Trajectory Tracking Control of Second-order Uncertain Nonlinear Systems based on Integral Concurrent Learning Predictor

Aiming at the trajectory tracking control of AGV in aeronautical manufacturing and automated warehouse, a novel trajectory tracking control method was proposed. Firstly, the kinematic model and trajectory tracking control model of AGV were established. After analysing the reversing problem of AGV in practical situation and the limitations of classic trajectory tracking control method, and based on the similarity between the forward mode and reversing mode of AGV, a novel trajectory tracking control rule was designed and its stability was proved. The trajectory tracking control rule had simpler, less amount of calculation and improved the flexibility and handling efficiency of AGV. Finally, the trajectory tracking control experiments of sinusoid and path consisting of segments and arcs were done on experimental platform. The experiment results verified that the trajectory tracking control method were effective and feasible. The method could meet the requirements of autonomous handing for AGV in aeronautical manufacturing and automated warehouse.

The trajectory tracking control of the quadrotor unmanned aerial vehicle (QUAV) is investigated. The aim of this paper is to develop a novel trajectory tracking control method considering model uncertainties and external disturbances with finite-time stability and prescribed performance of the attitude. Firstly, the trajectory tracking control system of the QUAV is decoupled into two subsystems, i.e., position and attitude subsystems, wherein, backstepping technique is used to design the position tracking controller. Then, to guarantee the tracking performance of the attitude subsystem, a finite-time convergent performance function is developed, and a prescribed performance attitude controller is devised via using integral barrier Lyapunov function thereafter. In the design process of the above two controllers, ESO is used to estimate and compensate multiple uncertainties and disturbances simultaneously. Moreover, the fractional state feedback and discontinuous phenomenon of the control law are directly avoided to achieve the finite-time convergence rate. Finally, an illustrative example is organized to verify the effectiveness of the proposed approach.

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.

The stratospheric airship is a low‐speed aircraft capable of remaining in near space for extended periods. It can transport payloads to perform various tasks, including intelligence gathering, reconnaissance, and surveillance. Trajectory tracking control is a critical technology that enables the airship to execute its missions, such as relocating flight sites and maintaining station‐keeping. Focusing on the large error in three‐dimensional trajectory decoupling control of stratospheric airship, a three‐dimensional trajectory direct tracking control method based on reinforcement learning was researched. According to the discrete state and action space, a Markov decision process model for the three‐dimensional trajectory tracking control problem was established. The Boltzmann random distribution of reward value and probability of wind direction angle were taken as the action selection criteria of the Q ‐learning algorithm, the cerebellar model articulation controller (CMAC) neural network was constructed for the discrete action value, and the optimal action sequence was fast obtained. Taking the cube and spherical discrete state distribution as an example, the trajectory tracking control method was validated through simulation. Simulation results show that, by properly setting the action range of actuators including fans, valves, and thrust vectors and refining state action value distribution, the proposed three‐dimensional trajectory control method has high tracking accuracy, the horizontal error is in a 10‐m level, the altitude error can achieve meter level, and the tracking trajectory is smooth and has well engineering realizability.

The waypoint behavior is a method of trajectory tracking control for unmanned surface vehicle (USV). We take the waypoint behavior as the research object, choose speed, steering angle, capture radius, slip radius, lead distance, lead damper as features, and establish a prediction model for trajectory tracking control of USV based on deep neural network. The model effectively predicts the navigation effect and provides assistance and reference for the maneuvering decision of the USV. Test results show that the predicted results and the reference samples have same tendency. The proposed model can improve the performance of trajectory tracking control for the USV.

A quadrotor unmanned aerial vehicle (UAV) must achieve desired flight missions despite internal uncertainties and external disturbances. This paper proposes an adaptive trajectory tracking control method that attenuates unknown uncertainties and disturbances. Although the quadrotor is underactuated, a fully actuated controller is designed using backstepping control. To avoid repeated derivatives of control inputs, a dynamic surface method introduces a filter and auxiliary controller. Lyapunov criteria guide adaptive laws for tuning controller gain and filters. A low-power observer is integrated for state estimation. Additionally, a disturbance observer is developed and combined with the control scheme to handle unknown disturbances. Simulations on a DJI F450 quadrotor demonstrate that the proposed control algorithm offers strong trajectory-tracking performance and system stability under multiple uncertainties and external disturbances during flight.

PurposeThere are various uncertain and nonlinear problems in hydraulic legged robot systems, including parameter uncertainty, unmodeled dynamics and external disturbances. This study aims to eliminate uncertainties and improve the foot trajectory tracking control performance of hydraulic legged robots, a high-performance foot trajectory tracking control method based on fixed-time disturbance observers for hydraulic legged robots is proposed.Design/methodology/approachFirst, the robot leg mechanical system model and hydraulic system model of the hydraulic legged robot are established. Subsequently, two fixed-time disturbance observers are designed to address the unmatched lumped uncertainty and match lumped uncertainty in the system. Finally, the lumped uncertainties are compensated in the controller design, and the designed motion controller also achieves fixed-time stability.FindingsThrough simulation and experiments, it can be found that the proposed tracking control method based on fixed-time observers has better tracking control performance. The effectiveness and superiority of the proposed method have been verified.Originality/valueBoth the disturbance observers and the controller achieve fixed-time stability, effectively improving the performance of foot trajectory tracking control for hydraulic legged robots.

As typical underactuated systems, overhead cranes are widely utilized in heavy-load transportation. However, their strong nonlinear coupling and underactuated characteristics complicate precise positioning and payload swing suppression. Furthermore, model uncertainties and external disturbances in practical environments increase control complexity and degrade system performance. To address these issues, this paper develops a trajectory tracking control scheme based on time delay estimation (TDE). Specifically, some transformations are made for the dynamic model and the TDE mechanism is used to estimate unknown nonlinear dynamics and external disturbances. Then, a sliding mode trajectory tracking controller, along with the TDE mechanism, is proposed for the trajectory tracking control and uncertainties estimation of the overhead crane system. Rigorous mathematical analysis is provided to demonstrate the asymptotic stability of the closed-loop system. Finally, simulation results verify the effectiveness of the proposed method in comparison with the existing control methods.

This paper designed a trajectory tracking controller for an underwater vehicle considering model uncertainties and external force disturbances using the Port-Hamiltonian (PH) control theory. Firstly, the mathematical model of the underwater vehicle is established and expressed under the PH framework. However, the hydrodynamic force coefficients inside the mathematical model are hard to obtain accurately without detailed towing tank experiments. To address this, we will propose a parameter observer to estimate the unknown parameters caused by model uncertainties. Meanwhile, the vehicle inevitably suffers from external unknown force disturbances driven by current and wind. So another observer based on passivity theory is proposed to address the unknown fore disturbance. The control law is derived from reshaping kinetic energy and injecting damping terms via the interconnection and damping assignment passivity-based control method. This proposed controller has a physical interpretation. Moreover, it has been proved that the proposed controller can enable vehicles to track the desired trajectory and minimize the tracking errors exponentially. Numerical simulations and comparisons illustrate the stability and effectiveness of the proposed controller to model uncertainties and unknown external disturbances.KeywordsUnmanned underwater vehiclePort-controlled Hamiltonian systemTrajectory tracking control

Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

The problem of event-mggered control for a class of second-order uncertain nonlinear systems subject to A novel in this paper is that the input-to-state stability (ISS) assumption is removed. By adding a power integrator technique, we design a virtual control signal to address the uncertainties and nonlinearities of nonlinear systems. Through rigorous stability analysis, all the signals of the closed-loop system are proven to be globally uniformly bounded and can converge to an arbitrarily small set which can be be adjusted by one parameter. A numerical example demonstrates the effectiveness of the designed scheme.

This paper investigates a trajectory tracking control method for multi-underactuated underwater vehicle (AUV) formations with uncertain model parameters and external environmental disturbances. Firstly, a dual closed-loop fixed-time integral sliding mode controller is designed. By combining fixed-time theory and integral sliding mode control, this controller ensures the stability of the formation tracking and guarantees the convergence of the tracking error to zero within a fixed time duration. Secondly, an adaptive radial basis function (RBF) neural network controller is integrated with a conditional integrator to address uncertainties in model parameters, approximation errors, and external environmental disturbances in practical multi-AUV systems. This controller exhibits robustness and adaptivity. Additionally, a virtual leader strategy is employed to enhance the robustness of the formation system and prevent formation collapse caused by leader AUV failures. Finally, simulation results validate the effectiveness of the proposed formation controller, demonstrating accurate trajectory tracking by the AUV formation.

In this paper, we investigate the trajectory tracking control problem of surface ships subject to unknown parameters, unknown time-varying disturbances, and input saturation. The input saturation is approximated using a Gaussian error function. The compounded uncertain term caused by the unknown parameters and disturbances is transformed into a linear parametric form with a single parameter called virtual parameter. Then, a novel robust adaptive ship trajectory tracking control law is designed using the adaptive vector-backstepping design method, where the adaptive law online provides the estimation of the virtual parameter. Further, a finite-time control scheme is developed injecting a power function vector of the position and velocity errors into the proposed control scheme such that the control performance is improved. Both our control schemes are simple to compute and easy to implement in engineering applications. Theoretical analysis indicates that the designed control laws can force the surface ship to track the desired trajectory while guaranteeing the global uniform ultimate boundedness of all the signals in the closed-loop trajectory tracking control system of surface ships. Simulation results and comparison verify the effectiveness of our novel robust adaptive trajectory tracking control schemes.

<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.

A bionic robotic fish has great potential application prospect. High maneuverability swimming control of a bionic robotic fish has been one of the research hotspots in the robotic fish field. In this paper, an iterative learning method has been proposed to solve the trajectory tracking control problem of robotic fish swimming. First, a dynamic model of the multi-joint bionic robotic fish is established. By considering a three-joint robotic fish as an example, the unified expression of the dynamic equation of the three-joint bionic robotic fish is obtained by Lagrange method. Second, the iterative learning controller for controlling the bionic robotic fish is designed. Then the convergence of the iterative learning controller is proved. Finally, the trajectory tracking control simulation experiment based on iterative learning is conducted. The simulation results show that the trajectory tracking control method based on iterative learning for a bionic robotic fish is effective.