Control Of Wheeled Mobile Robot Research Articles

A practical leader–follower hybrid control scheme for wheeled mobile robots

This paper addresses the problem of tracking control of wheeled mobile robots in leader–follower formation. By applying the backstepping technique, a hybrid control scheme consisting of continuous control and intermittent control is proposed, and leader–follower formation tracking is achieved in the presence of model uncertainty and external disturbance. A relationship between the intermittent control width and control gain and the convergence of the position errors is obtained for the tracking accuracy estimation. As a special case, the concerned intermittent control can be converted to impulsive control with a sufficiently small control width, and tracking of the trajectory can be realized by directly driving the state variables of the follower at well-designed discrete instants. The main novelty of the proposed hybrid control scheme is to drive the tracking errors into a preset region via intermittent control (or impulsive control) signals and then achieve the tracking goal in the practical sense via continuous control signals. An example with the corresponding numerical simulations and experimental tests is given to illustrate the effectiveness of the proposed method.

Backstepping and Novel Sliding Mode Trajectory Tracking Controller for Wheeled Mobile Robots

A novel variable structure controller based on sliding mode is developed for addressing the trajectory tracking challenge encountered by wheeled mobile robots. Firstly, the trajectory tracking error model under the global coordinate system is established according to the kinematic model of the wheeled mobile robot. Secondly, the novel sliding mode algorithm and backstepping method are introduced to design the motion controller of the system, respectively. Different sliding mode surfaces are formulated to guarantee rapid and stable convergence of the system’s trajectory tracking error to zero. Ultimately, comparative simulation trials validate the controller’s ability to swiftly and consistently follow the reference trajectory. In contrast to traditional controllers, this controller shows rapid convergence, minimal error, and robustness.

Robust Tracking Control of Wheeled Mobile Robot Based on Differential Flatness and Sliding Active Disturbance Rejection Control: Simulations and Experiments.

This paper proposes a robust tracking control method for wheeled mobile robot (WMR) against uncertainties, including wind disturbances and slipping. Through the application of the differential flatness methodology, the under-actuated WMR model is transformed into a linear canonical form, simplifying the design of a stabilizing feedback controller. To handle uncertainties from wheel slip and wind disturbances, the proposed feedback controller uses sliding mode control (SMC). However, increased uncertainties lead to chattering in the SMC approach due to higher control inputs. To mitigate this, a boundary layer around the switching surface is introduced, implementing a continuous control law to reduce chattering. Although increasing the boundary layer thickness reduces chattering, it may compromise the robustness achieved by SMC. To address this challenge, an active disturbance rejection control (ADRC) is integrated with boundary layer sliding mode control. ADRC estimates lumped uncertainties via an extended state observer and eliminates them within the feedback loop. This combined feedback control method aims to achieve practical control and robust tracking performance. Stability properties of the closed-loop system are established using the Lyapunov theory. Finally, simulations and experimental results are conducted to compare and evaluate the efficiency of the proposed robust tracking controller against other existing control methods.

Learning model predictive controller for wheeled mobile robot with less time delay

The weak spots have been examined, a solution has been suggested, the solution has been applied, and a comparison between the simulation and experimental test results has been given in this work. In order to do this, a nonlinear model predictive controller and a linear quadratic regulator controller have been utilized to control a four-wheel mobile robot during modeling and implementation. But the combination of these classic and modern controllers with machine learning can greatly help to make these controllers work more accurately; As a result, in order to increase the accuracy of the performance of these controllers, by training neural networks of multilayer perceptrons, the controllers have been made intelligent. Controllers with cost function have coefficients as weighting to the matrix of system state variables and control input, which are greatly affected by changing these two weighting matrices of problem solving and optimization. For this reason, it is necessary to extract these two matrices for each separate path in order to improve the performance of the controller by trial and error. But by applying the proposed network which is trained with a new algorithm, not only the performance accuracy has increased, but the network extracts these two matrices without the need to spend human energy. Additionally, by training additional neural networks to optimally extract the benefit of the forecast horizon, fewer calculations and faster solution times have been achieved, helping to reduce the current time delays, particularly in the implementation of the nonlinear controller on the robot in the experimental mode. In the hardware section, the solution time has been shortened by looking at and utilizing operators like the U2D2 interface and the pixy camera, which are quicker than the standard procedure. The information indicates that the proposed intelligence technology reacts 40%–50% quicker than the NMPC standard procedure. The suggested algorithm has also minimized the path tracking error since it extracts the optimal gain at each stage. Moreover, a hardware mode solving speed comparison between the recommended and conventional methods is given. In this domain, a 33% increase in solving speed has been noted.

Enhanced extended state observer based prescribed time tracking control of wheeled mobile robot with slipping and skidding

Enhanced extended state observer based prescribed time tracking control of wheeled mobile robot with slipping and skidding

A virtual reference trajectory scheme for tracking control of wheeled mobile robots with slip disturbances

The problem of trajectory tracking control for wheeled mobile robots with slip disturbances is investigated in this paper. A virtual reference trajectory scheme is proposed based on disturbance compensations. Different from the conventional design philosophy of adding disturbance compensations to the controller, the proposed scheme integrates slip disturbance estimates into the reference trajectory. The proposed scheme consists of two parts. First, the slip disturbance estimates and the reference trajectory are fused to generate a virtual reference trajectory, where the slip disturbances are estimated using a disturbance approximation method. Second, a controller is designed to enable the wheeled mobile robot’s odometry position and yaw angle track the virtual reference trajectory, and then the actual position of the wheeled mobile robot track the position of the real reference trajectory. Comparison simulations and experiments are provided to illustrate the validity and advantages of the proposed scheme.

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.

Observer-based fuzzy trajectory-tracking controller for wheeled mobile robots with kinematic disturbances

Wheeled mobile robots (WMRs) have become essential in civilian and industrial applications. Making the WMR accomplish a trajectory-tracking task is essential for several purposes, including autonomous navigation and transportation. Nevertheless, the performance of the closed-loop system may be affected due to unavoidable disturbances. Hence, this manuscript develops a new observer-based fuzzy logic controller that accomplishes the trajectory-tracking task despite disturbances. The controller is split into two elements. The first one consists of an observer compensating for matched disturbances. The second control part employs the fuzzy logic theory to develop a new controller, making the WMR follow a desired reference. Via the Lyapunov formalism, it is theoretically demonstrated that all the tracking errors converge to zero asymptotically. The new control scheme is compared against a proportional integral derivative controller, a feedback methodology, and a finite-time controller that employs a sliding mode controller combined with the backstepping technique. A detailed numerical analysis proves that the novel controller exhibits the lowest tracking errors when the system is affected by kinematic disturbances and noisy measurements, demonstrating its efficiency in a real scenario.

Trajectory tracking control of wheeled mobile robots with skidding and time-varying delay

This paper tackles the problem of trajectory tracking for wheeled mobile robots subject to time-varying input delay and bounded external disturbances. First, we establish a dynamic model for a wheeled mobile robot under sliding and skidding conditions, and determine the maximum allowable input delay that maintains system stability using Razumikhin-type stability analysis without prior knowledge of the variation in delay. The proposed adaptive robust controller combined with super-twisting sliding mode control is resilient to disturbances such as input delay, system uncertainty, and parameter variation. The proposed adaptive law enables real-time modification of switching gain based on tracking error without predefined knowledge of uncertainty bounds. Compared with traditional sliding mode control strategies, the super-twisting algorithm can eliminate chattering phenomenon while combined robust methods further reduce modeling uncertainties’ influence on system performance. Finally, we select an appropriate Lyapunov function to analyze and prove uniformly ultimately bounded of the closed-loop system. MATLAB simulation comparison results demonstrate that this approach achieves high tracking accuracy, faster response speed, and robustness.

Adaptive Image-Based Moving-Target Tracking Control of Wheeled Mobile Robots With Visibility Maintenance and Obstacle Avoidance

Adaptive Image-Based Moving-Target Tracking Control of Wheeled Mobile Robots With Visibility Maintenance and Obstacle Avoidance

Tracking control of wheeled mobile robots via intermittent control.

This paper was concerned with the trajectory tracking control of wheeled mobile robots using aperiodic intermittent control. By establishing the corresponding motion model of the wheeled mobile robot, a tracking control strategy was proposed based on the intermittent control approach and backstepping method. Compared to the controllers using continuous state feedback, the proposed control strategy was activated only on separate time intervals, which combined the features of closed- and open-loop control. An example was given to illustrate the effectiveness of the obtained result.

A novel saturated PID‐type observer‐based controller for wheeled mobile robots with a guaranteed performance considering path curvature

AbstractThis article proposes a novel saturated PID‐type formation controller for a class of nonholonomic wheeled mobile robots (WMRs) of type (m, s) with mobility m and steerability s. Since the path curvature causes a severe deviation of the followers from the leaders' trajectory in the corners, a new definition of output equations is suggested to construct the coordinates of a virtual reference point in the front of each follower based on the concept of the extended look‐ahead control that utilizes a corrective angle to compensate the trajectory curvature efficiently. A nonlinear transformation and the robot's kinematic and dynamic equations are heuristically combined to develop a novel second‐order Euler–Lagrange formulation of unconstrained errors from constrained errors. Then, a novel saturated PID‐type controller is proposed for WMRs to build a desirable formation with a prescribed performance. An innovative nonlinear observer is presented to estimate followers' velocities. An efficient mixture of a radial basis function neural network (RBFNN) and an adaptive mechanism is proposed to compensate for model uncertainties, external disturbances, and network approximation errors. Lyapunov's direct method verifies semi‐global uniform ultimate boundedness stability by estimating a gain‐dependent region of attraction. Numerical simulations finally show the efficiency of the proposed controller.

Event-Triggered Model-Free Adaptive Control for Wheeled Mobile Robot With Time Delay and External Disturbance Based on Discrete-Time Extended State Observer

Abstract In this paper, an improved model-free adaptive control strategy is proposed for the trajectory tracking problem of the wheeled mobile robot (WMR) with time-delay and bounded disturbance. Firstly, the original nonlinear time delay system is transformed into a data model by applying the full-form dynamic linearization method (FFDL). Secondly, the discrete-time extended state observer (DESO) is applied to estimate the unknown residual nonlinear time-varying term. A full-form dynamic linearization model-free adaptive control scheme based on discrete-time extended state observer (DESO-based FFDL MFAC) is proposed. In addition, a full-form dynamic linearization event-triggered model-free adaptive control based on discrete-time extended state observer (DESO-based FFDL ET-MFAC) is established by designing an event-triggering condition to assure Lyapunov stability. The control input signal is updated only if the system indicator meets the provided event-triggering condition; otherwise, the control input remains unchanged which can address limited communication bandwidth effectively. Finally, the effectiveness of the proposed method is verified by simulation.

Hybrid Dynamic Event-Triggered Tracking Control of Wheeled Mobile Robots Against Stochastic DoS Attacks

Hybrid Dynamic Event-Triggered Tracking Control of Wheeled Mobile Robots Against Stochastic DoS Attacks

Concurrent Learning-Based Neuroadaptive Robust Tracking Control of Wheeled Mobile Robot: An Event-Triggered Design

In this paper, an event-based neuro-adaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network, which approximates an unknown perturbation, is used to design an adaptive sliding mode controller (SMC). The RBFNN weights and SMC parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Further, a suitable triggering condition is designed to achieve a reduced number of control computations while minimizing network resources without sacrificing the stability of the sampled data closed-loop control system. A finite sampling frequency is guaranteed for the designed triggering condition by establishing a positive lower bound on the inter-event execution time which is equivalent to the Zeno-free behaviour of the system. Finally, the proposed event-based neuro-adaptive robust controller is implemented on a practical system (Q-bot 2e) to show the effectiveness of the proposed design.

Differential flatness-based adaptive robust tracking control for wheeled mobile robots with slippage disturbances

Wheeled mobile robots (WMRs) have a wide range of applications in logistics transportation and industrial productions, among which the motion control has always been one of the hot spots in the current WMR researches. However, most of previous designed controllers assumed that the WMR motion had no slippage. Ignoring the slippage factors usually results in a decrease in control performance and even leads to unstable motion. To address such a challenge, a kinematic model with differential flatness is established through dynamic feedback-linearization, which comprehensively considers the multidirectional slippage of mobile robot, including longitudinal and steering slippage. Subsequently, benefited from the one-to-one mapping of states and inputs to flat outputs in differential flat system, an adaptive robust control (ARC) method is proposed to stabilize the system. Different from previous robust control studies, even if the knowledge of the upper bound of system uncertainties is unknown in advance, the proposed adaptive robust controller can still achieve satisfying performance by adaptive estimation of the upper bound of system uncertainties. The effectiveness and feasibility of the proposed method are confirmed by comparative experiments on WMR with slippage disturbance.

Autonomous Planning and Robust Control for Wheeled Mobile Robot With Slippage Disturbances Based on Differential Flat

AbstractThis paper proposes a wheeled mobile robot (WMR) robust control scheme. The feasible strategy is developed to achieve an efficient and robust autonomous mobile robot motion. To realize kinematic autonomous planning and control of the WMR, a novel controller is designed based on control Lyapunov function. This part can be divided into the following two aspects: 1) considering the nonholonomic constraints in the autonomous mobile robot trajectory tracking, a dynamic feedback‐linearization is adopted by utilizing differential flatness‐based integrated control framework to achieve full‐state controllability; 2) to compensate the structured uncertainties and slippage disturbances related to the robot kinematic model, a robust controller is designed based on control Lyapunov function with quadratic programming. Such a strategy can achieve autonomous motion even with unknown slippage disturbances subject to various constraints. Moreover, the sufficient condition is also analyzed to ensure the WMR system exponential stability. The effectiveness and performance of the proposed method are verified by numerical simulation.

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%.

Prescribed-time formation control for wheeled mobile robots with time-varying super-twisting extended state observer

This article studies the prescribed-time formation control for wheeled mobile robots within the leader-following consensus framework under directed topology. A time-varying prescribed-time super-twisting extended state observer is constructed for each robot to estimate the total disturbance of its dynamics. Different from the traditional active disturbance rejection control, the total disturbance is allowed to contain some nonsmooth component. Based on it, a formation control method with prescribed-time convergence is developed for the follower to realize the formation. The effectiveness of the proposed approach is demonstrated through two experiments.

Lyapunov based global trajectory tracking control of wheeled mobile robot

This paper considers the global trajectory tracking control problem of wheeled mobile robot. A novel tracking controller for the kinematic model of wheeled mobile robot is presented based on the cascaded approach theory. Meanwhile, by using the Lyapunov stability theory, the stability of the controller is proved. Finally, the proposed scheme is implemented on the wheeled mobile robot. The simulation results show the effectiveness of the controller.