A trajectory tracking and obstacle avoidance approach for nonholonomic mobile robots based on model predictive control

Compared to four-wheel independent drive and steering (4WID4WIS) mobile robots, three-wheel independent drive and steering (3WID3WIS) mobile robots are more cost-effective due to their lower cost, lighter weight, and better handling performance, even though their acceleration performance is reduced. This paper proposes a dynamic model predictive control (DMPC) controller for trajectory tracking of 3WID3WIS mobile robots to simplify the computational complexity and improve the accuracy of traditional model predictive control (MPC). The A* algorithm with a non-point mass model is used for path planning, enabling the robot to navigate quickly in narrow and constrained environments. Firstly, the kinematic model of the 3WID3WIS mobile robot is established. Then, based on this model, a DMPC trajectory tracking controller with dynamic effects is developed. By replacing MPC with DMPC, the computational complexity of MPC is reduced. During each control period, the prediction horizon is dynamically adjusted based on changes in trajectory curvature, establishing a functional relationship between trajectory curvature and prediction horizon. Subsequently, a comparative study between the proposed controller and the traditional MPC controller is conducted. Finally, the new controller is applied to address the trajectory tracking problem of the 3WID3WIS mobile robot. The experimental results show that DMPC improves the lateral trajectory tracking accuracy by 62.94% and the heading angle tracking accuracy by 34.81% compared to MPC.

In this study, model predictive control with softening constraints is applied to a non‐holonomic wheeled mobile robot for trajectory tracking in the presence of external disturbances. In order to improve real‐time robustness of the wheeled mobile robot, a linearised tracking error model is used to predict system behaviours. The proposed control scheme contains a feedforward controller and a feedback controller, in which both control constraints and control increment constraints are considered to achieve trajectory tracking smoothly. Finally, numerical simulations demonstrate the performances of the control scheme.

Trajectory tracking and speed control for intelligent vehicles are put forward higher requirements on accuracy by the high traffic density scenario. A standard Model Predictive Control (MPC) algorithm focuses on the trajectory tracking but ignores the speed control. This paper proposes a simultaneous trajectory tracking and speed control algorithm based on MPC approach to address fine control in both space and time dimensions for intelligent vehicles. A threefold cost function including lateral and longitudinal speed as well as comfort has been proposed to improve the control algorithm. The simulations and comparisons demonstrate that the proposed method has a better tracking and speed control performance than the existing method.

Many researchers have become interested in wheeled mobile robot (WMR) trajectory tracking control in recent years. This is due to the increased application of mobile robots in the industry, the military, the home, and public service. Classically, the movement of WMR is controlled depending on its kinematic model. However, in real-time applications, both the dynamic and kinematic models of robots and external disturbance and uncertainty affect system performance. This paper proposes backstepping combined with a Nonlinear Proportional-Integral-Derivative (NPID) controller to control a two-wheeled mobile robot (TWMR). The kinematic and dynamic models of the WMR are derived. The dynamic modeling is derived using a Lagrangian approach, and stability of the system is achieved using the Lyapunov method. The controller gains are optimized using the Genetic Algorithm optimization technique. The proposed algorithms’ performance is tested using Matlab software. The simulation result shows that the proposed method achieved preferable reference trajectory tracking with a minimum tracking error. The proposed controller outperforms the GA-based backstepping plus PID controller in terms of root-mean-square (RMS) of trajectory tracking error (47.36% in a linear and 60.32% in a nonlinear case). In addition, it shows good unknown disturbance rejection and initial point change in all scenarios.

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.

This article studies the formation and trajectory tracking control of multiagent systems. We present a novel multilayer graph for the multiagent system to enable extensibility of the interaction network. Based on the multilayer graph, a formation control law by using the potential function approach is developed for autonomous formation, formation maintenance, collision, and obstacle avoidance. When the desired formation is achieved, the barycentric of the formation shape is viewed as a virtual leader, and a model predictive control (MPC) scheme is applied to the virtual leader for tracking a reference trajectory; meanwhile, the agents will maintain the desired angles and distances via the formation control law. By applying the proposed schemes, the tasks of formation maintenance and trajectory tracking in a constrained space are fulfilled. Comprehensive simulation studies under different environmental constraints and trajectories confirm the effectiveness of the proposed approaches in addressing the formation and trajectory tracking problems.

Spherical mobile robot (spherical robot in short) has remarkable stability, flexible motion as well as significant advantages in the exploration of unmanned environments when compared with traditional mobile robots. However, as a special under-actuated nonholonomic system spherical robot cannot be transformed to the classic chained form and is difficult to control. At present, there has not been a kinematics based trajectory tracking controller which could track both the position states and the attitude states of spherical robot simultaneously. This paper deduced a three states (two position states and one attitude state) trajectory tracking controller using sliding-mode control method and based on the kinematic model of a spherical mobile robot BHQ-1. The jittering on the sliding surface was reduced under the effect of a saturation function in the controller. The effectiveness of the proposed trajectory tracking controller were verified through MATLAB simulations for both a linear trajectory and a circular trajectory.

Sliding mode observer-based model predictive tracking control for Mecanum-wheeled mobile robot

As nonholonomic mobile robots have constraints imposed on motions that are not integrable, i.e. the constraints cannot be written as time derivatives of some function of the generalized coordinates, advanced techniques are needed for the tracking control. In the paper a robust control law is proposed for trajectory tracking of nonholonomic wheeled mobile robots. The state variables of the mobile robot are represented in polar coordinates, and the dynamic equation of the system is feedback-linearized by a computed-torque method. A novel sliding mode control law is derived for asymptotically stabilizing the mobile robot to a desired trajectory. It is shown that the proposed scheme is robust to bounded system disturbances. Simulation examples and experimental results are provided to show the effectiveness of the accurate tracking capability and the robust performance of the proposed controller.

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.

This paper presents the model predictive control of Hilare-type robot on a slippery 3D terrain having known ground conditions, while avoiding wheel slip. The controller is developed based on the dynamic model of the Hilare-type robot on a 3D terrain. So, the terrain's gradient appears in the dynamic model, and is fedback in real-time for more precise trajectory tracking. Nonlinear model predictive control incorporates constraints such as maximum affordable longitudinal and lateral wheels static friction, to avoid slip and control input saturation. The Hilare-type robot is simulated while moving on a slippery hill with known static friction. The simulation exhibits accurate trajectory tracking of the autonomous mobile robot on a slippery 3D terrain having known surface conditions, while eliminating wheel slip.

Unmanned logistics vehicle (ULV) realize the automation and intelligence of cargo transportation, which improves the efficiency, cost-effectiveness and safety of logistics and distribution, while the trajectory tracking control of ULV is the key technology to ensure their safe and efficient delivery of goods. In order to solve the trajectory tracking problem of ULV in the process of delivering goods, this paper proposes a model predictive control (MPC) method based on event-triggered and fuzzy adaptive optimization parameters. Firstly, the dynamics model of the ULV is established. Secondly, an event-triggered mechanism is introduced to establish ET-MPC, while a disturbance observer is designed considering the external disturbance and the controller calculation discarding the nonlinear term. Thirdly, the advantages of fuzzy control and MPC algorithms are integrated, and the four important parameters in the MPC controller are adaptively optimized by fuzzy control, and the improved MPC control strategy is designed. Finally, the CarSim-Matlab/Simulink co-simulation platform and the experimental vehicle platform are constructed to verify the effectiveness of the improved MPC trajectory tracking controller proposed in this paper. The results show that the improved MPC control strategy can reduce the computation time of the controller, and the total number of triggering times of the controller is reduced by 46.44% compared with the classical MPC, which reduces the computational complexity of the controller and improves the accuracy and smoothness of the trajectory tracking of the ULV.

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.

To improve the stability and accuracy of quintic polynomial trajectory tracking, an MPC (model predictive control) and fuzzy PID (proportional‐integral‐difference)‐ based control method are proposed. A lateral tracking controller is designed by using MPC with rule‐based horizon parameters. The lateral tracking controller controls the steering angle to reduce the lateral tracking errors. A longitudinal tracking controller is designed by using a fuzzy PID. The longitudinal controller controls the motor torque and brake pressure referring to a throttle/brake calibration table to reduce the longitudinal tracking errors. By combining the two controllers, we achieve satisfactory trajectory tracking control. Relative vehicle trajectory tracking simulation is carried out under common scenarios of quintic polynomial trajectory in the Simulink/Carsim platform. The result shows that the strategy can avoid excessive trajectory tracking errors which ensures a better performance for trajectory tracking with high safety, stability, and adaptability.

A novel model predictive control- (MPC-) based trajectory tracking controller for mobile robot is proposed using the event-triggering mechanism, and the aim is to solve the problem that the MPC optimization problem requires a large amount of online computation and communication resources. This method includes two different event-triggering strategies, namely, the event-triggering based on threshold curve and the event-triggering based on threshold band. The selection of the triggering threshold is achieved by applying the statistical method to the historical data of the trajectory tracking of the mobile robot under the classic MPC method. Simulation and experimental tests illustrate that the proposed approach is able to significantly reduce the computation and communication burdens without affecting the control performance. Furthermore, the experimental results show that compared with the classic MPC-based tracking method, the proposed two event-triggering strategies can reduce 28.1% and 75.7% of the computation load and 0.886 s and 2.385 s communication time.