scholarly journals The Impact of the Dynamic Model in Feedback Linearization Trajectory Tracking of a Mobile Robot

2021 ◽  
Author(s):  
Welid Benchouche ◽  
Rabah Mellah ◽  
Mohammed Salah Bennouna
Keyword(s):  
Dynamic Model ◽  
Trajectory Tracking ◽  
Feedback Linearization ◽  
Position Control ◽  
Tracking Error ◽  
Kinematic Model ◽  
Control Approach ◽  
Output State ◽  
Lyapunov Stability Analysis ◽  
The Impact

This paper proposes the impact of the Dynamic model in Input-Output State Feedback Linearization (IO-SFL) technique for trajectory tracking of differential drive mobile robots, which has been restricted to using just the kinematics in most of the previous approaches. To simplify the control problem, this paper develops a novel control approach based on the velocity and position control strategy. To improve the results, the dynamics are taken into account. The objective of this paper is to illustrate the flaws unseen when adopting the kinematics-only controllers because the nonlinear kinematic model will suffice for control design only when the inner velocity (dynamic) loop is faster than the slower outer control loop. This is a big concern when using kinematic controllers to robots that don’t have a low-level controller, Arduino robots for example. The control approach is verified using the Lyapunov stability analysis. MATLAB/SIMULINK is carried out to determine the impact of the proposed controller for the trajectory tracking problem, from the simulation, it was discovered that the proposed controller has an excellent dynamic characteristic, simple, rapid response, stable capability for trajectory-tracking, and ignorable tracking error. A comparison between the presence and absence of the dynamic model shows the error in tracking due to dynamic system that must be taken into account if our system doesn’t come with a built-in one, thus, confirming the superiority of the proposed approach in terms of precision, with a neglectable difference in computations.

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.

scholarly journals Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
J. Humberto Pérez-Cruz ◽  
José de Jesús Rubio ◽  
Rodrigo Encinas ◽  
Ricardo Balcazar
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Feedback Linearization ◽  
Neural Control ◽  
Tracking Error ◽  
Uncertain System ◽  
Reference Trajectory ◽  
System A ◽  
Multiple Input ◽  
Feedback Linearization Control

The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed.

scholarly journals Feedback Linearization Based Robust Control for Linear Permanent Magnet Synchronous Motors

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5242
Author(s):  
Yung-Te Chen ◽  
Chi-Shan Yu ◽  
Ping-Nan Chen
Keyword(s):  
Robust Control ◽  
Permanent Magnet ◽  
Feedback Linearization ◽  
Position Control ◽  
Feedback Controller ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Linear Motors ◽  
Highly Nonlinear ◽  
The Impact

In this study, we designed a feedback linearization control strategy for linear permanent magnet synchronous motors (LPMSMs) as well as a robust control mechanism. First, the highly nonlinear system was transformed into an exact linear system by the feedback linearization technique. Then, we designed a robust controller to mitigate the impact of system parameter disturbances on system performance. This novel robust feedback controller can be applied to electromagnetic force, speed and position control loops in linear motors, correct the errors created by uncertainty factors in the entire system in real time, and set the system’s settling time based on the application environment of the plant. Finally, we performed simulations and experiments using a PC-based motor control system, which demonstrated that the proposed robust feedback controller can achieve good performance in the controlled system with robust anti-disturbance control.

Three-Degree-of-Freedom Dynamic Model Based IT2RFNN Control for Gantry Position Stage

2013 ◽  
Vol 416-417 ◽  
pp. 554-558
Author(s):  
Po Huan Chou ◽  
Faa Jeng Lin ◽  
Chin Sheng Chen ◽  
Feng Chi Lee
Keyword(s):  
Control System ◽  
Dynamic Model ◽  
Adaptive Learning ◽  
Tracking Error ◽  
Degree Of Freedom ◽  
Mechanical Coupling ◽  
Control Approach ◽  
Model Based ◽  
Interval Type ◽  
Three Degree Of Freedom

A three-degree-of-freedom (3-DOF) dynamic model based interval type-2 recurrent fuzzy neural network (IT2RFNN) control system is proposed in this study for a gantry position stage. To consider the effect of inter-axis mechanical coupling, a Lagrangian equation based 3-DOF dynamic model for gantry position stage is derived first. Then, to minimize the synchronous error and tracking error of the gantry position stage, the 3-DOF dynamic model based IT2RFNN control system is proposed. In this approach, the adaptive learning algorithms of the IT2RFNN on-line are derived from the Lyapunov stability theorem. Finally, some experimental results of optical inspection application are illustrated to show the validity of the proposed control approach.

scholarly journals Effect of Servo Valve Dynamic on Precise Position Control of a Pneumatic Servo Table

2008 ◽  
Vol 2 (1) ◽  
pp. 43-48 ◽  
Author(s):  
Toshinori Fujita ◽  
◽  
Kenji Kawashima ◽  
Takashi Miyajima ◽  
Taro Ogiso ◽  
...  
Keyword(s):  
Natural Frequency ◽  
Trajectory Tracking ◽  
Position Control ◽  
Tracking Error ◽  
Air Bearing ◽  
Precise Position ◽  
Servo Valve ◽  
Valve Dynamics ◽  
Feedforward Controller ◽  
Tracking Response

In experimentally investigating the effect of servo valve dynamics on control of a pneumatic servo table with an air bearing, we propose control using a PDD2 and a feedforward controller, then test step and trajectory tracking response with natural servo valve frequencies. We confirmed that pneumatic servo performance is affected by servo valve dynamics. We found that tracking error fell below +- 0.5 mm when the servo valve has a natural frequency of 300 Hz.

Nonlinear Decentralized Position Control of Electrohydraulic Actuators in Road Simulation

2005 ◽  
Author(s):  
Beshahwired Ayalew ◽  
Bohdan T. Kulakowski
Keyword(s):  
Feedback Linearization ◽  
Position Control ◽  
Tracking Error ◽  
Time Domain Analysis ◽  
Position Tracking ◽  
Vertical Acceleration ◽  
Test Vehicle ◽  
The Road ◽  
Error Metric ◽  
Detailed Computer

In this paper, a nonlinear position tracking controller is derived based on feedback linearization to globally linearize the nonlinear dynamics of an electrohydraulic actuator with nonlinear state feedback. A detailed computer model is developed for a four-post road simulation system with a transit bus as the test vehicle. Using this model, comparisons are conducted between the proposed nonlinear decentralized controller and a traditional linear decentralized controller. Previously introduced interaction measures suitable for time domain analysis of nonlinear systems confirm that, for the test vehicle considered, load plate position loop interactions are quickly eliminated by either the linear or nonlinear decentralized position controllers. The performance of the road simulator as gauged by a position tracking error metric for a typical rough road profile is improved by over 60% across all actuators and response matching of sprung mass vertical acceleration PSD is likewise improved by over 50% when using the nonlinear decentralized controller.

Adaptive and Robust Control of an Unmanned Surface Vessel

2013 ◽  
Author(s):  
Dongbin Lee
Keyword(s):  
Dynamic Model ◽  
Feedback Linearization ◽  
Sliding Mode ◽  
Mathematical Proof ◽  
Tracking System ◽  
Parametric Uncertainty ◽  
Adaptive Sliding Mode Control ◽  
Control Approach ◽  
Unmanned Surface Vessel ◽  
Surface Vessel

This paper presents an adaptive sliding mode control structure of underactuated unmanned surface vessel systems under parametric uncertainty. The primary motivation in this research is to compensate for disturbances related to the added hydrodynamic forces and moment in the nonlinear control of a three degree-of-freedom marine vessel. The novelty of this work is the tracking robustness and the compensation for uncertainties common to surface vessels. The first work is to divide the dynamic model of the system into the ship rigid-body terms and added terms induced by hydrodynamics. A sliding-mode controller is designed to force the error trajectory into the sliding surface, which produces a robust tracking result in a finite time. For the parametric uncertainties in the dynamic model, an adaptive controller is designed to compensate using a projection-based adaptation law. After combining these two control schemes, a closed-loop controller designed by a Lyapunov-based control approach over feedback linearization is appropriately designed to yield the nonlinear tracking system bounded in the presence of uncertainties. The mathematical proof shows that a stable tracking result in the sense of Lyapunov-type stability is achieved. Numerical simulation results are shown to demonstrate the validity of these proposed controllers.

scholarly journals Inverse Kinematic Control of a Delta Robot Using Neural Networks in Real-Time

Robotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 115
Author(s):  
Akram Gholami ◽  
Taymaz Homayouni ◽  
Reza Ehsani ◽  
Jian-Qiao Sun
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Kinematic Model ◽  
Inverse Kinematic ◽  
Robot Kinematics ◽  
Kinematic Control ◽  
Modeling Uncertainties ◽  
Delta Robot

This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot.

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