Adaptive Neural Dynamic Compensator for Mobile Robots in Trajectory tracking control

This paper explores lane changing trajectory planning and tracking control for intelligent vehicle on curved road. A novel arcs trajectory is planned for the desired lane changing trajectory. A kinematic controller and a dynamics controller are designed to implement the trajectory tracking control. Firstly, the kinematic model and dynamics model of intelligent vehicle with non-holonomic constraint are established. Secondly, two constraints of lane changing on curved road in practice (LCCP) are proposed. Thirdly, two arcs with same curvature are constructed for the desired lane changing trajectory. According to the geometrical characteristics of arcs trajectory, equations of desired state can be calculated. Finally, the backstepping method is employed to design a kinematic trajectory tracking controller. Then the sliding-mode dynamics controller is designed to ensure that the motion of the intelligent vehicle can follow the desired velocity generated by kinematic controller. The stability of control system is proved by Lyapunov theory. Computer simulation demonstrates that the desired arcs trajectory and state curves with B-spline optimization can meet the requirements of LCCP constraints and the proposed control schemes can make tracking errors to converge uniformly.

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.

In this work, original results, concerning the application of a discrete-time adaptive PID neural controller in mobile robots for trajectory tracking control, are reported. In this control strategy, the exact dynamical model of the robot does not need to be known, but a neural network is used to identify the dynamic model. To implement this strategy, two controllers are implemented separately: a kinematic controller and an adaptive neural PID controller. The uncertainty and variations in the robot dynamics are compensated by an adaptive neural PID controller. It is efficient and robust in order to achieve a good tracking performance. The stability of the proposed technique, based on the discrete-time Lyapunov's theory, is proven. Finally, experiments on the mobile robot have been developed to show the performance of the proposed technique, including the comparison with a classical PID.

Disturbance compensation control of a free-flying space robot is considered in this paper. In the method proposed here, disturbances imposed on a nominal dynamic model are estimated and compensated by utilizing a combination of joint torque input and acceleration signals. The dynamics of the equivalent manipulator with a fixed base is treated as the nominal model, noting that the mass of the base satellite is usually much larger than that of the manipulator. The attitude of the base satellite changes because of the reaction force generated by the manipulator. However, the influence of the change is estimated and compensated as a disturbance based on a combination of the joint torque and the acceleration of the end-effector in the operational coordinate frame. Computation of the proposed control method is as simple as the control of a manipulator with a fixed base. The proposed method is robust against unknown disturbances and modeling errors. This method can also be extended to the case of a space robot with multiple manipulators and attitude control of the base satellite. The effectiveness of the method is demonstrated by computer simulations.

This paper investigates robustness and convergence of iterative learning control (ILC) for mecanum-wheeled mobile robots operating under plant uncertainty and nonrepetitive disturbances. A Model-Based Corrector (MBC) ILC is introduced, which employs a fixed nominal inverse model to update the control input each iteration, thereby eliminating the need for online parameter estimation. Theoretical analysis based on the lifted-system formulation shows that convergence depends solely on the nominal model’s fidelity, ensuring stability under moderate modeling errors. The proposed method is experimentally compared with a baseline P-type ILC and an adaptive parameter-estimation ILC on a lemniscate trajectory tracking task subject to Gaussian measurement noise, a one-time disturbance, and a permanent plant change. Quantitative results demonstrate that MBC-ILC achieves the desired noise-floor accuracy (RMSE ≈ 0.02) within a few iterations, rejects nonrepetitive disturbances in a single iteration, and re-converges within six iterations following a plant change. Results show that a fixed-model corrector achieves a strong tradeoff of convergence speed, steady accuracy, and robustness at low computational cost, making it practical control architecture for robust trajectory learning in mobile robots.

In this paper, asymptotically stable control laws are developed for leader---follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation. First, a kinematic controller is developed around control strategies for single mobile robots and the idea of virtual leaders. The virtual leader is replaced with a physical mobile robot leader, and an auxiliary velocity control law is developed in order to prove the global asymptotic stability of the followers which in turn allows the local asymptotic stability of the entire formation. A novel approach is taken in the development of the dynamical controller such that the torque control inputs for the follower robots include the dynamics of the follower robot as well as the dynamics of its leader, and two cases are considered--the case when the robot dynamics are known and the case when they are unknown. In the first case, a robust adaptive control term is utilized to account for unmodeled dynamics. For the latter, a robust adaptive term is augmented with a NN control law to achieve asymptotic tracking performance in contrast with most NN controllers where a bounded tracking error result is shown. Additionally, the NN approximation error is assumed to be a function of tracking errors instead of a constant upper bound, which is commonly found in the literature. The stability of the follower robots as well as the entire formation is demonstrated in each case using Lyapunov methods and numerical results are provided.

This paper provides a discussion of challenges of neural net adaptive flight control and an examination of stability and convergence issues of adaptive control algorithms. Understanding stability and convergence issues with adaptive control is important in order to advance adaptive control to a higher technology readiness level. The stability and convergence of neural net learning law are investigated. The effect of unmodeled dynamics on learning law is examined. Potential improvements in the learning law and adaptive control architecture based on optimal estimation are presented. The paper provides a brief summary of the future research of the Integrated Resilient Aircraft Control (IRAC) in the area of adaptive flight control. The paper also discusses challenges and future research in verification and validation.

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.

The wind-diesel-based isolated hybrid power system requires dynamic reactive power compensation for the fast recovery of voltage under load and input changes. Dynamic compensation techniques give a better voltage response but at a high cost; in contrast, the static compensator reduces the compensation cost by compromising on the voltage response. The use of, both, static as well as dynamic compensators together may give cost-effective reactive power compensation for predefined transient limits of voltage response. This paper presents the pricing of reactive power compensation under steady-state and dynamic conditions of a system with fixed capacitor (FC) and STATCOM. The main contributions of the paper are: (1) evaluation of reactive power compensation using FC as a static and STATCOM as a dynamic compensator, (2) fast recovery of voltage response using genetic algorithm-based tuning of the STATCOM controller's gain constant, (3) evaluation of reactive power compensation cost for steady and dynamic conditions due to change in load and/or input demand, and (4) comparison of responses obtained for the optimized case with a pre-existing reference compensation method.

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.

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.

This paper introduces a novel control strategy under the slow down navigation circumstance for the underactuated surface vehicle (USV)-unmanned aerial vehicle (UAV) cooperative system. Within the guidance planning structure, a time-variable slow down function is presented to become the desired velocity signal of the virtual vehicles. In order to accomplish the aim of the path following, the neural adaptive controller different from position and attitude is designed. For the sake of the control system stability, combining the backstepping and the sliding mode theory, multiple parameter update laws are designed to tackle the model uncertainty and the external interferences. The stability of the closed-loop control system is proved by Lyapunov theory. Finally, the simulation experiment is conducted to testify the effectiveness of the proposed algorithm.

Differential Wheeled mobile robots with adaptive fault tolerant control are distinguished from other mobile robots for the features of detecting and identifying errors. This type of mobile robots can provide system stability and satisfactory performance of such faults. An Adaptive fault tolerant control system can be applied with many different controller structures in the literature. In this study, in order to decrease the disturbances, error and actuator faults of the system, an adaptive sliding mode controller and kinematic controller are proposed as a controllers system for the mobile robot. Besides the disturbance, the kinematic model is obtained by considering the fault in the actuator and the system states are examined. Two different system models have been compared and results are demonstrated in this work. These are mobile robot in case of actuator fault and disturbances and mobile robot with disturbance only.

This paper addresses an adaptive finite-time controller-based sliding mode approach for a wheeled mobile robot in the trajectory tracking task under external disturbances and uncertainties. This method, based on the nonlinear dynamic model of the robot and its actuators, guarantees the stability and the convergence of the closed-loop system within the finite - time. Moreover, the smoothness of the computing voltage against the chattering phenomenon is improved thanks to the adaptive switching gain, which is updated online based on the fractional power of the sliding surface. The corrective function updated online contributes to the estimation and compensation of the external disturbances. This controller is associated with the kinematic controller, which is based on the back-stepping technique to complete the trajectory tracking controller scheme. The stability of the closed-loop system is theoretically proved using Lyapunov criteria. To check the efficiency of the proposed method, the numerical simulations are carried out in the Matlab/Simulink environment.

In this paper, we propose an adaptive sliding mode-based fault tolerant control for mobile robots. While a mobile robot is tracking a given trajectory, several fault cases may occur, such as sensor model and controller faults, changes in the dynamic equation due to robot body shape or weight changes, and loss of actuator effectiveness. Disturbance signals are caused by the actuator faults and, for various reasons, can be considered the primary issue for the robots. In real-time applications, the Sliding Mode Controller (SMC) is insufficient if the robot parameters are unknown, the robot model is non-linear, and the overall system is subject to disturbances. An adaptive law is used to support the SMC to maintain the sliding surface and solve the problems of unknown system parameters, actuator faults, and disturbances. Besides SMC, the kinematic controller is also used, and its gain values are optimized using a neural network and a kinematic controller. The stability of the overall system is proven by using the Lyapunov theory. Besides actuator faults, the system is disturbed by defining a disturbance signal, which is added to the control signals. To show the effectiveness of the proposed controller, it is compared with traditional SMC and PID.