot manipulators with permanent magnet AC (PMAC) motors is proposed. The conventional vector control architecture has been simplified by specifying the motor stator phase so that the rotating d-axis current is zero. The position control is designed to be an enhanced fuzzy PD controller, by incorporating two nonlinear tracking differentiators into a conventional fuzzy PD controller. The proposed control methodology is easy to implement, and exhibits better control performance than conventional control methods. Experiments conducted on a single-link manipulator directly driven by a PMAC motor demonstrate the validity of the proposed approach.

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