Model Of Wheeled Mobile Robot Research Articles

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.

Model Predictive Control of Magnetic Helical Swimmers in Two-Dimensional Plane

Magnetic micro/mini-swimmers have a great potential application in biomedical research and have gained broad attention. Recent research studies focus on the automatic control methods of magnetic micro/mini-swimmers, such as artificial intelligence methods. However, their autonomous manipulation remains a challenge since they are subject to various disturbances from the external environment and model uncertainties. The current methods employ a status observer to estimate these disturbances and uncertainties. In this paper, we apply a data-driven technique that utilizes the nonlinear approximation ability of neural networks (NNs). To be specific, a flexible structure of NNs, Broad Learning System (BLS), is employed to model the input-output mapping relationship between the direction of the rotating magnetic field and the swimming direction of the helical miniature swimmer. Then, according to the dual-variable decoupling control, a levitation controller is formulated and a path following controller is proposed based on a planar three-degree-of-freedom model of magnetic helical swimmers, which is inspired by the model of wheeled mobile robots. Simulations and experiments are conducted to quantitatively validate the proposed control method using different planar paths. The experiment results show that the mean absolute error of the path following control is about 3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> of the body length which is less than 0.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mm$</tex-math> </inline-formula> . Our proposed control method provides a preliminary study to alleviate the impact of disturbances and uncertainties on the control performance of magnetic micro/mini-swimmers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is mainly motivated by the potential application of artificial intelligence methods in magnetic micro/mini-robot community, especially for the applications that reject disturbances and uncertainties. In practice, a path planner is employed to compute a pre-defined reference path that connects the start and the targeted locations. Then our control method is applied to the magnetic helical swimmer and it is guided to follow the reference path. Tackling external disturbances and model uncertainties is still challenging during the control progress. Neural network-based technique is an intuitive approach since their nonlinear modeling ability and generalization are suitable for estimating these disturbances and uncertainties. As for training the neural-network model, the training data about the angle parameters should be recorded by manual control of the helical swimmer. These angle parameters define the rotating axis of the uniform rotating magnetic field and the self-rotating axis of the magnetic helical swimmer. According to the planar motion model, the optimal controller is formulated using the feedback distance error and angle error, and the sum of the control signal and the prediction of the compensating model is used as the final control input. Our electromagnetic coil system features easy operation and configuration of cameras or other sensors. Simulations and experiments validate the performance of the neural network-based compensating method and the proposed optimal control method using magnetic micro/mini-swimmers.

Power Consumption Modeling of Wheeled Mobile Robots With Multiple Driving Modes

Wheeled mobile robots (WMRs) are being used in many real-world applications. The main challenge in deployment of WMRs is their limited battery power. In order to complete a given task autonomously, it becomes necessary to estimate a priori the total energy consumption for the task assigned to a WMR. An accurate power consumption model allows to estimate the energy consumption of a WMR. This paper presents the power consumption modeling of WMRs using a power-based graphical modeling tool called bond graph. First, the dynamic bond graph model is developed for the generalized configuration of a WMR according to number and types of wheels attached in chassis. Then, the developed bond graph model is utilized for the power consumption modeling of a WMR. The proposed power consumption model includes the various driving modes of a WMR, by considering its degree of steerability. Finally, the proposed methodology is applied to a reconfigurable outdoor WMR with a degree of steerability varying from 0 to 2. The estimated power from the model is compared with the measured power from the experiments. Further, the proposed power model is used for the energy planning in a trajectory tracking task based on changing driving modes of a reconfigurable WMR.

Neural‐based online finite‐time optimal tracking control for wheeled mobile robotic system with inequality constraints

AbstractIn this study, a finite‐time online optimal controller was designed for a nonlinear wheeled mobile robotic system (WMRS) with inequality constraints, based on reinforcement learning (RL) neural networks. In addition, an extended cost function, obtained by introducing a penalty function to the original long‐time cost function, was proposed to deal with the optimal control problem of the system with inequality constraints. A novel Hamilton‐Jacobi‐Bellman (HJB) equation containing the constraint conditions was defined to determine the optimal control input. Furthermore, two neural networks (NNs), a critic and an actor NN, were established to approximate the extended cost function and the optimal control input, respectively. The adaptation laws of the critic and actor NN were obtained with the gradient descent method. The semi‐global practical finite‐time stability (SGPFS) was proved using Lyapunov's stability theory. The tracking error converges to a small region near zero within the constraints in a finite period. Finally, the effectiveness of the proposed optimal controller was verified by a simulation based on a practical wheeled mobile robot model.

A Comparative Study of Various Intelligent Algorithms Based Nonlinear PID Neural Trajectory Tracking Controller for the Differential Wheeled Mobile Robot Model

This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSOlearning algorithm is more effective and robust than genetic learning algorithm; this is demonstrated by the minimized tracking error and obtained smoothness of the velocity control signal, especially when external disturbances are applied.

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.

Nonlinear tracking control for WMRs with state, sensor and control delays

This paper presents tracking control method for wheeled mobile robots (WMRs) with state, sensor and control delays. The differential equations are obtained from the kinematic model of WMR with Pfaffian constraint. Thus, the state-space representations of WMR motion system and the target path system can be achieved. And the tracking error system with state, sensor and control delays is got. A nonlinear tracking control is designed with not only the state of future time but also that of past time. The exponential stability of the closed-loop system under the tracking control is proved. Since the control has the predictive states, it cannot be physically implemented. A high-gain observer (HGO) is constructed to solve this physical implement problem. Moreover, the exponential stability of the errors between the original states and the generated states from HGO is demonstrated. The simulation experiments verified the effectiveness, convenience and simplicity of the designed state-feedback control (SFC) and the output feedback control (OFC) via HGO, which illustrates that the target paths of a circle and a figure-of-8 can be successfully tracked by WMR under the proposed controllers by this paper.

A Custom Parallel Hardware Architecture of Nonlinear Model-Predictive Control on FPGA

This article presents the field-programmable gate array (FPGA) implementation of a particle swarm optimization (PSO)-based nonlinear model-predictive control (NMPC) for applications with millisecond timescales and resource-constrained embedded systems. A custom hardware circuit architecture of an NMPC controller, which mainly includes phase-locked loop clock, universal asynchronous receiver transmitter interface, trigonometric function, random number generator, objective function, and PSO solver modules, is implemented on the FPGA based on a hardware description language specification. It employs parallelism, pipelining, and specialized numerical formats to enhance the online computation performance of NMPC. In addition, the partial parallel expansion calculations of the fitness (objective) function and the particle position update are adopted for trading off the computational performance against resource usage. The hardware-in-the-loop experimental results through controlling a wheeled mobile robot model demonstrate that the design is capable of computing the required control action in real time by using a midrange FPGA, while still offering good control performance.

Kinematic and dynamic control model of wheeled mobile robot under internet of things and neural network.

This study aims to solve the issues of nonlinearity, non-integrity constraints, under-actuated systems in mobile robots. The wheeled robot is selected as the research object, and a kinematic and dynamic control model based on Internet of Things (IoT) and neural network is proposed. With the help of IoT sensors, the proposed model can realize effective control of the mobile robot under the premise of ensuring safety using the model tracking scheme and the radial basis function adaptive control algorithm. The results show that the robot can be controlled effectively to break the speed and acceleration constraints using the strategy based on the model predictive control, thus realizing smooth movement under the premise of safety. The self-adapting algorithm based on the IoT and neural network shows notable advantages in parameter uncertainty and roller skidding well. The proposed model algorithm shows a fast convergence rate of about 2 s, which has effectively improved performances in trajectory tracking and robustness of the wheeled mobile robot, and can solve the difficulties of wheeled mobile robots in practical applications, showing reliable reference value for algorithm research in this field.

Trajectory Tracking and Stabilization of Nonholonomic Wheeled Mobile Robot Using Recursive Integral Backstepping Control

In this paper, a generalized nontriangular normal form is presented to facilitate designing a recursive integral backstepping control for the class of underactuated nonholonomic systems, i.e., wheeled mobile robots (WMRs) that perform posture stabilization and trajectory tracking in environments without obstacles. Based on the differential geometry theory, we develop a multiple input multiple output (MINO) generalization of normal form using the input-output feedback linearization technique. Then, the change of variables (diffeomorphism) transform the state-space model of WMR, incorporating both kinematic and dynamic models into nontriangular normal form. As a result, the system dynamics can be represented as internal and external dynamics. The nonlinear internal dynamics of WMR pose serious challenges to design a suitable controller due to its internal dynamics being not minimum phase and non-strict feedback form structure. The proposed backstepping controller is designed in two steps. First, a standard integral backstepping controller is designed to stabilize the robot’s orientation angle. Then, a recursive integral backstepping control technique is applied to achieve asymptotic convergence of position error to zero. Hence, both asymptotic posture stabilization and trajectory tracking are achieved in semi-global regions, except the nonzero initial condition of the orientation angle. The asymptotic stability of the entire closed-loop system is shown using the Lyapunov criteria.

Ball-Catching System Using Image Processing and an Omni-Directional Wheeled Mobile Robot.

The ball-catching system examined in this research, which was composed of an omni-directional wheeled mobile robot and an image processing system that included a dynamic stereo vision camera and a static camera, was used to capture a thrown ball. The thrown ball was tracked by the dynamic stereo vision camera, and the omni-directional wheeled mobile robot was navigated through the static camera. A Kalman filter with deep learning was used to decrease the visual measurement noises and to estimate the ball’s position and velocity. The ball’s future trajectory and landing point was predicted by estimating its position and velocity. Feedback linearization was used to linearize the omni-directional wheeled mobile robot model and was then combined with a proportional-integral-derivative (PID) controller. The visual tracking algorithm was initially simulated numerically, and then the performance of the designed system was verified experimentally. We verified that the designed system was able to precisely catch a thrown ball.

Path tracking control optimization algorithm for mobile robot based on backstepping control algorithm

Path tracking control of wheeled mobile robot is always an important research content in intelligent control research. Aiming at the control problem that the nonholonomic nature of wheeled mobile robot and external interference factors affect the tracking control effect, an improved backstepping control algorithm is proposed based on backstepping algorithm and speed compensation control method. Firstly, the kinematics model of wheeled mobile robot is constructed, and the mathematical relations of kinematics and path tracking state error are deduced. Secondly, the kinematics speed control law of mobile robot is designed based on the backstepping control algorithm and speed compensation control method, which improves the robustness of path tracking control. Finally, the design is verified by Lyapunov stability theorem. The tracking convergence of the tracking control algorithm is discussed. Compared with the traditional backstepping control algorithm, the improved algorithm improves the robustness of path tracking control. The tracking control algorithm proposed in this paper can be applied in the related fields of wheeled mobile robot, and the practical benefit is remarkable.

Motion modeling of a non-holonomic wheeled mobile robot based on trajectory tracking control

Trajectory tracking is a problem of emphasis for the mobile robot. In this study, a coordinate transformation method was used to build a kinematic model of the wheeled mobile robot. A traditional proportional-integral-derivative control method was researched and improved by combining it with a neural network. A neural network proportional-integral-derivative trajectory tracking control method was thus designed, and a simulation experiment was performed using Simulink. The results show that in circular trajectory tracking control, the maximum errors of the X axis, Y axis, and θ were approximately 2.1 m, 2.3 m, and 0.4 rad, respectively, and that the system remained stable after running for 10 s. In straight-line trajectory tracking control, the maximum errors of the X axis, Y axis, and θ were approximately −0.8 m, 1.3 m, and 0.3 rad, respectively, and the system remained stable after running for 8 s. The error was relatively small, and the effect of trajectory tracking control was good. The studied method had good performance in terms of wheeled mobile robot trajectory tracking control and is worthy of further promotion and application.

Control synthesis for 4WS vehicle-robot model for traffic program motion

The object of research is an autonomous wheeled mobile robot model 4WS (Four Wheel Steering). The need for such studies is dictated by the limitations of using 2WS (Two Wheel Steering) robots to solve the problem of achieving multiple goals associated with insufficient maneuverability and traffic safety. This is one of the most problematic places of this model.Such a task was successfully solved to achieve a single goal with this model, including reverse movements for an articulated crew, but the nature of the trajectory with multiple goals makes such a task practically unsolvable. To solve it, the design of the autonomous mobile robot DDMR model was successfully applied. The advantages of the 4WS model compared to the 2WS in terms of increasing maneuverability have led to the study of the possibility of using it to solve this problem.In this study, the possibility of synthesizing the controlled movement of an autonomous mobile robot of the 4WS model along a program path specified in an explicit, implicit, parametric form or by the law of change in its curvature is realized. In this case, the angle of rotation of the front wheels is a function of the curvature of the program path, and the rear wheels are a function of the angle of rotation of the front. A feature of synthesized control for the 4WS model is the connection with control for the 2WS model.Control for this model is synthesized initially and is an independent value. Such a reference control: the empirical dependence determines the virtual radius for which such a control is calculated for the 4WS model so that it moves along the path of the 2WS model. The rotation of the rear wheels (in phase with the front or out of phase) is considered in this case as an additional control.An important feature of the study was the development of software that made it possible to perform numerical modeling of the synthesized control in the Maple mathematical package and visualization of maneuvers of movement in the Unity 3D system.The results of numerical modeling and their visualization allow to conclude that it is possible to use the synthesized law to control autonomous mobile robots created using the 4WS model.

Enhanced closed-loop systematic kinematics analysis of wheeled mobile robots

The traditional homogeneous transform maintains central position in the kinematic modelling of robotics. However, for these kinematic modelling of wheeled mobile robots over uneven terrain, the homogeneous transform that represents angular velocity implicitly in the time derivative of the rotation matrix has a drawback in orientation representation. In this article, to improve the angular representation, a new general systematic method for kinematics modelling and analysis of wheeled mobile robot is proposed. The approach uses the Sheth–Uicker convention and loop-closure kinematic chains to derive the position, velocity and acceleration kinematics. The screw coordinates are used to reform the velocity kinematics to centroidal kinematics; then, the Jacobian calculation is simplified to solve the screw vector algebra equations instead of the matrix equations. Meanwhile, the linear and angular components of the centroidal kinematics are endowed with physical meanings and are easy to be selected as control variables. The approach is applied to a wheeled mobile robot, and its effectiveness is verified by the simulation results with various terrain.

Design of Mobile Control system for wheeled Mobile Robot based on embedded ARM Technology

The mobile control of the wheeled mobile robot is affected by local disturbance error, which leads to low control precision. A mobile control model of wheeled mobile robot based on RTK-GPS (real-time kinematic GPS) is proposed. The mobile control system of wheeled mobile robot is designed with embedded ARM technology as the core processor of controller. Firstly, the mobile control algorithm of wheeled mobile robot is designed. The attitude determination parameter of wheeled mobile robot is taken as the control constraint parameter, and the attitude determination control of the wheeled mobile robot and the path map traversal of attitude parameter output are processed. According to the coordinate position of the locus map, the robot’s moving location is carried out, and the trajectory following adjustment is realized by RTK-GPS technology, and the control law is optimized. Then the hardware of the control system is developed under the embedded ARM technology integrated information processing environment. The robot attitude adjusting system, the central integrated control module and the man-machine interaction module are designed and described in detail. The system test results show that the proposed method has better output performance, lower attitude determination error and better control convergence of wheeled mobile robot.

Dynamic Compensation Based Control of a Comprehensive Model of Wheeled Mobile Robots

This paper uses the general framework of dynamic feedback linearization for stabilization of mobile robots. Instead of using unicycle model for control design, this paper uses a comprehensive model which is based on the physics of differential drive robots. The model used in the paper describes nonholonomic underactuated behavior of robot in terms of the physical dimensions and velocities of the wheels. Next, the proposed control is applied to this model and it is theoretically proven that dynamic feedback linearization can successfully solve the stabilization problem. To evaluate the performance of proposed control, another controller is designed based on the Lyapunov's method. The performance of the two controllers and the complexity of control gain tuning process are compared. Next, the robustness of the proposed control against uncertainties is studied. The results of analysis and simulations show that the proposed control has very good performance against parametric uncertainties.

High-Fidelity Yet Fast Dynamic Models of Wheeled Mobile Robots

Advances in hardware design have made wheeled mobile robots (WMRs) exceptionally mobile. To fully exploit this mobility, we present a novel dynamic model formulation for use in WMR planning, control, and estimation systems. The formulation is high fidelity, general, modular, and fast. It builds on our prior work on recursive methods for 3-D kinematics derivation and constrained motion prediction using differential algebraic equations. It is stable, even for large integration steps, and can enforce realistic nonlinear models of wheel–terrain interaction. Simulation tests show our dynamic models to be more functional, stable, and efficient than common alternatives. Simulations can run over 1K $\times$ faster than real time on an ordinary PC. Experimental results on multiple platforms and terrain types show that, once calibrated, our models predict motion accurately. To facilitate their use, we have released open-source MATLAB and C++ libraries implementing our modeling/simulation methods.

Kinematic Analysis and Motion Control of Wheeled Mobile Robots in Cylindrical Workspaces

Wheeled mobile robots (WMRs) are often used for maintenance of round pipes or ducts, which can typically be represented as a cylindrical workspace. Working in round pipes or ducts, kinematic models of WMRs are different from those applying on a plane and thus pose significant challenges in terms of kinematic analysis and motion control. To address these challenges, the kinematic properties of WMRs in a cylindrical workspace are analyzed in this paper. First, we discuss the kinematic properties of a single wheel in a cylindrical workspace. Then, we analyze the geometric constraints of WMRs in round pipes or ducts with analytical geometry. Based on these analyses, kinematic properties of WMRs in cylindrical workspaces are discussed with screw theory. A control law based on biaxial clinometer information is proposed, and it enables the robot to move horizontally in round pipes or ducts. Finally, the motion of a single wheel purely rolling in a cylindrical workspace is simulated. Experiments using a car-like mobile robot moving in round ducts are carried out to show the feasibility of the proposed algorithm.

Velocity Tracking Control of Wheeled Mobile Robots by Iterative Learning Control

This paper presents an iterative learning control (ILC) strategy to resolve the trajectory tracking problem of wheeled mobile robots (WMRs) based on dynamic model. In the previous study of WMRs’ trajectory tracking, ILC was usually applied to the kinematical model of WMRs with the assumption that desired velocity can be tracked immediately. However, this assumption cannot be realized in the real world at all. The kinematic and dynamic models of WMRs are deduced in this chapter, and a novel combination of D-type ILC algorithm and dynamic model of WMR with random bounded disturbances are presented. To analyze the convergence of the algorithm, the method of contracting mapping, which shows that the designed controller can make the velocity tracking errors converge to zero completely when the iteration times tend to infinite, is adopted. Simulation results show the effectiveness of D-type ILC in the trajectory tracking problem of WMRs, demonstrating the effectiveness and robustness of the algorithm in the condition of random bounded disturbance. A comparative study conducted between D-type ILC and compound cosine function neural network (NN) controller also demonstrates the effectiveness of the ILC strategy.