Path-following Control Research Articles

ℒ1 Adaptive Path-Following of Airships in Wind

This paper proposes an adaptive, three-dimensional (3D) path-following controller for airships in the presence of wind disturbances, which explicitly considers that wind speed is time-varying. The main idea is to formulate airship path-following as control design for systems in the presence of parametric uncertainties and external disturbances. Assuming that there is no prior information on wind, the proposed solution is based on the [Formula: see text] adaptive controller. This approach makes clear statements for performance specifications of the controller and relaxes the common assumption that wind speed is constant. This makes the design more realistic and the analysis more rigorous, because in practice, the wind speed may be time-varying. The results of the simulation indicate that the path following system has a good performance and is robust against wind disturbances.

Automatic tracking method for submarine cables and pipelines of AUV based on side scan sonar

Submarine cables and pipelines laid on the seafloor are critical infrastructures for energy transportation and communication transmission and must be inspected in a timely manner to determine maintenance needs. Autonomous inspection of cables (including pipelines) and their surroundings using underwater vehicles is difficult because of sensor myopia and deviations in cable routing. This study proposes an automatic cable-tracking method (ACTM) based on side-scan sonar (SSS) that endows autonomous underwater vehicles (AUVs) with autonomous decision-making capabilities to achieve fast localization and stable tracking of submarine cables with large uncertainties. Compared with the traditional predefined waypoint method, the proposed method decomposes the tasks into a series of independent behaviors according to operational objectives; thus, the AUV can perform online dynamic task replanning and realize an autonomous coverage search of the target region. When a cable is detected, the cable-tracking task is constructed as a path-following control problem on the horizontal plane. To reduce uncertainties caused by environmental noise and sensor measurement errors, a uniform motion model based on target tracking theory was used to model the virtual mass abstracted from the cable, and the states of the cable were estimated using the Kalman filter. Subsequently, an adaptive line-of-sight guidance algorithm for the SSS is established to dynamically adjust the look-ahead distance by introducing states such as cable curvature and angular change rate, which somewhat alleviates the tracking loss caused by the SSS short-sightedness. The proposed ACTM effectiveness and robustness were validated through numerical simulations and field tests. The results showed that the proposed method can achieve automatic search and adaptive tracking of cables, further reducing the role of humans in the task.

Robust Switched Velocity-Dependent Path-Following Control for Autonomous Ground Vehicles

This paper proposes a switched velocity-dependent path-following control method for autonomous ground vehicles under uncertain cornering stiffness and time-varying velocity. A switched polytopic linear-parameter-varying (LPV) model combining path-following and vehicle lateral dynamics is established, where the velocity-dependent parameters are divided into several switched regions. In each region, a trapezoidal polytope is adopted to describe the varying parameters. A global <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty}$</tex-math> </inline-formula> performance analysis is carried out for the switched polytopic LPV vehicle system based on the average dwell time approach with multiple Lyapunov functions. Based on the analysis, a switched velocity-dependent controller is synthesized to achieve a desired path-following performance by regulating the steering angle of the front wheels. The proposed switched path-following control specializes in handling large range of varying velocity, and achieves superior path-following performance compared with the conventional gain-scheduling control. Simulations are carried out for typical road maneuvers to illustrate the advantages of the proposed method. The results are also applicable to other LPV analysis and synthesis conditions involving time-varying parameters of large varying ranges.

Potential field-based dual heuristic programming for path-following and obstacle avoidance of wheeled mobile robots

AbstractPath-following control of wheeled mobile robots has been a crucial research topic in robotic control theory and applications. In path-following control with obstacles, the path-following control and collision avoidance goals might be conflicting, making it challenging to obtain near-optimal solutions for path-following control and obstacle avoidance with low tracking error and input energy consumption. To address this problem, we propose a potential field-based dual heuristic programming (P-DHP) algorithm with an actor–critic (AC) structure for path-following control of mobile robots with obstacle avoidance. In the proposed P-DHP, the path-following control and collision avoidance problems are decoupled into two ones to resolve the control conflict. Firstly, a neural network-based AC is constructed to approximate the near-optimal path-following control policy in a no-obstacle environment. Then, with the trained path-following control policy fixed, a potential field-based control policy structure is constructed by another AC network to generate opposite control forces as the robot moves toward the obstacle, which can guarantee the robot’s control safety and reduce the tracking error and input energy consumption in obstacle avoidance. The simulated and experimental results show that P-DHP can realize near-optimal path-following control with the satisfaction of safety constraints and outperforms state-of-the-art approaches in control performance.

Improved iterative learning path-following control for USV via the potential-based DVS guidance

Considering navigation scenarios in elaborate waters, the high-precision waypoints-based path-following problem of underactuated surface vessel (USV) is investigated by combining the iterative learning control (ILC) and the artificial potential field (APF) in this note. A potential-based dynamic virtual ship (DVS) guidance, considering the challenge of preventing collisions with irregular barriers, is designed with the path following module and the collision avoidance module. A virtual potential field is constructed in the guidance strategy to provide the desired heading signal In particular, the desired heading signal will force that to navigate to one secure location, where the USV approaches the barrier. On the basis of guidance, the iterative control algorithm is selected to design the controller of the system due to the advantages of high accuracy. The dynamic surface control (DSC) and the robust neural damping techniques are utilized to constraint the intrinsic problem of “explosion of complexity” in ILC. The merits of the method involve concise form and controllable precision. Through the Lyapunov criterion, the corresponding closed-loop system is with the semi-global uniformly ultimately bounded (SGUUB) stability. The simulation results are illustrated to verify the effectiveness of the proposed algorithm.

Design and Simulation-Based Optimization of an Intelligent Autonomous Cruise Control System

Significant progress has recently been made in transportation automation to alleviate human faults in traffic flow. Recent breakthroughs in artificial intelligence have provided justification for replacing human drivers with digital control systems. This paper proposes the design of a self-adaptive real-time cruise control system to enable path-following control of autonomous ground vehicles so that a self-driving car can drive along a road while following a lead vehicle. To achieve the cooperative objectives, we use a multi-agent deep reinforcement learning (MADRL) technique, including one agent to control the acceleration and another agent to operate the steering control. Since the steering of an autonomous automobile could be adjusted by a stepper motor, a well-known DQN agent is considered to provide the discrete angle values for the closed-loop lateral control. We performed a simulation-based analysis to evaluate the efficacy of the proposed MADRL path following control for autonomous vehicles (AVs). Moreover, we carried out a thorough comparison with two state-of-the-art controllers to examine the accuracy and effectiveness of our proposed control system.

Continuous-Time Receding-Horizon Estimation via Primal-Dual Dynamics on Vehicle Path-Following Control

In vehicle control, state estimation is essential even as the sensor accuracy improves with technological development. One of the vehicle estimation methods is receding-horizon estimation (RHE), which uses a past series of the measured state and input of the plant, and determines the estimated states based on linear or quadratic programming. It is known that RHE can estimate the vehicular state to which the extended Kalman filter cannot be applied owing to modeling errors. This study proposes a new computational form of the RHE based on primal-dual dynamics. The proposed form is expressed by a dynamic system; therefore, we can consider the computational stability based on the dynamic system theory. In this study, we propose a continuous-time representation of the RHE algorithm and redundant filters to improve the convergence performance of the estimation and demonstrate its effectiveness through a vehicle path-following control problem.

A path following controller for deep-sea mining vehicles considering slip control and random resistance based on improved deep deterministic policy gradient

This study aimed to develop a deep-sea mining vehicle (DSMV) path-following controller that could better reflect the actual deep-sea mining conditions. First, the dynamic model of the DSMV was improved. By introducing a nonlinear slip-control model and random environmental noise resistance, the controlled plant was developed to be closer to the actual mining operation condition. Second, an improved deep deterministic policy gradient (IDDPG) algorithm was proposed. Compared to the standard DDPG algorithm, the improved algorithm reduces the overestimation of the Q value and enhances the ability of an agent to explore the global optimum. A warm-up stage was introduced to improve stability at the beginning of training and accelerate the convergence speed of training. Third, a general reward function was designed for this type of problem. Combined with the uncertainty of the improved model, the generalization ability and adaptability to the unknown environment of the controller could be improved. Finally, through a random one-point-following training test in the simulation environment and different path-following comparison tests, the path-following control ability of the controller was verified.

DDPG-based active disturbance rejection 3D path-following control for powered parafoil under wind disturbances

DDPG-based active disturbance rejection 3D path-following control for powered parafoil under wind disturbances

Path-Following Control of Small Fixed-Wing UAVs under Wind Disturbance

Aiming at the problems of low following accuracy and weak anti-disturbance ability in the three-dimensional path-following control of small fixed-wing Unmanned Aerial Vehicles (UAV), a Globally Stable Integral Sliding Mode Radial Basis Function S-Plane (GSISM+RBF S-Plane) controller is designed. The controller adopts the inner and outer loop mode, the outer loop adopts the Globally Stable Integral Sliding Mode (GSISM) control, and the inner loop adopts the S-Plane control. At the same time, the unknown disturbance in the model is estimated via an RBF neural network. Firstly, the outer loop controller is designed based on the GSISM, and its stability is proved using the Lyapunov theory. Then, the S-Plane controller is designed for the instruction signal of the inner loop. Considering the complexity of the derivation in the S-Plane controller, a second-order differentiator is introduced. Finally, considering the problem of external wind disturbance, the controller is modeled, studied, and processed in order to better reflect the impact of real external wind on UAV path following. Finally, the Globally Stable Sliding Mode (GSSM) control and Globally Stable Integral Sliding Mode S-Plane (GSISM S-Plane) control are used for a comparative experiment. The simulation results show that the designed GSISM+RBF S-Plane controller can accurately track the ideal path compared with the GSSM and GSISM S-Plane controller, and it has good control performance and anti-disturbance performance.

Learning-Based $H_\infty$ Path-Following Controller Design for Autonomous Ground Vehicles Subject to Stochastic Delays and Actuator Constraints

In order to decrease computation loads for path following control of autonomous ground vehicles (AGVs), in this article, we aim to design an output-feedback path following controller by using a learning-based least squares support vector machine (LS-SVM) model. The signal transmission delay, which would deteriorates the control performance, is considered as well. First, the LS-SVM model for vehicle path following system is trained. Then, the output-feedback controller subject to stochastic communication delay, which meets the stability condition, is designed for the LS-SVM model. The modeling error and bias terms of LS-SVM model are regarded as the disturbance term. In order to attenuate the effect of disturbance, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> controller is designed for the AGVs to track the target path. Grey wolf optimizer is employed to solve the controller design problem with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance and transform stability condition from bilinear matrix inequality to linear matrix inequality (LMI). Then, we employ efficient LMI toolbox to solve the LMI. To guarantee the effectiveness of proposed controller, characteristics of steering wheel in a hardware-in-loop platform are studied and rate of steering angle is regarded as the actuator constraints for designing controller. Experimental results based on the hardware-in-loop platform have verified the effectiveness of the proposed control strategy.

Fixed-Time and Fault-Tolerant Path-Following Control for Autonomous Vehicles With Unknown Parameters Subject to Prescribed Performance

With the consideration of actuator faults, including the unknown steering mechanism misalignments and motor traction losses, this article presents a fixed-time control protocol to follow reference paths and velocities for autonomous ground vehicles (AGVs) with preset performance constraints. To provide sufficient large boundaries for the initial states, the hyperbolic tangent function is employed to predefine the constraints with respect to the path-following and velocity control performance. Based on the homeomorphic mapping and barrier Lyapunov theorem, the fixed-time prescribed performance control (PPC) objective-integrated fault-tolerant scheme can be achieved for the controlled AGV. In comparison to three different fixed-time controllers without the fault-tolerant or PPC scheme, the hardware-in-the-loop (HIL) test results demonstrate that the proposed control protocol can always provide superior control performance for the AGV under various maneuvering conditions.

Attack parameter dependent resilient [formula omitted] path-following control design for autonomous vehicles under cyber attacks

Autonomous vehicles typically transmit information over wireless communication network, which is vulnerable to malicious cyber attacks and poses huge security threats for autonomous vehicles. To address these challenges, this paper is concerned with resilient H∞ path-following control problem for autonomous vehicles under false data injection (FDI) attacks and denial-of-service (DoS) attacks, simultaneously. Firstly, a novel observer is constructed to estimate both sensor FDI attack signal and system state. An attack parameter dependent time-varying piecewise Lyapunov function is constructed to ensure stability of the underlying system. Then, a resilient H∞ path-following control scheme is developed to suppress the impact of FDI attacks and external interference despite the presence of intermittent DoS attacks. The design criterion of resilient controller is derived to assure the exponential stability of the closed-loop system and maintain the desired H∞ performance. Finally, simulation studies are carried out to verify the efficiency and merits of the developed results.

Reinforcement Learning-Based Path Following Control with Dynamics Randomization for Parametric Uncertainties in Autonomous Driving

Reinforcement learning-based controllers for safety-critical applications, such as autonomous driving, are typically trained in simulation, where a vehicle model is provided during the learning process. However, an inaccurate parameterization of the vehicle model used for training heavily influences the performance of the reinforcement learning agent during execution. This inaccuracy is either caused by changes due to environmental influences or by falsely estimated vehicle parameters. In this work, we present our approach of combining dynamics randomization with reinforcement learning to overcome this issue for a path-following control task of an autonomous and over-actuated robotic vehicle. We train three independent agents, where each agent experiences randomization for a different vehicle dynamics parameter, i.e., the mass, the yaw inertia, and the road-tire friction. We randomize the parameters uniformly within predefined ranges to enable the agents to learn an equally robust control behavior for all possible parameter values. Finally, in a simulation study, we compare the performance of the agents trained with dynamics randomization to the performance of an agent trained with the nominal parameter values. Simulation results demonstrate that the former agents obtain a higher level of robustness against model uncertainties and varying environmental conditions than the latter agent trained with nominal vehicle parameter values.

Fast Finite-Time Path-Following Control for Autonomous Vehicle via Complete Model-Free Approach

Without any knowledge of the vehicle model and its parameters, a novel complete model-free path-following control strategy is developed for autonomous vehicles based on the time-delay estimation (TDE) technique. Different from the existing time-delay control (TDC) approaches, an adaptive nonsingular terminal sliding mode (ANTSM) control law is designed to stabilize the path-following errors without any information of the suitable control gain, which is significant in the conventional TDC scheme, and the boundary of the TDE error, which is necessary for the sliding-mode-based control scheme. The proposed model-free control structure can dynamically update the gain of the designed controller and the boundaries of the TDE error, and the practical finite-time convergence of the preview error can be achieved. In the HIL tests, the comparative results demonstrate that the proposed ANTSM model-free control strategy can provide superior comprehensive tracking performance over both the model-based sliding mode controller and the conventional TDC controller, while the autonomous vehicle follows desired paths.

3-D Path-Following Control for Steerable Needles With Fiber Bragg Gratings in Multi-Core Fibers.

In this study, a closed-loop, 3-D path-following controller for steerable needles is presented. The control loop is closed via the feedback from FBG-inscribed MCFs embedded within the needle. The nonlinear guidance law, which is a well-known approach for path-following control of aerial vehicles, is used as the basis for the guidance method. To handle needle-tissue interactions, we propose using Active Disturbance Rejection Control (ADRC) because of its robustness within hard-to-model environments. We investigate both linear and nonlinear ADRC, and validate the approach with a Programmable Bevel-tip Steerable Needle (PBN) in both phantom tissue and ex vivo brain, with some of the experiments involving moving targets. The mean, standard deviation, and maximum absolute position errors are observed to be 1.79 mm, 1.04 mm, and 5.84 mm, respectively, for 3-D, 120 mm deep, path-following experiments. MCFs with FBGs are a promising technology for autonomous steerable needle navigation, as demonstrated here on PBNs. FBGs in MCFs can be used to provide effective feedback in path-following controllers for steerable needles.

Topological Analysis of Vector-Field Guided Path Following on Manifolds

A path-following control algorithm enables a system’s trajectories under its guidance to converge to and evolve along a given geometric desired path. There exist various such algorithms, but many of them can only guarantee local convergence to the desired path in its neighborhood. In contrast, the control algorithms using a well-designed guiding vector field can ensure almost global convergence of trajectories to the desired path; here, “almost” means that in some cases, a measure-zero set of trajectories converge to the singular set where the vector field becomes zero (with all other trajectories converging to the desired path). In this article, we first generalize the guiding vector field from the Euclidean space to a general smooth Riemannian manifold. This generalization can deal with path-following in some abstract configuration space (such as robot arm joint space). Then, we show several theoretical results from a topological viewpoint. Specifically, we are motivated by the observation that singular points of the guiding vector field exist in many examples where the desired path is homeomorphic to the unit circle, but it is unknown whether the existence of singular points always holds in general (i.e., is inherent in the topology of the desired path). In the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional Euclidean space, we provide an affirmative answer, and conclude that it is not possible to guarantee global convergence to desired paths that are homeomorphic to the unit circle. Furthermore, we show that there always exist nonpath-converging trajectories (i.e., trajectories that do not converge to the desired path) starting from the boundary of a ball containing the desired path in an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional Euclidean space where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n \geq 3$</tex-math></inline-formula> . Examples are provided to illustrate the theoretical results.

On the global dynamics of path-following control of automated passenger vehicles

The nonlinear dynamics of the path-following control of passenger cars is analyzed in this paper. The effect of specific modeling aspects, such as tire deformation, steering dynamics, feedback delay and controller saturation, is considered. Possible equilibrium points and singularities in the state space are uncovered and analyzed for different vehicle model and controller designs. The equilibrium of stable path following is then analyzed in greater detail: The domains of stabilizing control gains are presented in stability charts and the basin of attraction of the equilibrium along the stable domain is approximated with the help of numerical continuation. Unsafe zones of control gains are highlighted, where the stable equilibrium is surrounded by low-amplitude unstable limit cycles. Finally, it is shown how specific modifications of the control law can remove unwanted equilibrium points and increase the basin of attraction of stable path following, resulting in safer and more reliable control of the vehicle.

Improved LVS guidance and path-following control for unmanned sailboat robot with the minimum triggered setting

This paper presents a dynamic event-triggered control algorithm for the unmanned sailboat robot (USR) to carry out the waypoints-based path-following mission. The proposed scheme consists of the guidance module and control one. For the guidance module, the improved logic virtual ship (LVS) guidance law is proposed to plan the reference route for the USR considering the marine environment with time-varying wind direction. The navigation zones for USR are divided into crosswind zone and non-crosswind zone. It is beneficial for the sailboats to achieve tracking and gybe in different navigation zones. As for the control part, the dynamic event-triggered control is proposed by setting tuning threshold parameter. It is advantageous to decrease the resource waste and channel occupancy. Compared with the existing event-triggered control with static threshold parameter, the proposed algorithm is with the better performance in data transmission. The closed-loop control system has been proved to be with the semi-global uniform ultimate bounded (SGUUB) stability. Finally, the simulation experiment illustrates the superiorities of the proposed algorithm.

Path driven formation-containment control of multiple UAVs: A path-following framework

In this article, a formation-containment control problem of networked unmanned aerial vehicles (UAVs) guided by multiple leaders evolving along implicit paths is addressed, comprising of a two-layer control design. At the leader layer, by formulating a novel collaborative error associated with projective arc length rather than relative distance, a special path-following control law with characteristics of spatial-temporal decoupling is proposed to allow for a predetermined formation with a desired spacing, successfully evading the unnecessary transient maneuvering behaviors. At the follower layer, under the premise of neighboring interaction, a containment controller is presented that enables several UAVs to enter into the convex hull formed by the leaders. Moreover, unknown system dynamics estimators (USDEs) are elaborated in both layers to accommodate the wind perturbations based on ideal invariant manifold and straightforward filtering operations. The prominent merits are twofold, one is that formation-containment issue is firstly resolved in path-following paradigm, where multiple leaders are cooperatively deployed along the predefined curves without temporal constraints and a consensus in velocity can be achieved without considering uncertainties. Another is that higher adaptiveness against time-varying wind can be implemented using a concise filtering without resorting to prevailing disturbance observers. The input-to-state stability of entire system is proved according to Lyapunov principles. Finally, the efficacy and superiority of suggested method are validated by simulations.