Robust Lateral Control Research Articles

A Novel Robust H∞ Control Approach Based on Vehicle Lateral Dynamics for Practical Path Tracking Applications

This paper proposes a robust lateral control scheme for the path tracking of autonomous vehicles. Considering the discrepancies between the model parameters and the actual values of the vehicle and the fluctuation of parameters during driving, the norm-bounded uncertainty is utilized to deal with the uncertainty of model parameters. Because some state variables in the model are difficult to measure, an H∞ observer is designed to estimate state variables and provide accurate state information to improve the robustness of path tracking. An H∞ state feedback controller is proposed to suppress system nonlinearity and uncertainty and produce the desired steering wheel angle to solve the path tracking problem. A feedforward control is designed to deal with road curvature and further reduce tracking errors. In summary, a path tracking method with H∞ performance is established based on the linear matrix inequality (LMI) technique, and the gains in observer and controller can be obtained directly. The hardware-in-the-loop (HIL) test is built to validate the real-time processing performance of the proposed method to ensure excellent practical application potential, and the effectiveness of the proposed control method is validated through the utilization of urban road and highway scenes. The experimental results indicate that the suggested control approach can track the desired trajectory more precisely compared with the model predictive control (MPC) method and make tracking errors within a small range in both urban and highway scenarios.

Robust Lateral and Longitudinal Control for Vehicle Platoons with Unknown Interaction Topology Subject to Multiple Communication Delays

Robust Lateral and Longitudinal Control for Vehicle Platoons with Unknown Interaction Topology Subject to Multiple Communication Delays

Linear Active Disturbance Rejection Lateral Controller for Unmanned All-Terrain Vehicle

Abstract: To address the disturbance of model uncertainty, a linear active disturbance rejection controller (LADRC) was designed for robust lateral control of unmanned all-terrain vehicle. In terms of relative motion of target node and current state, first-order lateral tracking model is established. According to the developed model, linear tracking differentiator (LTD), linear extended state observer (LESO) and linear state error feedback (LSEF) are designed in turn. LESO could observe the uncertainty of system and LSEF could compensate the uncertainty to make system robust. In order to verify the effectiveness, two typical scenarios, circle and double lane tracking, were designed for test. And the uncertainties of wheelbase and steering ratio were considered. Results illustrate that the designed LADRC can stably control the unmanned all-terrain vehicle tracking reference trajectory under both scenarios and has the advantages of small tracking error and small overshoot compared with the conventional pure tracking methods.

Robust Discrete-Time Lateral Control of Racecars by Unknown Input Observers

This brief addresses the robust lateral control problem for self-driving racecars. It proposes a discrete-time estimation and control solution consisting of a delayed unknown input-state observer (UIO) and a robust tracking controller. Based on a nominal vehicle model, describing its motion with respect to a generic desired trajectory and requiring no information about the surrounding environment, the observer reconstructs the total force disturbance signal, resulting from imperfect knowledge of the time-varying tire-road interface characteristics, presence of other vehicles nearby, wind gusts, and other model uncertainty. Then, the controller actively compensates the estimated force and asymptotically steers the tracking error to zero. The brief also presents a closed-loop stability proof of the method, ensuring perfect asymptotic estimation and tracking by the controlled vehicle. The proposed solution advantageously needs no a-priori information about the total disturbance boundedness, additional variables to model uncertainty, or observer parameters to be tuned. Its effectiveness and superiority to existing methods are studied in theory and shown in simulations where a full racecar model, based on the vehicle dynamics blockset, is required to track aggressive maneuvers. Through a faster and more accurate disturbance estimation, the solution robustly ensures better dynamic responses even with measurement noise.

Emerging robust and data‐driven control methods for uncertain learning systems

Emerging robust and data‐driven control methods for uncertain learning systems

Robust lateral motion control of distributed drive vehicle considering long input delays

AbstractThis article presents a comparative study of lateral motion controller design for distributed drive vehicle. Aiming at handling long input delays, two types of robust controllers are specifically investigated. First, a Lyapunov‐Krasovskii functional based robust controller design is proposed. Considering input delay, the design conditions of the state‐feedback controller are derived in terms of linear matrix inequalities (LMIs), which can be effectively solved using LMI toolbox. Second, based on the same control law, a robust predictive controller design is further presented. However, different from the Lyapunov‐Krasovskii functional based robust controller, the control gain of the proposed robust predictive controller is calculated without considering the input delay. An input delay estimator and a predictor are developed instead to reconstruct the delay‐dependent state, which is further used to generate a new feedback error. Moreover, the feasibility of this control scheme is theoretically proved. Finally, extensive co‐simulations with Carsim and Matlab/Simulink are conducted to compare the performance between these two controllers. Compared with the Lyapunov‐Krasovskii functional based robust controller, longer input delay can be handled by using the proposed robust predictive controller.

Robust learning‐based lateral tracking control for autonomous driving with input constraints

This work investigates the robust lateral tracking control problem of autonomous vehicles subject to unmodeled system uncertainties, external disturbances as well as input constraints that commonly exist in the vehicle dynamics. The tracking controller design under such uncertain environments is challenging due to the under-actuated characteristics of the vehicle dynamics. Targeting at this issue, a nonlinear vehicle model is firstly established and transformed into a novel parametric model to facilitate the controller design. A robust adaptive learning control (RALC) approach is then proposed based on the parametric vehicle model, where an input-dependent auxiliary system is employed to compensate the influence of the input constraints. The convergence of the tracking errors is rigorously analyzed based on the framework of composite energy function. The proposed RALC scheme is proven to be able to achieve an impressive path tracking performance under perturbed and constrained scenarios. Moreover, the proposed technique in tackling the under-actuated dynamics is novel that can be applied to deal with other generic non-square systems. The proposed controller is validated with case studies under various conditions.

Gain-scheduling LPV synthesis H∞ robust lateral motion control for path following of autonomous vehicle via coordination of steering and braking

This paper proposes a robust gain-scheduling lateral motion control strategy via coordination of the active front-wheel steering (AFS) and direct yaw moment control (DYC) for path following of autonomous vehicle. Distinguishing from other relevant researches, a seamless application of the DYC is designed to reduce the influence of braking for the vehicle longitudinal dynamics. In order to achieve this objective, the weighting function with a self-scheduling parameter is designed. Based on the above, a novel LPV (Linear Parameter Varying) synthesis H∞ performances lateral motion controller is designed considering the vehicle longitudinal velocity as a time-varying parameter. The path following is realised through achieving a desired yaw rate, which is generated based on the path-following dynamics. The feedback gains can be derived by solving the LMI (Linear Matrix Inequalities). Furthermore, the physical limitations of the actuators (steer-by wire and brake-by-wire) are considered by weighting functions. Finally, experiments in the HIL system are carried out to verify the effectiveness and real-time performance of the proposed control strategy, and the results show that the proposed controller has good tracking capability under different manoeuvres and can reduce the possibility of actuator saturation and the influence of braking for the vehicle longitudinal dynamics.

Robust lateral and longitudinal stability control for delta three-wheeled vehicles with suspension system

Robust lateral and longitudinal stability control for delta three-wheeled vehicles with suspension system

H2/H∞ robust lateral control of an off-road two-steering-axle vehicle on slippery sloping soils

This paper presents an efficient design for a lateral control synthesis of an off-road vehicle. The considered vehicles have two steering axles and are intended to move on a slippery soil with important lateral and longitudinal slopes. The proposed design relies on an extended bicycle model that accounts for the slopes. The lateral control is designed based on a feed forward/feedback architecture. The feed forward takes advantage of the knowledge of the path characteristics (curvature and slopes), and the feedback ensures robust reference-trajectory tracking. Through an H2/H∞ multi-objective synthesis, the robustness of the lateral controller is ensured with regard to the model uncertainties, path, and soil features. This is important because off-road vehicles’ dynamics are by nature highly variable. To cope with the difficulty caused by uncertain knowledge of key parameters, the proposed robust approach is a practical compromise that maximizes performance under constraints of robustness. The results obtained on a realistic non-linear simulator support this assertion.

Robust Vehicle Dynamics Control for a Sharp Curve With Uncertain Road Condition

Recently, more and more research has been conducted to develop Connected Autonomous Vehicles (CAVs) applications that ensures the safety driving of CAVs under some extreme situations. This brief presents a robust control strategy for CAVs to preserve a precise tracking performance and maintain the stability of lateral dynamics when passing a sharp curve with uncertain road friction coefficient changes. In the proposed robust lateral dynamics control, robust optimization-based lateral dynamics controller is designed to achieve the stability of the lateral dynamics with the consideration of the road friction coefficient uncertainty. Simulation validations are carried out to evaluate the proposed control strategy. The results show that the robust optimization-based lateral dynamics can improve the robustness even with the uncertainty of the road friction coefficient.

Neural Network Based Robust Lateral Control for an Autonomous Vehicle

The lateral motion of an Automated Vehicle (AV) is highly affected by the model’s uncertainties and unknown external disturbances during its navigation in adverse environmental conditions. Among the variety of controllers, the sliding mode controller (SMC), known for its robustness towards disturbances, is considered to generate a robust control signal under uncertainties. However, conventional SMC suffers from the issue of high frequency oscillations, called chattering. To address the issue of chattering and reduce the effect of unknown external disturbances in the absence of precise model information, a radial basis function neural network (RBFNN) is employed to estimate the equivalent control. Further, a higher order sliding mode (HOSM) based switching control is proposed in this paper to compensate for the effect of external disturbances. The effectiveness of the proposed controller in terms of lane-keeping and lateral stability is demonstrated through simulation in a high-fidelity Carsim-Matlab Simulink environment under a variety of road and environmental conditions.

Robust lateral and longitudinal stability control for delta three-wheeled vehicles with suspension system

Robust lateral and longitudinal stability control for delta three-wheeled vehicles with suspension system

Robust Control Framework for Lateral Dynamics of Autonomous Vehicle Using Barrier Lyapunov Function

This article presents the robust lateral control of an autonomous vehicle in the presence of unknown lateral tire forces, road curvature angle, and parametric uncertainties. The sliding mode control (SMC) with barrier Lyapunov function is implemented to guarantee the system robustness while maintaining the outputs of the system in realistic bounds. Following the model reduction approach, the slow and fast dynamics of the system are separately controlled using the proposed control technique. The efficacy of the proposed control technique is examined by comparing the simulation results with conventional sliding mode control in two-time scales.

A robust lateral tracking control strategy for autonomous driving vehicles

A robust steering torque control strategy for lateral tracking functionality of autonomous driving vehicles that perform active steering, active accelerating and active braking is researched in this paper. The main target of this research is to track references of lateral position and heading angle, which are provided by upstream motion planning module, via torque that robotic arm applies to steering hand wheel. Firstly, a system with tracking errors is generated and analyzed. To keep control system’s robustness against time varying parameters, such as speed, center of mass, etc., gain-scheduling approach is utilized to obtain proper feedback gain. To achieve performance robustness, methods of linear matrix inequalities are used in design to maintain a performance function. Control performance is validated in Hardware in the loop environment built by ETAS labcar and Matlab/Simulink for a lane changing scenario and two typical parking scenarios. Results show that the proposed control strategy can regulate vehicle to follow target trajectory precisely even when speed varies. In lane changing scenario, steady state error is close to zero and maximum lateral position error is about ±30 cm. In parking scenarios, tracking error of lateral position is about ±3 cm, and of heading angle is about ±0.1 rad at end points. In addition, in comparison with linear quadratic regulator (LQR) and model predictive control (MPC), the proposed control outperformances in the three test scenarios.

Robust Lateral Stabilization Control of In-Wheel-Motor-Driven Mobile Robots via Active Disturbance Suppression Approach.

Due to the praiseworthy maneuverability and actuation flexibility, the in-wheel-motor-driven mobile robots (IWMD-MR) are widely employed in various industrial fields. However, the active estimation and rejection of unknown disturbances/uncertainties remain a tough work for formulating a stable lateral motion controller. To address the challenge, this paper proposes a robust lateral stabilization control (RLSC) scheme for the developed IWMD-MR by designing an active disturbance suppression mechanism. The distinctive features of the proposed RLSC method are threefold: (i) With a fuzzy estimator, a modified super-twisting sliding mode method is designed to eliminate the system perturbations and time-varying lumped disturbances in an active manner; (ii) The resultant system trajectory is forced into a bounded switching region within finite time, which can be maintained therein for subsequent periods; (iii) Employing the Lyapunov function, new adaption rules for multivariable gains are derived to preserve the lateral motion stability and robustness. Finally, under the direct yaw moment control framework, simulation experiments of real-life IWMD-MR are offered to verify the effectiveness of the presented RLSC method.

Robust lateral control of autonomous four-wheel independent drive electric vehicles considering the roll effects and actuator faults

This paper proposes a robust lateral control system with fuzzy state observer based on Takagi-Sugeno fuzzy model of autonomous four-wheel independent drive electric vehicles (A-4WDEVs) considering the roll effects and actuator faults. Firstly, a Takagi-Sugeno fuzzy model of A-4WDEVs with uncertainties, time-varying parameters and actuator faults is presented, which can describes the coupling lateral and roll dynamic features of A-4WDEVs. Then, a robust H∞ state feedback lateral control system of A-4WDEVs is proposed to deal with the characteristics of uncertainties and external disturbance and produce the desired front wheel steering angle and external yaw moment of A-4WDEVs. Next, the control allocation law of additional longitudinal tire forces of A-4WDEVs is reviewed as a quadratic programming problem to achieve the desired external yaw moment. Furthermore, a Takagi-Sugeno fuzzy observer is designed to estimate the roll angle and roll angle rate of A-4WDEVs in real time. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed control scheme.

Hydrodynamic Force Decoupling Using a Distributed Sensory System

In this letter, we present a distributed pressure sensory system, inspired by the lateral line found in fish, for estimating hydrodynamic forces acting on an autonomous underwater vehicle. By canceling these forces using the vehicle control system, the dynamics of the vehicle can be decoupled from the state of the surrounding fluid. We discuss a control scheme which combines the measured hydrodynamic forces with a feedback controller that is robust to disturbances and measurement errors, achieving asymptotic tracking in the presence of bounded disturbances and errors. We compare this robust lateral line controller to a baseline robust controller not using the lateral line system and to a controller using the lateral line with simple PD feedback. We simulate a spatially and time varying velocity flow field on the scale of the vehicle and show that the robust lateral line controller has improved performance compared to both the baseline robust controller and the PD controller using the lateral line system.

Robust Lateral Control of Intelligent Vehicle in the Human-machine Sharing Based on μ-synthesis

Robust Lateral Control of Intelligent Vehicle in the Human-machine Sharing Based on <i>μ</i>-synthesis

Robust Lateral Motion Control for In-Wheel-Motor-Drive Electric Vehicles With Network Induced Delays

In this article, a robust control scheme for an in-wheel-motor-drive electric vehicle (IWMD EV) is put forward to enhance vehicle lateral stability considering network-induced time delays. A robust sliding mode controller (RSMC) is devised, and the derived control law is partitioned into two portions, i.e., the continuous and discontinuous parts. A Linear-Quadratic-Regulator (LQR) problem with network-induced time delays is formulated with the objectives of minimizing the reference states tracking errors and reducing the control efforts. Then, it is transformed into an iterative solution derivation of a two-point boundary value problem without delays, and the derived solution is obtained and constitutes the continuous part of the control law. Meanwhile, the global sliding mode theory is applied to deriving the discontinuous part of the control law, which has robustness to vehicle parameters variation and modeling uncertainties. The proposed control scheme exhibits better performance in dealing with network-induced time delays compared with the original optimal LQR controller in simulation and Hardware-in-the-Loop (HIL) tests.