Control Problem Of Vehicle Research Articles

Theory and experiment on distributed output formation tracking of unmanned aerial and ground vehicle swarm systems over jointly connected digraphs

This paper studies the distributed output formation tracking control problem of the unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) swarm systems, where the UAV swarm cooperatively tracks the output trajectory of the UGV in a formation under directed jointly connected communication networks. A three-layer formation tracking protocol is proposed. Firstly, a novel distributed observer using neighbor interactions is designed for each UAV to estimate the states of the UGV with parameterized inputs over periodic jointly connected digraphs. Next, based on the observation result and output regulation theory, a virtual reference system that tracks the trajectory of the UGV in the desired formation is constructed to generate reference states for each UAV. Then based on the differential flatness of UAVs, a geometric controller is utilized for UAVs to track the reference states and form the formation. An algorithm to determine the gain matrices of the protocol is also presented while the convergence of the system is analyzed. Finally, an experiment platform with three quadrotor UAVs and one UGV is built. The effectiveness of the proposed protocol is validated both by the simulation and experiment.

A Robust Hybrid Iterative Learning Formation Strategy for Multi-Unmanned Aerial Vehicle Systems with Multi-Operating Modes

This paper investigates the formation control problem of multi-unmanned aerial vehicle (UAV) systems with multi-operating modes. While mode switching enhances the flexibility of multi-UAV systems, it also introduces dynamic model switching behaviors in UAVs. Moreover, obtaining an accurate dynamic model for a multi-UAV system is challenging in practice. In addition, communication link failures and time-varying unknown disturbances are inevitable in multi-UAV systems. Hence, to overcome the adverse effects of the above challenges, a hybrid iterative learning formation control strategy is proposed in this paper. The proposed controller does not rely on precise modeling and exhibits its learning ability by utilizing historical input–output data to update the current control input. Furthermore, two convergence theorems are proven to guarantee the convergence of state, disturbance estimation, and formation tracking errors. Finally, three simulation examples are conducted for a multi-UAV system consisting of four quadrotor UAVs under multi-operating modes, switching topologies, and external disturbances. The results of the simulations show the strategy’s effectiveness and superiority in achieving the desired formation control objectives.

Trajectory tracking control of unmanned surface vehicle based on optimized barrier Lyapunov function under real ocean wave modeling

The state-constrained trajectory tracking control problem of unmanned surface vehicle (USV) under real wave interference is studied in this paper. This method is closer to the actual situation. Firstly, the interference of real ocean waves measured by ocean wave experiments on USV is considered. The wave spectrum and the equal interval method are used to simulate ocean waves in combination with the classical long-crested wave model. Secondly, the simulated wave is innovatively transformed into wave interference force, and the interference of the real wave is applied to the state constraint control of USV. The disturbance observer is used to observe and compensate for the external real wave interference, and the input saturation problem in real situations is fully considered. Furthermore, this paper proposes an improved logarithmic constraint function, which pre-set the performance function to constrain the trajectory tracking position error of the USV within a reasonable range. Finally, the rationality and effectiveness of the control strategy are verified by simulation.

Distributed state constraint containment control of multi-AUV formation with unknown control direction

The control problem of multi-autonomous underwater vehicle (multi-AUV) formation systems is a hot issue in current research. The research goal is to make the formation have a more comprehensive ability to deal with emergencies under higher control accuracy. In this paper, input saturation, disturbances, and system uncertainties are considered. To achieve the convergence of system containment errors, a state constraint containment control method is proposed for multi-AUV systems with unknown control direction. Combined with unknown control direction, new auxiliary variables are constructed to compensate the input saturation. On this basis, the follower reference trajectories and errors are defined. To constrain some system states, the tan-type barrier Lyapunov function (TBLF) is proposed. The estimated values of system uncertainties and disturbances can be obtained by neural network. Compared to other works, the amount of data generated by neural network is further reduced by the optimized adaptive law in this study. The unknown control direction problem is handled based on Nussbaum function, and its limitation range is expanded. The designed control method ensures the stable operation of formation systems. The containment errors can converge to a small neighborhood of zero. By comparing the numerical simulation results with other work, the advantages of the control algorithm proposed in this paper has been verified.

Neuro-adaptive path following control of autonomous ground vehicles with input deadzone

This article investigates the path-following control problem of an autonomous ground vehicle (AGV) with unknown external disturbances and input deadzones. Neural networks are used to estimate unknown external disturbances, dead zones, and nonlinear functions. The minimum learning parameter scheme is employed to adjust the neural network to reduce the computational load. A backstepping control is proposed to facilitate the tracking of the target path. The steady-state path-following error is decreased by adding an integral error term to the backstepping controller. Command filtering is employed to address the explosion of the complexity issue of the conventional backstepping approach, and the filtering error is compensated via an auxiliary signal. Lyapunov stability study indicates that the AGV closed-loop system is bounded by the proposed control with reasonable accuracy. At last, simulations are given to demonstrate the potential of the proposed scheme in path-following control.

Disturbance observer–based finite‐time fault tolerant control of quadrotor unmanned aerial vehicles with command filtered and event‐triggered techniques

AbstractThis article primarily focuses on the finite‐time fault‐tolerant tracking control problem of quadrotor unmanned aerial vehicle (UAV) systems. Our main innovation points are: (1) A simple cubic form of Lyapunov function is applied to the design of the position ring controller of the quadrotor UAV; (2) The command filtering technology effectively solves the challenge of derivation of complex coupling matrixs in the controller design process; (3) An improved disturbance observer and a variable threshold event‐triggered mechanism are applied in the Quadrotor UAV system. In summary, the overall approach combines the backstepping method and finite‐time Lyapunov stability to prove the finite‐time stability of all the signals in the system. Finally, the effectiveness and rationality of the algorithm are verified through simulation experiments and its application on an actual simulation platform.

Grouping Formation and Obstacle Avoidance Control of UAV Swarm Based on Synchronous DMPC

This paper focuses on the grouping formation control problem of unmanned aerial vehicle (UAV) swarms in obstacle environments. A grouping formation and obstacle avoidance control algorithm based on synchronous distributed model predictive control (DMPC) is proposed. First, the UAV swarm is divided into several groups horizontally and into a leader layer and a follower layer vertically. Second, tracking is regarded as the objective, and collision avoidance and obstacle avoidance are considered as constraints. By combining the velocity obstacle method with synchronous DMPC and providing corresponding terminal components, a leader layer control law is designed. The control law can enable the UAV swarm to track the target while avoiding collisions and dynamic obstacles. Then, considering the formation maintenance term, based on different priorities, member-level obstacle avoidance and group-level obstacle avoidance strategies are proposed, and the corresponding follower layer control laws are provided. Furthermore, the stability of the UAV swarm system under the control algorithm is demonstrated based on the Lyapunov theory. Finally, the effectiveness of the designed algorithm and its superiority in obstacle avoidance are verified through simulations.

Safe path planning and adjustable zonotope-tube model predictive tracking control for autonomous vehicle

The path planning and tracking control problem of autonomous vehicle with model matched uncertainty and external disturbance is studied in this paper. A safe lane change zone is designed by considering lane change time, distance, and driving speed. Meanwhile, the candidate paths are generated using cubic quasi-uniform B-spline curve, and the optimal path is selected based on feasibility, comfort, and efficiency criteria. The proposed planning method ensures an efficient and comfortable lane change path without collision with surrounding vehicles. Model uncertainty and external disturbance may affect lane change tracking control of autonomous vehicle. Hence, a fault-tolerant path tracking controller is implemented by using deep neural networks and adjustable zonotope-tube model predictive control. A deep neural network-based controller is proposed to compensate for model matched uncertainty, thereby enhancing the fault tolerance of the control system. Then, an adjustable zonotope-tube model predictive controller is designed to enclose the actual trajectory within a zonotope-tube with the nominal state as the center and the disturbance set as the radius by using the feedback control to achieve the asymptotic stabilization of the closed-loop system. The effectiveness of this path planning and tracking control method is finally verified by numerical simulation.

Robust adaptive fault-tolerant control for path maneuvering of autonomous surface vehicles with actuator faults based on the noncooperative game strategy

This paper investigates a parameterized path-guided following/maneuvering control problem of an autonomous surface vehicle (ASV) subject to actuator faults via a noncooperative game strategy. Unlike the existing path maneuvering control methods, the noncooperative game of this paper includes two sub-games. On one hand, the path update is considered to be against the effort of kinematic control. On the other hand, the kinetic control is considered to be against the total disturbances consisting of actuator faults and external disturbances. A robust adaptive fault-tolerant path maneuvering controller is designed based on a noncooperative game approach. Specifically, we design a kinematic control law using an improved dynamic surface control approach to achieve the geometric objective. An improved neural predictor is constructed, in which a concurrent learning-based adaptation is designed to identify unknown fault coefficients. A path update law and a kinetic control law are calculated to cover noncooperative games by using an adaptive dynamic programming approach. Theoretical analysis shows that the closed-loop system is input-to-state stable, and the proposed method can meet the geometric objective, the dynamic objective, and the fault-tolerant objective. Finally, simulation results demonstrate the efficacy of the proposed robust adaptive fault-tolerant control method for path maneuvering.

Optimal tracking control of an Autonomous Underwater Vehicle: A PMP approach

This paper addresses the optimal tracking control problem of an Autonomous Underwater Vehicle (AUV) using Pontryagin’s Minimum Principle (PMP). The formulation uses a choice of Hamiltonian function using the desired control objective with AUV dynamics acting as dynamic constraints. We first develop PMP based on AUV kinematics and then extend it to the dynamics to arrive at optimal thrusts and moments. Necessary conditions for optimality are derived for both the models using PMP, which results in optimal trajectories that simultaneously minimize the tracking error as well as the control cost, thereby arriving at energy optimality. It was observed that the adjoint variables (costates) are indeed the momenta in the inertial and body-fixed frames. At the kinematic level, this forms a stable solution. The developed methodology is applied to both 2D and 3D AUV model with disturbances due to inputs and ocean currents. Numerical simulations are carried out with the derived control laws for a given trajectory tracking target. Quantitative evaluation of the performance and comparison of the controller is done using Mean Square Error(MSE) and Total Variation(TV) measures. The proposed control laws are found to achieve the desired control objectives.

Depth control of an unmanned underwater vehicle based on robust inversion and sliding mode variable structure control

This paper addresses the depth control problem of an underactuated Unmanned Underwater Vehicle (UUV) subject to model uncertainties and ocean current interference. A robust inversion sliding mode variable structure control method is proposed. First, a vertical-plane UUV depth control model is established. Then, the inversion control method is combined with the sliding mode variable structure control method to devise an inversion sliding mode control algorithm. In order to further improve the control system’s robustness against external environmental interference, a robust control methodology is introduced for the design of the depth controller. Simulation results demonstrate that the designed controller effectively addresses the issues of model uncertainties and ocean current interference, thereby enhancing the UUV’s depth control accuracy.

Collision avoidance in target encirclement and tracking of unmanned aerial vehicles under a dynamic event-triggered formation control

This paper addresses the dynamic event-triggered fixed-time distributed control problem of unmanned aerial vehicle (UAV) formation for target encirclement and tracking with collision avoidance. A fixed-time radial basis function (RBF) neural network (NN) adaptive disturbance observer is designed for faster estimation and compensation of the lumped disturbances from the UAV formation system. By extending the lemmas of interconnected systems and fixed-time, a novel fixed-time interconnected system lemma is proposed. Then, a dynamic event-triggered control algorithm is proposed to establish the stability of the complex closed-loop interconnected system for target encirclement and formation tracking in fixed time and to demonstrate that the convergence time of the system is independent of the initial state. Subsequently, a risk avoidance mechanism is incorporated into the event-triggered fixed-time control framework to avoid collisions within the UAV formation. The proposed control strategy has been rigorously mathematically demonstrated. Finally, using a visual simulation platform, several simulations are presented to illustrate the remarkable performance and superiority of the proposed control method.

Integrated Localization and Planning for Cruise Control of UGV Platoons in Infrastructure-Free Environments

This paper investigates the cruise control problem of unmanned ground vehicle (UGV) platoons from the implementation perspective. Unlike most existing works related to platoon cruise control which rely on positioning infrastructures such as lane markings, roadside units, and global navigation satellite systems (GNSS), this paper explores a new problem: platoon cruise control in environments without positioning infrastructures. The introduction of this constraint disables most existing cruise control approaches. To address this problem, an integrated localization and planning framework is proposed, which is composed of three modular algorithms. Firstly, to localize multiple vehicles in a common coordinate system, a collaborative localization algorithm is developed through matching local perceptions of different vehicles. Secondly, to maintain the desired platoon configuration, the historical trajectory of the preceding vehicle is reconstructed, based on which the target state is planned for the following vehicle. Finally, a virtual controller based algorithm is designed to generate feasible trajectories for the following vehicle in real time. The proposed framework has two salient features. Firstly, it does not depend on positioning infrastructures and does not introduce additional positioning sensors, such as GNSS/INS modules, ultra-wideband (UWB) devices, magnetic meters and so on, as long as each vehicle is equipped with a perception sensor (Lidar, radar or camera), which however is essential equipment for nowaday autonomous systems. Secondly, the proposed framework does not depend on direct observations between vehicles to achieve relative localization, making it applicable in non-line-of-sight (non-LOS) situations. Real-world experiments have been conducted to validate the effectiveness, robustness and practicality of the proposed framework.

Research on Practical Path Tracking Control of Autonomous Underwater Vehicle Based on Constructive Dynamic Gain Controller

In this study, the stochastic nonlinear system-based trajectory tracking control problem of an autonomous underwater vehicle (AUV) is studied. We investigate the time-varying gain adaptive control method to find possible approaches to reduce the excessive computational burden. Enhanced adaptive algorithms are devised by considering the dynamic characteristics of AUV motion. By transforming the original controller design problems into parameter selection problems and subsequently solving them using the functional time-varying observer technical theorem, we can achieve optimal control performance. The control system is shown to constrain system state error due to stochastic disturbances within arbitrarily small domains. A coordinate transformation is proposed for all system states to meet boundedness conditions. We show that the closed-loop stability is confirmed, the system is asymptotically probabilistically stable, and contraction limits given in the stability analysis may be used to certify the convergence of the AUV trajectory errors. A large number of simulation studies using an underwater vehicle model have proved the effectiveness and robustness of the proposed approach. A real-time, time-varying gain constructive control strategy is further developed for the hardware-in-the-loop simulation; the effectiveness of the controller design is confirmed by introducing the controller into the AUV actuator model.

Robust Self-Learning Fault-Tolerant Control for Hypersonic Flight Vehicle Based on ADHDP

In this article, a robust self-learning fault-tolerant control (FTC) strategy is proposed to deal with the tracking control problem of the hypersonic flight vehicle (HFV) with uncertainties, actuator faults, and external disturbances. First, an adaptive baseline controller is constructed to achieve stable tracking, in which neural networks are introduced to approximate the unknown dynamics, adaptive laws are formulated to compensate the unknown lumped disturbances, and the Nussbaum technique is applied to address the time-varying actuator faults. Then, to improve the command tracking performance of the baseline controller, a data-driven auxiliary controller which can adaptively adjust the action–critic network weights over time along with the tracking deviation to obtain the optimal control signals in the sense of performance index is developed based on action-dependent heuristic dynamic programming technology. Finally, a comprehensive robust self-learning FTC law is constructed by synthesizing the baseline controller and the auxiliary controller, which leads to good robustness and tracking performance of the closed-loop HFV system. The stability and the superiority of the proposed control algorithm are verified by the Lyapunov theory and comparative numerical simulations, respectively.

Fault-Tolerant Attitude Control for Hypersonic Flight Vehicle Subject to Actuators Constraint: A Model Predictive Static Programming Approach

In this article, an improved model predictive static programming (MPSP) based fault-tolerant control (FTC) scheme is proposed to solve the attitude tracking control problem of the hypersonic vehicle (HSV). In the field of HSV, the MPSP technique has been applied successfully to solve guidance problems for its high computational efficiency. While we try to address the attitude control problem directly using it. The attitude model of HSV with uncertainty and disturbance, together with the fault model of aircraft body injury, are constructed firstly. The actuator of HSV is suffering from input constraints. Then, a feasible attitude control trajectory is generated by the improved MPSP method. The methodological innovation in this paper extends MPSP technique to the direct control of the attitude of HSV both in fixed and flexible final time. By utilizing the improved MPSP technique, the complexity of processing multiple constraints and the computation are reduced. The effectiveness of the designed FTC scheme is demonstrated through simulation under different cases with actuator constraints.

Unknown Input Observer-Based Fixed-Time Trajectory Tracking Control for QUAV with Actuator Saturation and Faults

The trajectory tracking control problem of a quadrotor unmanned aerial vehicle (QUAV) subject to external disturbances, inertia uncertainties, actuator faults, and input saturation is addressed in this paper. In contrast with previous works, input saturation herein refers to rotor speed saturation rather than thrust and torque saturation. First, the control system is decoupled into translational and rotational subsystems. Then, for both subsystems, two novel fixed-time unknown input observers (UIO) based on disturbance filtering are developed to estimate the lumped disturbance rapidly and precisely without awareness of the boundary of disturbances. Furthermore, fixed-time tracking controllers for translational and rotational subsystems are proposed based on the estimation values provided by UIO to stabilize tracking errors into a small region in fixed time regardless of the initial values. The theoretical analysis based on the Lyapunov method is presented to demonstrate the stability. Finally, the simulation results show that the proposed control method is effective. The comparison simulation is carried out to validate superiority of the proposed observer and its advantage can be summed up as: (1) the upper bound of the disturbance or its derivative is not needed; (2) the estimation results are smoother and the observation precision is higher due to the absence of sign function; (3) the mutant disturbance can be also estimated quickly and precisely.

Robust control strategy for networked vehicle system with fake data injection attack

This article investigates the safety control problem of intelligent networked vehicle and utilizes the sliding-mode control to solve the system with time delay, system disturbance, and parameter uncertainty. First, the modeling of automobile system is introduced. What’s more, time delay, parameter uncertainty, disturbance, and attack are added to the original model. Then, designed an integral sliding-mode surface and given the sufficient conditions for system stability using linear matrix inequalities. Furthermore, the controller is designed according to the slide approach rate, and it is proved that the designed controller can make the system state reach the sliding surface in finite time. Finally, the simulation is used to verify the effectiveness of the arithmetic.

Safety Flight Control Design of a Quadrotor UAV With Capability Analysis

This article considers the safety control problem of a quadrotor unmanned aerial vehicle (UAV) subject to actuator faults and external disturbances, based on the quantization of system capability and safety margin. First, a trajectory function is constructed online with backpropagation of system dynamics. Therefore, a degraded trajectory is gracefully regenerated, via the tradeoff between the remaining system capability and the expected derivatives (velocity, jerk, and snap) of the trajectory. Second, a control-oriented model is established into a form of strict feedback, integrating actuator malfunctions and disturbances. Therefore, a retrofit dynamic surface control (DSC) scheme based on the control-oriented model is developed to improve the tracking performance. When comparing to the existing control methods, the compensation ability is analyzed to determine whether the faults and disturbances can be handled or not. Finally, simulation and experimental studies are conducted to highlight the efficiency of the proposed safety control scheme.

Prescribed time observer based trajectory tracking control of autonomous underwater vehicle with tracking error constraints

This paper addresses the trajectory tracking control problem of the autonomous underwater vehicle with tracking error constraints and prescribed time convergence. In view of the fact that underwater vehicle is inevitably influenced by unknown external disturbances and input saturation, a disturbance observer is first proposed to achieve prescribed time disturbance attenuation. By virtue of the proposed observer, the observation error will arrive and maintain at a user-defined compact set in a prescribed time, which lays the foundation for subsequent control design. Then, an auxiliary dynamic system with time varying gain is constructed to deal with the input saturation phenomenon. Finally, a robust tracking control scheme is derived by integrating the proposed observer and auxiliary dynamic system into the prescribed performance control architecture, which results in both prescribed time convergence and transient performance constraints in the presence of input saturation and disturbances. Numerical simulation studies are carried out to illustrate the superiority and benefits of the proposed algorithm.