Trajectory tracking control of a unicycle-type mobile robot with a new planning algorithm

This paper presents a trajectory tracking control scheme for a three wheeled mobile robot using sliding mode control method system. The indoor mobile robot system is subject to parameter perturbation and external interference during trajectory tracking control, and the problem of slow tracking speed and large tracking error will occur. In order to improve the tracking precision, a sliding mode control algorithm based on new approach law is proposed. Firstly, the motion control model of mobile robot is established, and a sliding mode trajectory tracking controller is designed. Then adopting MATLAB to track the linear and circular trajectories, the tracking error converges to zero around 3s. Experimental results show that sliding mode control method effectively reduces sliding mode control input chattering, and the convergence speed and tracking accuracy improve. Finally, the effectiveness of the control method is thus verified in practice.

The centroid of the whole vehicle moves when the automatic guided vehicle (AGV) loads and unloads goods between stations in the intelligent factory, which reduces the trajectory tracking accuracy. To this end, the dynamics and kinematics models of a four-wheel steering AGV were established, and the Lyapunov direct method was used to construct a trajectory tracking controller with global asymptotic stability in this study. Based on the adaptive learning factor and inertia weight, an adaptive particle swarm optimization algorithm was designed to optimize the control parameters of the controller, and an adaptive global asymptotic tracking control (AGATC) controller was proposed. Under simulated working condition of moving centroid, the AGATC controller was compared with adaptive model predictive control (AMPC) controller, and the trajectory tracking simulation was carried out. The results show that the position deviation of the AGATC controller was 23.97% lower than that of the AMPC controller, and the trajectory tracking control accuracy is higher under the condition of moving centroid. Moreover, a prototype of AGV was developed, and the trajectory tracking control verification experiment was carried out. The results show that the error between simulation and experiment was less than 9.03%, which proves the authenticity and effectiveness of the AGATC controller. This study provides theoretical and experimental basis for intelligent factory to realize precision and intelligent handling technology.

Accurate trajectory tracking control of mobile robots is a complex task due to inherent system nonlinearities, modeling uncertainties, and external disturbances. To tackle these challenges, this paper proposes a neural network-based residual learning approach (NNRes) for high-fidelity and efficient system modeling. Unlike full model learning, the NNRes framework focuses on learning only the residual error between a nominal physical model and the actual system dynamics, thus preserving prior knowledge while enhancing adaptability. This hybrid model, combining the physical model and learned residual, is embedded into a Nonlinear Model Predictive Control (NMPC) scheme to improve control accuracy and robustness. The proposed method is systematically compared with state-of-the-art methods: full model learning using NNs (NNFull) and residual learning using Gaussian Processes (GPRes). Comparative simulation results demonstrate that NNRes achieves superior trajectory tracking control performance, enhanced robustness, and lower computational cost. These findings validate the effectiveness of NNRes within NMPC frameworks and offer valuable insights for the intelligent control of complex robotic systems.

<abstract> <bold>Abstract.</bold> For the spraying mobile robot working in greenhouse, due to the inconsistency of drive motors and the rough walking surface, it is easy to track off. For the liquidity of pesticide, the load always changes even the speed jumps. Because of these uncertainties, external disturbances and the difficulty of constructing the system dynamic model, it is hard to implement the trajectory tracking control of the spraying mobile robot steadily, precisely and quickly. In order to solve the problem, a smooth sliding mode trajectory tracking control method is proposed based on the distribute control strategy for each branch. Moreover, its stability is proved using the Lyapunov function. The simulation results show that the proposed method can track the desired trajectories precisely, quickly and steadily under the strong white noise. The chattering phenomenon of the control is restrained compared to the conventional sliding mode control. The trajectory tracking performance is better than that of the fuzzy control. The designed method is easy to realize and doesn't need to construct the precise mathematical model, so, it affords an economical and convenient control method for solving the trajectory tracking problem of the greenhouse spraying mobile robot under various uncertain interferences.

The paper concerns the problem of trajectory tracking control of a four-wheeled PIAP SCOUT mobile robot with non-steered wheels. For this kind of wheeled robots, it is impossible to find kinematic relationship between robot’s body motion and motion of driven wheels, because of inherent sliding of wheels on the ground during turning. This is an important problem from the point of view of control of the robot. The approach followed in the present work relies on introducing a simple linear controller with feedback of actual yaw rate of robot’s body. The yaw velocity is measured by inexpensive MEMS gyroscope. Experiments were conducted on two kinds of floor typical for office buildings: PVC flooring and carpet flooring. Measurements of motion parameters were possible with INS technique. It was found that the proposed yaw rate controller significantly reduces the angular error of path tracking for 90 degrees turn maneuver.

In order to realize the automation of transport equipment, according to the automatic guided vehicle (AGV) kinematics model, the trajectory tracking control of AGV with front wheel steering and rear wheel drive is studied. Lyapunov function is used to ensure the global stability of the system. Time-varying state feedback method based on integral backstepping is adopted, introducing virtual feedback, a trajectory tracking controller for AGV with nonholonomic constraints is proposed. In order to ensure the smooth motion of AGV, the speed and acceleration limited strategy of AGV is introduced into the controller. Simulation results show that the algorithm is fast, accurate and globally stable for different paths.

A new trajectory tracking control scheme is proposed aiming at the problem of model parameters uncertainty and poor environmental adaptability in non-holonomic mobile robot control. Dynamic characteristics of mobile robots are considered in problem formulation. The control objective is to make the robot move along an expected trajectory with low tracking error while fulfilling its physical constraints. The proposed control scheme is implemented by a combined use of MPC algorithm and grey-box identification. Simulation results show the benefits of the systematic control methodology employed.

The unfalsified control is one type of model-free adaptive control. The control strategy enables one to design a controller that is consist with a performance objective and measured past input-output online data without plant models or assumptions on the plant. The theory works by eliminating candidate controllers that do not satisfy performance specification by evolving experimental data. The controller which remained to the last is a controller which satisfies a performance specification. Based on unfalsified control theory, the trajectory tracking control of nonholonomic mobile robot is analyzed. By adopted adaptive PID control algorithm of unfalsified control strategy to feedback linearization system, the mobile robot can track the desired trajectory rapidly.

In order to realize the trajectory tracking control of mobile robots, a global fast terminal sliding mode control method is proposed in the kinematic analysis. Based on the combination of the backstepping method and the thought of the fast terminal sliding mode control, the method is more superior than the backstepping design method on convergence time and convergence rate. The system state variables are designed based on based backstepping method, then, the controller is applied to mobile robot system by recursive structure of second order fast terminal sliding mode, the control law is designed by Lyapunov method. Finally, simulation results show the validity of the presented method.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Trajectory tracking control is a critical component of the autopilot system, essential for achieving high-performance autonomous driving. This paper presents the design of a stable, reliable, accurate, fast, and robust trajectory tracking controller. Specifically, a lateral and longitudinal trajectory tracking controller based on a linear parameter time-varying model predictive control (LPV-MPC) framework is designed. Firstly, a three-degree-of-freedom vehicle dynamics model and a tracking error model are established. Secondly, a multi-objective function and constraints considering tracking accuracy and lateral stability are formulated, and the quadratic programming (QP) method is employed to solve the optimization problem. Finally, PID speed tracking control is introduced in the longitudinal control scheme for comparison with the proposed MPC longitudinal speed control. A step velocity tracking test validates the effectiveness of the MPC speed tracking controller. In the lateral control scheme, double lane change and circumferential tests are designed to assess the comprehensive performance of the controller under medium-low adhesion road conditions. Additionally, the trajectory tracking performance of the proposed MPC controller is evaluated by comparing it with traditional Sliding Mode Control (SMC) and Linear Quadratic Regulator (LQR) trajectory tracking controllers. The simulation results indicate that the maximum heading error of the MPC controller is less than 0.09 rad, the heading error remains within 0.05 rad, and the front wheel angle stabilizes within 7.5 degrees under both operating conditions. Furthermore, the MPC controller ensures tracking accuracy and smoothness without any obvious jitter while minimizing control inputs to achieve optimal control, thereby significantly enhancing the accuracy and stability of the vehicle's trajectory tracking.&lt;/div&gt;&lt;/div&gt;

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 work focuses on the trajectory tracking control of Wheeled Mobile Robots (WMRs) through neural networks and feedback control techniques. We employ different neural network architectures to analyze the environment, divide the road into segments, and generate reference trajectories for the WMR. For control purposes, we consider the nonlinear kinematic model of the WMR considering the nonholonomic constraints. Then, this kinematic model is fully linearized through a dynamic controller, which accomplishes the trajectory tracking objective. The reference trajectory procedure and control scheme combination are used to attain the desired trajectory tracking objective. To this end, we utilize a numerical simulator for a scale vehicle, namely the Autominy car. Numerical simulations using Matlab and the Robotic Operative System (ROS) demonstrate the performance of the proposed control methodology.

Path planning and tracking control are important modules of mobile robots, which have outstanding advantages in improving the flexibility of mobile robots. However, in the existing applications, these two modules are usually processed separately, which makes the obstacle avoidance process of the mobile robot uncontrollable and reduces the adaptability of the application scenario. In order to realize collision-free high-precision trajectory tracking control of mobile robots in dynamic scenes, this paper proposes an adaptive MPC control method based on curvature constraints, which can improve the safety of operation and reduce the chattering caused by unreasonable paths in the process of operation. Firstly, according to the kinematic constraints of general mobile robots, the constraint model of mobile robot path planning is constructed. At the same time, the adaptive parameter adjustment rules are adopted to realize the balance between the calculation efficiency and the control cycle. Finally, a smooth safe path is obtained by re-fitting the planned path and reprocessing the nodes with excessive curvature. Compared with traditional methods, the proposed method has better advantages.

Cable-driven parallel robots (CDPRs) are a new type of parallel robots that use cables to control a mobile platform. They possess several advantages, including large workspace, low inertia, and high payload capacity. However, there are several problems in the high-precision trajectory tracking control of CDPRs. On the one hand, all the cables must remain in tension during the entire motion process. On the other hand, the controller design is subjected to model uncertainties and external disturbances. Accordingly, this article proposes a robust synchronization control (RSC) scheme in the cable length space to achieve high-precision trajectory tracking. The synchronization control ensures motion coordination among all the cables and prevents cable relaxation, whereas the robust control eliminates modeling errors and restrains external disturbances. The uniformly ultimate boundedness of the tracking and synchronization errors in the closed-loop system equation was proved using the Lyapunov theory. Simulations and experiments of the trajectory tracking control were both implemented on a three-degree-of-freedom CDPR. Compared with the adaptive robust control scheme and the augmented proportional derivative scheme on the premise of the approximate energy consumption, the proposed RSC scheme could reduce not only the tracking errors of the cables but also the synchronization errors between adjacent cables. Moreover, the RSC scheme could significantly improve the trajectory tracking accuracy of the mobile platform. The robustness of this scheme was verified using load experiments and torque-disturbance experiments.

Extended state observer-based finite-time trajectory tracking control for wheeled mobile robots under FDI attacks