Tracking Of Wheeled Mobile Robot Research Articles

Trajectory Tracking of Wheeled Mobile Robot Through System Identification and Control Using Deep Neural Network

Trajectory Tracking of Wheeled Mobile Robot Through System Identification and Control Using Deep Neural Network

Observer-based finite-time control for trajectory tracking of wheeled mobile robots with kinematic disturbances

Wheeled Mobile Robots (WMRs) are systems with applications in diverse fields such as transportation, civilian services, military use, and space exploration. Then, their use will continue increasing, making WMRs an essential research topic that deserves further study. To this end, this work presents a novel observer-based finite-time controller for trajectory tracking control of WMRs disturbed by kinematic disturbances. In the proposed approach, the kinematic model of the WMR is transformed into a set of two decoupled second-order systems. Then, the proposed controller is divided into two parts. The first one employs an observer to estimate the effect of the kinematic disturbances. The second part consists of a finite-time controller designed to achieve finite-time convergence of the tracking error. A detailed synthesis procedure theoretically demonstrates the feasibility of the proposed controller. Subsequently, the proposed scheme is compared against finite-time, feedback, and H∞ controllers. Exhaustive numerical simulations show that the proposed new control methodology achieves the trajectory tracking objective despite kinematic disturbances and outperforms the other control procedures. Finally, some comments and numerical results are given to clarify how the proposed control methodology can be used to design new controllers for trajectory tracking in WMRs and demonstrate that the new proposal remains to have a good performance when the system’s coordinates are corrupted by measurement noise.

Path planning and tracking of wheeled mobile robot: using firefly algorithm and kinematic controller combined with sliding mode control

Path planning and tracking of wheeled mobile robot: using firefly algorithm and kinematic controller combined with sliding mode control

Tracking Control of Wheeled Mobile Robot Based on MMPC

In order to solve the problems of low tracking accuracy and large steady-state error in track tracking of wheeled mobile robots, a MPC control scheme combining feedforward and feedback control is designed on the basis of Traditional Model Predictive Control (TMPC). First, when establishing the WMR kinematic error model, the linearization error is retained, and the control input and incremental constraints are considered. Secondly, a feedforward controller is designed for a given desired trajectory of wheeled mobile robot; At the same time, the multi constraint model predictive control (MMPC) strategy with speed optimization is added to design the feedback controller. Then, the stability of the designed MMPC controller is analyzed based on Lyapunov theory. Finally, MMPC control scheme is compared with TMPC control scheme and PID control scheme. The results show that compared with TMPC control scheme, MMPC control scheme reduces the lateral error by 17.87%, the longitudinal error by 17.12%, and the heading angle error by 9.81%; Compared with the PID control scheme, the lateral error is reduced by 20.82%, the longitudinal error is reduced by 23.26%, and the heading angle error is reduced by 5.87%.

Observer-based proportional integral derivative control for trajectory tracking of wheeled mobile robots with kinematic disturbances

This manuscript presents a novel observer-based proportional integral derivative (PID) control methodology for trajectory tracking control of wheeled mobile robots (WMR) disturbed by kinematic disturbances. The new proposal employs the kinematic model of the WMR robot together with a reference system to generate a transformed set of two decoupled and disturbed second-order systems. The control design stage consists in dividing the control signal into two parts. The first one uses an observer to compensate for the effect of the kinematic disturbances, which makes all the disturbances affecting the closed-loop system converge to zero uniformly and asymptotically. Then, the second control part consists of a PID controller designed to achieve asymptotic convergence of the tracking error. We provide a synthesis procedure through rigorous Lyapunov-based analysis, demonstrating that the new control scheme achieves the trajectory tracking control objective. Also, we include a set of numerical simulations to assess the performance of the new controller. Here, we compare our novel methodology against a feedback controller and a finite-time controller. The numerical simulations demonstrate that the proposed control scheme achieves the trajectory tracking objective despite kinematic disturbances and outperforms the other control methodologies with the lowest overshoots and tracking errors.

Neural Network-Based Adaptive Controller for Trajectory Tracking of Wheeled Mobile Robots

Trajectory tracking control is indispensable for a wheeled mobile robot to achieve successful navigation. The classical tracking control systems that are used in wheeled mobile robots do not compensate for the parameter uncertainties and external disturbances. For control strategy, this paper presents a novel hybrid approach, combining a neural network-based kinematic controller and a model reference adaptive control. The controller parameters are adaptively determined online using neural networks. The adaptively tuned kinematic controller ensures a fast convergence to the desired trajectory. The model reference adaptive controller retains the desired tracking performance when parameter and model uncertainties occur. The Lyapunov stability method is used to obtain the adaptive gains which guarantee the asymptotic stability of the error dynamics, where the error is the difference between the outputs of the reference model and the actual plant. The performance of the proposed controller is compared with that of the PID controller, kinematic controller, and adaptive dynamic controller using different performance analysis indices such as integral absolute error, integral squared error, and mean absolute error. Simulation studies demonstrate that the proposed controller achieves high tracking accuracy and fast convergence as compared to the PID, kinematic, and adaptive dynamic controllers considering parameter uncertainties and slip disturbances. The outcomes of the simulation studies also illustrate that the proposed controller achieves the best transient performance. Experiments using real-world tests based on a two-wheeled differential drive robot architecture have elucidated the feasibility of the developed controller regarding tracking accuracy, total control effort, and robustness against uncertainties.

Path Planning and Tracking of Wheeled Mobile Robot: Using Firefly Algorithm and Different Control Architectures

Path Planning and Tracking of Wheeled Mobile Robot: Using Firefly Algorithm and Different Control Architectures

Trajectory tracking of wheeled mobile robots using only Cartesian position measurements

The problem of trajectory tracking of nonholonomic wheeled mobile robots has been extensively studied in the last decades. Nevertheless, most of the control laws proposed in the literature assume full state measurements. Based on the flatness property of the robot’s kinematic model, we proposed a dynamic feedback linearization controller in combination with attitude and velocity observers. The proposed control-observer scheme requires only Cartesian position measurements and achieves asymptotic trajectory tracking. The stability analysis is carried out by means of Lyapunov’s theory and the properties of cascade time-varying systems. Experimental results are presented to show the performance of the proposed approach.

Composite control for trajectory tracking of wheeled mobile robots with NLESO and NTSMC

This paper proposes a control strategy integrating the non-linear extended state observer (NLESO) and the non-singular terminal sliding mode control (NTSMC) for the trajectory tracking of wheeled mobile robots subject to bounded disturbances. A new transformation method of chained model in terms of Lie derivative is presented to simplify the controller design. A specific NLESO combining linear term and non-linear term is designed to estimate the disturbances with a faster convergence performance. A scheme for determining the gain range of NLESO is explicitly given to facilitate the tuning of experimental parameters. Meanwhile, the NTSMC achieves finite time convergence of the tracking error system and the chattering phenomenon in NTSMC is dramatically alleviated with the compensation from NLESO. The experimental results validate the strong robustness and good performance of the proposed control strategy.

Enhancing Tilt-Integral-Derivative Controller to Motion Control of Holonomic Wheeled Mobile Robot by Using New Hybrid Approach

Trajectory tracking of wheeled mobile robot is the most interesting and important subject in robotic systems field. In this paper, a four mecanum wheeled mobile robot (FMWMR) has been considered. This type was considered because it is the most famous holonomic type of wheeled mobile robot (WMR). The main purpose of this work is to design a new hybrid controller for the trajectory tracking of FMWMR based on the kinematic model. The proposed controller consists of Tilt-Integral-Derivative (TID) controller tuned parameters with neural network as well as social spider optimization i.e., (TID-NN-SSO) which is applied on the kinematic model of the FMWMR. The forward and inverse kinematic equations were derived. The TID controller was used for computing the controlled signals which are the angular velocities of each wheel while the neural network (NN) and social spider optimization (SSO) were to compute the parameters of TID controller. MATLAB/Simulink programing was implemented to simulate the results. A comparative study between TID-NN-SSO and TID controller tuned parameters by using particles swarm optimization (TID-PSO) was conducted. The results of the mean square errors (MSE) in (x and y) coordinates as well as the orientation error obtained from TID–NN-SSO are (2.105*10−4 m, 1.025*10−4 m, 0.0815 rad) and they are less than the MSE that was obtained from TID-PSO which are (0.00567 m, 0.0356 m, 1.22 rad/s). This indicates that TID–NN-SSO is more efficient than TID-PSO.

Anti‐sideslip path tracking of wheeled mobile robots based on fuzzy model predictive control

When a wheeled mobile robot is travelling at high longitudinal velocities and turning, it may sideslip. Except for a few researchers, this issue has not received much attention. However, there is a high possibility that sideslip may cause danger, so the authors think this problem needs to be solved urgently. The mechanism of preventing sideslip is simple, which is to reduce the longitudinal velocity of the robot when turning. But, how to slow down is a question worth studying. To this end, they have proposed two schemes, but these two schemes have poor real-time performance and poor robustness to positioning errors, respectively. In order to solve the above problems, considering that the real-time and robustness of fuzzy control are very good, they propose a controller by combining robust control and model predictive control in this Letter. The fuzzy control is used to adjust the longitudinal velocity according to the state of the robot, and the model predictive control is used to achieve path tracking. According to the simulation results, the proposed fuzzy model predictive controller does have small errors, small sideslip, good real-time performance, and good robustness to positioning errors.

Trajectory Tracking of Wheeled Mobile Robots Using Z-Number Based Fuzzy Logic

Being trajectory tracking key for safe mobile robot navigation, Fuzzy Logic (FL) has been useful in tackling uncertainty and imprecision to realize robust and smooth trajectory tracking. In this paper, we present the Z-number based Fuzzy Logic control for trajectory tracking of differential wheeled mobile robots. The unique point of our approach lies in the ability to encode constraint and reliability in multi-input and multi-output rules, whose antecedent universe considers only the instantaneous measurements of distance and the orientation gaps, and whose consequent universe is computed by the interpolative reasoning and the graded mean integration approach. As a consequence, not only our approach avoids the complexity of encoding error gradients, but also is advantageous to model versatile control rules able to cope with missing observations and noisy inducements on actuators. Our experiments using both physics-based simulations and real-world tests based on a Pioneer 3DX robot architecture have elucidated the superior efficacy and the feasibility of the proposed controller regarding accuracy, robustness, and smoothness compared to other well-known related frameworks such as Fuzzy Logic Type 1, Fuzzy Logic Type 2 and Fuzzy Logic with PID. Our results provide unique insights to realize generalizable algorithms aided by FL and Z-number towards robust trajectory tracking.

Adaptive trajectory tracking of wheeled mobile robot based on fixed-time convergence with uncalibrated camera parameters

The problem of fixed-time trajectory tracking control for a wheeled mobile robot with uncalibrated camera parameters is considered. The fixed-time adaptive trajectory tracking control laws are developed. Firstly, the dynamics mode of robot system with unknown parameters is introduced and the tracking error system is given. Secondly, the problem of trajectory tracking is transformed into the problem of fixed-time stabilization for the error system by using the state and input transformations. Then, the fixed-time controller is designed based on nonsingular recursive terminal sliding mode control method, which enables the actual system to track the reference trajectory in fixed time. Finally, the simulation examples verify the validity of the conclusions.

Double Closed-Loop General Type-2 Fuzzy Sliding Model Control for Trajectory Tracking of Wheeled Mobile Robots

In this paper, a double-loop control scheme, combining general type-2 fuzzy logic controller (GT2FLC) and non-singular terminal sliding mode control (NTSMC), is proposed to stabilize the non-holonomic wheeled mobile robot. In the double-loop tracking controller, the outer ring adopts the exponential approach law used to track the position state quickly and the inner ring adopts the double-power approaching law, which makes the attitude convergence faster than the position to ensure the stability of the closed-loop system and deal with the random disturbances. NTSMC is designed for both loops to achieve fast convergence of the system in a finite time. The GT2FLC is used to adjust the gain of the approaching law, so as to enhance the adaptability to random disturbances and weaken the chattering of sliding mode input. The simulation results show that the proposed method can achieve small error and fast tracking of position and attitude under random disturbance, and its performance is better than other controllers.

Adaptive Trajectory Tracking of Wheeled Mobile Robots Based on a Fish-eye Camera

This paper presents a novel adaptive trajectory tracking control method, which can precisely control wheeled mobile robots only using an uncalibrated fish-eye camera fixed on the ceiling. Different from existing approaches, the inertial device, distorted image correction, and the trajectory expression are not required in the control system. The position and orientation of the mobile robot in the camera coordinate system are estimated by the extended POSIT (Pose from Orthography and Scaling with Iteration) algorithm in real-time. Based on estimation results, the controller considering both tracking errors and parameter estimated errors is designed by linear parameterization, where the camera intrinsic parameters are online updated. The asymptotic convergence of the tracking error and the estimated error to zero is proved by the Barbalat lemma. Circular trajectory and irregular trajectory tracking experiments have been conducted to verify the performance of our controller.

Path Tracking of Wheeled Mobile Robots Based on Dynamic Prediction Model

In the field of path tracking control for wheeled mobile robots, researchers generally believe that the motion of robots meets non-holonomic constraints. However, the robot may sideslip by centrifugal force when it is steering. This kind of slip is usually uncontrollable and dangerous. In order to prevent sideslip and improve the performance of path tracking control, we propose a controller based on tire mechanics. Moreover, the new controller is based on the model predictive control, and this control method has proven to be suitable for path tracking of wheeled mobile robots. The proposed controller was verified by simulation and compared with a model predictive controller based on kinematics (non-holonomic constraints). As for the simulation results, the maximum values of displacement error, heading error, lateral velocity, and slip angle of the dynamic-based model predictive controller are 0.2086 m, 0.1609 rad, 0.1546 m/s, and 0.0576 rad, respectively. Compared with the kinematics-based model predictive controller, the maximum values of the above-mentioned parameters of the dynamic-based controller reduce by 86.55%, 72.25%, 96.30%, and 90.02%, respectively. The above-mentioned results show that the proposed controller can effectively improve the accuracy of path tracking control and avoid sideslip.

Design fuzzy neural petri net controller for trajectory tracking control of mobile robot

In this paper, a Fuzzy Neural Petri Net (FNPN) controller has been designed established on Particle Swarm Optimization (PSO) for controlling the path tracking of Wheeled Mobile Robot (WMR). The path planning controller problem has been solved using two FNPN controllers to get the desired velocity and azimuth. The PSO method has used to detection the optimal values parameters of FNPN controllers. The overall models of wheeled mobile robot for path tracking control created on PSO algorithm are implemented in Simulink-Matlab. Simulation outcomes demonstrate the suggested FNPN controllers is more effectiveness and has good dynamic performance than the conventional methods.

Modeling and trajectory tracking control of a two-wheeled mobile robot: Gibbs–Appell and prediction-based approaches

SUMMARYThis study deals with the problem of trajectory tracking of wheeled mobile robots (WMR's) under non-holonomic constraints and in the presence of model uncertainties. To solve this problem, the kinematic and dynamic models of a WMR are first derived by applying the recursive Gibbs–Appell method. Then, new kinematics- and dynamics-based multivariable controllers are analytically developed by using the predictive control approach. The control laws are optimally derived by minimizing a pointwise quadratic cost function for the predicted tracking errors of the WMR. The main feature of the obtained closed-form control laws is that online optimization is not needed for their implementation. The prediction time, as a free parameter in the control laws, makes it possible to achieve a compromise between tracking accuracy and implementable control inputs. Finally, the performance of the proposed controller is compared with that of a sliding mode controller, reported in the literature, through simulations of some trajectory tracking maneuvers.

Optimal Trajectory Tracking Control for a Wheeled Mobile Robot Using Fractional Order PID Controller

This paper present an optimal Fractional Order PID (FOPID) controller based on Particle Swarm Optimization (PSO) for controlling the trajectory tracking of Wheeled Mobile Robot(WMR).The issue of trajectory tracking with given a desired reference velocity is minimized to get the distance and deviation angle equal to zero, to realize the objective of trajectory tracking a two FOPID controllers are used for velocity control and azimuth control to implement the trajectory tracking control. A path planning and path tracking methodologies are used to give different desired tracking trajectories. PSO algorithm is using to find the optimal parameters of FOPID controllers. The kinematic and dynamic models of wheeled mobile robot for desired trajectory tracking with PSO algorithm are simulated in Simulink-Matlab. Simulation results show that the optimal FOPID controllers are more effective and has better dynamic performance than the conventional methods.

An Adaptive Unscented Kalman Filter-based Controller for Simultaneous Obstacle Avoidance and Tracking of Wheeled Mobile Robots with Unknown Slipping Parameters

A novel unified control approach is proposed to simultaneously solve tracking and obstacle avoidance problems of a wheeled mobile robot (WMR) with unknown wheeled slipping. The longitudinal and lateral slipping are processed as three time-varying parameters and an Adaptive Unscented Kalman Filter (AUKF) is designed to estimate the slipping parameters online More specifically, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the Unscented Kalman Filter (UKF) context. A stable unified controller is applied to simultaneously handle tracking and obstacle avoidance for this WMR system to compensate for the unknown slipping effect. Applying Lyapunov stability theory, it is proved that tracking errors of the closed-loop system are asymptotically convergent regardless of unknown slipping, the tracking errors converge to the zero outside the obstacle detection region and obstacle avoidance is guaranteed inside the obstacle detection region. The effectiveness and robustness of the proposed control method are validated through simulation and experimental results.