Control Of Wheeled Mobile Robot Research Articles

Design and Study of an Adaptive Fuzzy Logic-Based Controller for Wheeled Mobile Robots Implemented in the Leader-Follower Formation Approach

This paper presents a new design of an adaptive fuzzy logic control by implementing the leader-follower approach. The principle is to modify the feedback control of non-holonomic Wheeled Mobile Robot (WMR) to be adaptive according to a fuzzy controller in the control loop, in order to adjust the feedback control gains according to the distance error between the reference path and the real position. The trajectory tracking control for a single WMR is extended to the formation control for two WMRs in which the first one is the leader and the second is the follower. Simulation results are presented to demonstrate the effectiveness of the proposed controller.

Development of an Adaptive Trajectory Tracking Control of Wheeled Mobile Robot

Classical modeling and control methods applied to differential locomotion mobile robots generate mathematical equations that approximate the dynamics of the system and work relatively well when the system is linear in a specific range. However, they may have low accuracy when there are many variations of the dynamics over time or disturbances occur. To solve this problem, we used a recursive least squares (RLS) method that uses a discrete-time structure first-order autoregressive model with exogenous variable (ARX). We design and modify PID adaptive self-adjusting controllers in phase margin and pole allocation. The main contribution of this methodology is that it allows the permanent and online update of the robot model and the parameters of the adaptive self-adjusting PID controllers. In addition, a Lyapunov stability analysis technique was implemented for path and trajectory tracking control, this makes the errors generated in the positioning and orientation of the robot when performing a given task tend asymptotically to zero. The performance of the PID adaptive self-adjusting controllers is measured through the implementation of the criteria of the integral of the error, which allows to determine the controller of best performance, being in this case, the PID adaptive self-adjusting type in pole assignment, allowing the mobile robot greater precision in tracking the trajectories and paths assigned, as well as less mechanical and energy wear, due to its smooth and precise movements.

Robust forward\\backward control of wheeled mobile robots

Obtaining a control algorithm capable of navigating the system both in forward and backward motions is one of the control objectives for tractor-trailer wheeled robots (TTWRs). In this paper, a relatively general structure is presented for both forward and backward control of an n-trailer wheeled mobile robot (NTWMR) in the presence of wheel slip effects. To keep better overall performance and track the reference trajectories in forward and backward motions, the NTWMR is intended to be controlled in the presence of slip effects. A control algorithm accompanied by a slip compensation procedure is proposed for the system simultaneously. First, the mathematical model of the system in the presence of slip effects is obtained. A novel physically motivated algorithm is proposed for the tracking control in the presence of unknown uncertainties (longitudinal and lateral slips) for both forward and backward motions. By estimating the slip effects at any instant, the control inputs are produced to compensate for their destructive effects on tracking control of the NTWMR. Then the stability of the closed-loop system is evaluated using the Lyapunov theory. The potential of the proposed controller was verified through several case studies, including comparative results and experimental validation in various motion control manoeuvers for a vehicle with trailers. The proposed method is the first algorithm that can cover a broad range of TTWR motion tasks (forward and backward trajectory tracking, slip attenuation, and global stability), which are required to be developed in NTWMRs.

Robust zeroing neural network for fixed-time kinematic control of wheeled mobile robot in noise-polluted environment

Based on a new robust zeroing neural network (RZNN) model, the trajectory tracking control of a wheeled mobile robot (WMR) within fixed-time in noise-polluted environment is presented in this paper. Unlike most of the previous reported works, the RZNN model approach for trajectory tracking control of the WMR reaches fixed-time convergence and noise suppression simultaneously. Besides, detailed theoretical analysis of its convergence and robustness are provided. Numerical simulation verification is also provided to demonstrate the superior robustness and accurateness of the RZNN model approach for trajectory tracking control of the WMR in noise-polluted environment. Both of the theoretical analysis and numerical simulation results verify the effectiveness and robustness of the RZNN model approach.

Mapless LiDAR Navigation Control of Wheeled Mobile Robots Based on Deep Imitation Learning

This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method for wheeled mobile robots based on deep imitation learning. The proposed method is a data-driven control method that directly uses LiDAR sensors and relative target position for mobile robot navigation control. A deep convolutional neural network (CNN) model is proposed to predict motion control commands of the mobile robot without the requirement of the global map to achieve navigation control of the mobile robot in unknown environments. While collecting the training dataset, we manipulated the mobile robot to avoid obstacles through manual control and recorded the raw data of the LiDAR sensor, the relative target position, and the corresponding motion control commands. Next, we applied a data augmentation method on the recorded samples to increase the number of training samples in the dataset. In the network model design, the proposed CNN model consists of a LiDAR CNN module to extract LiDAR features and a motion prediction module to predict the motion behavior of the robot. In the model training phase, the proposed CNN model learns the mapping between the input sensor data and the desired motion behavior through end-to-end imitation learning. Experimental results show that the proposed mapless LiDAR navigation control method can safely navigate the mobile robot in four unseen environments with an average success rate of 75%. Therefore, the proposed mapless LiDAR navigation control system is effective for robot navigation control in an unknown environment without the global map.

Nonholonomic Wheeled Mobile Robot Trajectory Tracking Control Based on Improved Sliding Mode Variable Structure

Several research studies are conducted based on the control of wheeled mobile robots. Nonholonomy constraints associated with wheeled mobile robots have encouraged the development of highly nonlinear control techniques. Nonholonomic wheeled mobile robot systems might be exposed to numerous payloads as per the application requirements. This can affect statically or dynamically the complete system mass, inertia, the location of the center of mass, and additional hardware constraints. Due to the nonholonomic and motion limited properties of wheeled mobile robots, the precision of trajectory tracking control is poor. The nonholonomic wheeled mobile robot tracking system is therefore being explored. The kinematic model and sliding mode control model are analyzed, and the trajectory tracking control of the robot is carried out using an enhanced variable structure based on sliding mode. The shear and sliding mode controls are designed, and the control stability is reviewed to control the trajectory of a nonholonomic wheeled mobile robot. The simulation outcomes show that the projected trajectory track control technique is able to improve the mobile robot’s control, the error of a pose is small, and the linear velocity and angular speed can be controlled. Take the linear and angular velocity as the predicted trajectory.

Adaptive Visually Servoed Tracking Control for Wheeled Mobile Robot with Uncertain Model Parameters in Complex Environment

This paper investigates the stabilization and trajectory tracking problem of wheeled mobile robot with a ceiling-mounted camera in complex environment. First, an adaptive visual servoing controller is proposed based on the uncalibrated kinematic model due to the complex operation environment. Then, an adaptive controller is derived to provide a solution of uncertain dynamic control for a wheeled mobile robot subject to parametric uncertainties. Furthermore, the proposed controllers can be applied to a more general situation where the parallelism requirement between the image plane and operation plane is no more needed. The overparameterization of regressor matrices is avoided by exploring the structure of the camera-robot system, and thus, the computational complexity of the controller can be simplified. The Lyapunov method is employed to testify the stability of a closed-loop system. Finally, simulation results are presented to demonstrate the performance of the suggested control.

Design of a controller for wheeled mobile robots based on automatic movement sequencing

There are many kinds of robots and among them the wheeled mobile robots (WMR) stand out, because they are relatively cheap and easy to build. These features make WMRs the test prototypes for control strategies or motion generation. In general, the controllers developed are based on sensory schemes that give an WMR the ability to travel through flat or obstructed environments. However, these strategies are highly reactive, i.e. they are based on the control-action scheme and are not adaptive; or, they are motion schemes built from simulations that assume the environmental conditions to determine the robot's path. In both cases, WMRs do not adapt perfectly to the change of environment, since the controller does not find appropriate movements for the robot to move from one point to another. Therefore, this article proposesapartial solution to this problem, with a controller that generates sets of adaptive movements for an WMR to travel around its environment from the sensory perception information.

A state augmented adaptive backstepping control of wheeled mobile robots

The present paper aims to design an integrated kinematic/dynamic-based tracking controller for wheeled mobile robots (WMRs) considering motors’ dynamics. By defining a reference WMR, the role of kinematic controller is to not only minimize the posture error which indicates the difference between the reference and actual WMRs, but also to generate a desired path for the actual WMR. The kinematic tracking control problem of WMRs is so challenging if motors’ dynamics, parametric and nonparametric uncertainties and external disturbances are considered. Thus, proposing a dynamic control law alongside a kinematic control is unavoidable. In this study, we propose a new dynamic controller, namely, a state augmented adaptive backstepping such that the desired path is asymptotically tracked. Several numerical results accompanied by 3D simulations of trajectory tracking control of a WMR in ‘Simscape Multibody’ environment and comparisons with two well-designed controllers in the literature are reported to show the high performance of proposed control structure.

Vision-Based Formation Control of Mobile Robots With FOV Constraints and Unknown Feature Depth

In this the problem of leader-following tracking control of wheeled mobile robots with implicit communication through onboard visual sensing is addressed. New image-based visual servoing (IBVS) control schemes are proposed to ensure prescribed performance and the field-of-view (FOV) constraints in the absence of the feature depth information. First, the error transformation with predefined performance specifications is introduced for handling the visibility constraints. Then, in the setting of control of the uncertain system with unknown control coefficients, a Nussbaum gain adaptive controller and a static nonlinear gain controller are proposed without the need of feature depth information. Finally, both numerical simulations and physical experiments are presented to demonstrate the applicability and performance of the proposed control schemes.

Trajectory tracking control for wheeled mobile robots based on nonlinear disturbance observer with extended Kalman filter

This article tackles the trajectory tracking problem for a non-holonomic wheeled mobile robot (WMR) with non-random and random disturbances. A nonlinear disturbance observer with extended Kalman filter (NDEKF) is designed to observe the velocity and the non-random disturbance of the WMR. An error feedback controller and a kinematic controller are proposed to achieve the disturbance compensation and perfect position tracking. The mean square exponential boundedness for the estimation error of NDEKF is presented. Applying the Lyapunov stability theory, it is proved that the velocity and position tracking error of the double closed-loop system are uniformly ultimately asymptotically stable. Finally, numerical simulations demonstrate the validity of the presented methodology.

Definition and Application of Variable Resistance Coefficient for Wheeled Mobile Robots on Deformable Terrain

Resistance coefficient ( RC ) is an important measure when designing wheel-driving mechanisms and accurate dynamic models for real-time mobility control of wheeled mobile robots (WMRs). This measure is typically formulated as a constant that depends on the wheel load, wheel dimensions, and soil that the WMR is designed for. This article proposes a novel variable RC that responds to terrain deformation. This variable RC is then applied to controllers for WMRs that estimate driving torques and slip ratios on deformable terrain. Simple yet accurate models of RC are developed from both experimental results and theoretical analysis, and these models are then compared with other methods. The proposed RC models give more accurate and more computationally efficient estimations of driving torques and slip ratios for WMRs, with average estimation errors less than 6% and the shortest computation time in experiments. The two proposed estimators are then applied to the design of the tracking-control systems for a WMR running on deformable terrain. Experiments with simulated sandy terrain demonstrate that both proposed control systems are feasible, and the slip estimation effectively decreases velocity tracking errors from more than 20% to less than 10%.

Adaptive NN-based finite-time tracking control for wheeled mobile robots with time-varying full state constraints

An adaptive neural network-based finite-time tracking control method is proposed for wheeled mobile robots (WMR) in the presence of the slipping with full state time-varying constraints. The practical WMR model with coupled inputs as two torques for each wheel is difficult for the designed controllers to achieve finite-time convergence. A novel adaptive finite-time tracking control strategy is proposed by satisfying the time-varying constraints and utilizing barrier Lyapunov functions. It is proved that the controllers we designed are finite-time stable, and each tracking error converges to a small neighborhood of the original states in a finite time without violation of the full state constraints. Finally, a simulation based on a practical WMR model is conducted to verify the effectiveness of the proposed tracking control method. By using the exponential-decayed time-varying constraints, the simulation results show that the tracking control effect is very optimistic, which means that the control method proposed in this paper has great significance in engineering.

A Trajectory Tracking Method for Wheeled Mobile Robots Based on Disturbance Observer

In this article, an adaptive tracking control method is proposed for wheeled mobile robots (WMR). In the trajectory tracking control of WMR, skidding and slipping are taken into account, and the slip degree parameters are introduced. The disturbance observer is designed to estimate the total disturbances. Then, we design the adaptive controller to guarantee that the tracking errors asymptotically converge to zero, the stability of the system is proved by Lyapunov theory. Finally, the simulation results are provided to demonstrate the effectiveness of the proposed scheme.

Approximate dynamic programming in robust tracking control of wheeled mobile robot

Approximate dynamic programming in robust tracking control of wheeled mobile robot

Practical Implementation of Passivity-Based Robust Switching Posture Control of Wheeled Mobile Robots With Model Uncertainty

Our previous study presented the theoretical work for a passivity-based robust switching posture control of wheeled mobile robots (WMRs) with model uncertainty and performed the numerical simulations to show its effectiveness. In this regard, this paper validated the proposed technique based on actually fabricated WMR under the various initial conditions. The actual test results have proven that the posture stabilization of the WMR can be successfully accomplished via the proposed control strategy. In addition, it is found that the action of robust model uncertainty compensation reduces the time of the entire stabilization process and guarantees the strict decrease of navigation energy. This work will become a valuable asset for those who wish to practically implement the posture stabilization of WMR based on the passivity-based robust switching control laws totally designed by the 2nd order Largrangian dynamics instead of the 1st order kinematics based approach.

Consensus Control for Wheeled Mobile Robots Under Input Saturation Constraint

In this article, the consensus control problem for the nonholonomic wheeled mobile robots under the input saturation constraint is addressed. The specified-time observer is designed to estimate the root node’s linear speed and angular speed by only using the configuration of the robot, and the convergence time when the observer obtains the real values can be specified in advance according to the specific task requirements. Based on the observed velocity states, the consensus controller subjects to input saturation is constructed to steer a wheeled mobile robot to keep track of the root node. Last, to verify the feasibility and effectiveness of the proposed control strategy, both the numerical simulation and physical experiment results are conducted.

Active Disturbance Rejection Control for Wheeled Mobile Robots with Parametric Uncertainties

In this paper, an efficient controller design method is proposed based on active disturbance rejection control (ADRC) scheme for stabilization problem of wheeled mobile robots with parametric uncertainties, which can make the system converge quickly. By using the extended state observer (ESO), both the system states and the unknown parametric uncertainties could be estimated. In addition, the input-state scaling technique is used to transform the system into two decoupled subsystems. Based on the decoupled subsystems, a switching controller and ADRC are designed. Simulation results show that the proposed scheme can stabilize the wheeled mobile robot system asymptotically despite the presence of parametric uncertainties.

Adaptive Fuzzy Output Feedback Simultaneous Posture Stabilization and Tracking Control of Wheeled Mobile Robots With Kinematic and Dynamic Disturbances

This article proposes a simultaneous posture (i.e., position and heading direction angle) stabilization and tracking control for differential-drive wheeled mobile robots (WMRs) using an adaptive fuzzy output feedback tracking control (AFOFTC) method and a fuzzy basis function network (FBFN). Existing studies on simultaneous posture stabilization and tracking control of WMRs do not compensate for the uncertainties and disturbances caused by kinematics and dynamics (i.e., kinematic and dynamic disturbances). To inhibit the performance degradation caused by these disturbances, an FBFN-based adaptive fuzzy output feedback posture stabilization and tracking control of WMRs is proposed considering the unavailable velocities of the WMR. In this approach, i) kinematic error equations are derived in polar coordinates, ii) a nonlinear velocity observer for WMRs containing the kinematic and dynamic uncertainties is designed, iii) a backstepping-like feedback linearization controller is designed based on the kinematics in polar coordinates to generate a reference trajectory, and iv) an AFOFTC law is proposed based on the dynamics to ensure that the WMR follows the generated reference trajectory. The proposed method can maintain satisfactory posture stabilization and tracking control performance despite the kinematic and dynamic disturbances. A stability analysis as well as the simulation and experimental results are provided to validate the proposed scheme.

Adaptive Sliding Mode Based Disturbance Attenuation Tracking Control for Wheeled Mobile Robots

This paper is devoted to investigating a composite controller for wheeled mobile robots in the presence of external disturbance and parametric uncertainty. Unlike the traditional backstepping technique existing the impractical velocity jumps, the proposed neural dynamic model has the ability to generate smooth continuous signals. Subsequently, a disturbance observer based adaptive sliding mode dynamic controller is introduced to estimate disturbances online, adjust control gain automatically and eliminate chattering phenomena completely. Under the developed control law, the ultimate boundedness of all signals is guaranteed and the tracking errors can be arbitrarily small in finite time. Simulation results are carried out to demonstrate the effectiveness of the proposed scheme.