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This paper presents a new control method for autonomous vehicles. The design goal is to perform the automatic lane keeping under multiple system constraints, namely actuator saturation of the steering system, roads with unknown curvature and uncertain lateral wind force. Such system constraints are explicitly taken into account in the control desig...
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Context 1
... order to investigate the vehicle motions and to evaluate the control perfor- mance, the vehicle handling dynamics in the horizontal plane are represented by the non-linear single track vehicle model [2,14], see Figure 1. Then, the vehicle dynamics is given by ...
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... lane-keeping dynamics can be represented via two supplementary mea- surements provided by the vision system [2], namely the lateral deviation error y L from the centerline of the lane projected forward a lookahead distance l s , and the heading error ψ L between the tangent to the road and the vehicle orientation, see Figure 1. Then, the dynamics representing the vehicle positioning on the road is given by ...
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... experiment aims to show the lane keeping performance of the proposed T-S fuzzy controller during the whole Satory test track, see Figure 10 (a). Fig- ures 10 (b), (c), and (d) show respectively the road curvature of Satory test track, the vehicle speed for this test, and the designed steering control signal. ...
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... experiment aims to show the lane keeping performance of the proposed T-S fuzzy controller during the whole Satory test track, see Figure 10 (a). Fig- ures 10 (b), (c), and (d) show respectively the road curvature of Satory test track, the vehicle speed for this test, and the designed steering control signal. Despite a large variation of vehicle speed, it can be observed from the vehicle variables rep- resenting the lane keeping performance in Figure 11 that the proposed non-PDC controller guarantees a good control performance for the whole test with small lane keeping errors. ...
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... ures 10 (b), (c), and (d) show respectively the road curvature of Satory test track, the vehicle speed for this test, and the designed steering control signal. Despite a large variation of vehicle speed, it can be observed from the vehicle variables rep- resenting the lane keeping performance in Figure 11 that the proposed non-PDC controller guarantees a good control performance for the whole test with small lane keeping errors. In particular, for the first four curves although the vehicle speed is different between Scenarios 2 and 5, the vehicle responses (steering angle and ve- hicle variables) obtained for both cases are rather similar. ...
Context 6
... order to investigate the vehicle motions and to evaluate the control performance, the vehicle handling dynamics in the horizontal plane are represented by the non-linear single track vehicle model [2,14], see Figure 1. Then, the vehicle dynamics is given by ...
Context 7
... lane-keeping dynamics can be represented via two supplementary measurements provided by the vision system [2], namely the lateral deviation error y L from the centerline of the lane projected forward a lookahead distance l s , and the heading error ψ L between the tangent to the road and the vehicle orientation, see Figure 1. Then, the dynamics representing the vehicle positioning on the road is given by ...
Context 8
... experiment aims to show the lane keeping performance of the proposed T-S fuzzy controller during the whole Satory test track, see Figure 10 (a). Fig- ures 10 (b), (c), and (d) show respectively the road curvature of Satory test track, the vehicle speed for this test, and the designed steering control signal. ...
Context 9
... experiment aims to show the lane keeping performance of the proposed T-S fuzzy controller during the whole Satory test track, see Figure 10 (a). Fig- ures 10 (b), (c), and (d) show respectively the road curvature of Satory test track, the vehicle speed for this test, and the designed steering control signal. Despite a large variation of vehicle speed, it can be observed from the vehicle variables representing the lane keeping performance in Figure 11 that the proposed non-PDC controller guarantees a good control performance for the whole test with small lane keeping errors. ...
Context 10
... ures 10 (b), (c), and (d) show respectively the road curvature of Satory test track, the vehicle speed for this test, and the designed steering control signal. Despite a large variation of vehicle speed, it can be observed from the vehicle variables representing the lane keeping performance in Figure 11 that the proposed non-PDC controller guarantees a good control performance for the whole test with small lane keeping errors. In particular, for the first four curves although the vehicle speed is different between Scenarios 2 and 5, the vehicle responses (steering angle and vehicle variables) obtained for both cases are rather similar. ...
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Citations
... The related symbols in Fig. 1 are given in Table I. The nonlinear vehicle dynamics model is considered as [31]: ...
This paper focuses on the security tracking control problem of nonlinear autonomous ground vehicles (AGVs) under Denial-of-Service (DoS) attacks. In order to resist DoS attacks and other external disturbances, a new security control scheme is proposed. Firstly, the Takagi-Sugeno (T-S) fuzzy system is established to represent the nonlinear AGV system under DoS attacks. In particular, in order to improve the flexibility of control and reduce the impact of DoS attacks on tracking control, a novel controller with a specific switching mechanism is designed. The switching mechanism relies on fuzzy membership functions, which can make better use of nonlinear vehicle speed information. Secondly, the sufficient conditions to ensure global exponential stability of the T-S fuzzy-based switched system are given by using the Lyapunov function method. Finally, the simulation platform built by Carsim and Matlab/Simulink is used to verify the effectiveness of the proposed control scheme
... A nonlinear single track model is used to represent the vehicle motion in the horizontal plane, see Fig. 1. This model captures the essential vehicle dynamics, described as [28] M ...
... To derive the observer-based vehicle model, we assume that [28], [29]: (i) the vehicle speed is a time-varying parameter; (ii) the lateral tires forces are proportional to the slip angles of each axle; (iii) the small angle assumption is considered. Note that these assumptions are appropriate for normal driving under mild acceleration conditions. ...
... Note that condition (28) implies that P (h) 0, and ...
This paper proposes a method for simultaneous estimation of both the driver torque and the sideslip angle within the context of human-machine shared driving control for autonomous ground vehicles. To this end, the driver torque is considered as an unknown input (UI) and the sideslip angle is an unmeasured state of the vehicle dynamics system. For simultaneous estimation purpose, a decoupling-based technique is leveraged to design an unknown input observer (UIO). The UIO design goal is to decouple the effect of the unknown driver torque while minimizing the influence of the modeling uncertainties, considered as unknown exogenous disturbances, from the lateral tires forces and the steering system. Linear parameter-varying (LPV) framework is used to deal with the time-varying nature of the vehicle longitudinal speed. Based on Lyapunov stability theory, we derive sufficient conditions, expressed in terms of linear matrix inequality (LMI) constraints, for LPV unknown input observer design. The simultaneous vehicle estimation is reformulated as a convex optimization problem, where the modeling uncertainty influence can be minimized via the
$\ell_\infty-$
gain performance. Hardware-in-the-loop (HiL) tests are performed with the SHERPA dynamic simulator and a human driver to show the effectiveness of the proposed UIO-based estimation method, especially within the cooperative driving control framework.
Note to Practitioners
—We present a method to jointly estimate the driver torque and the sideslip angle in the context of human-machine shared driving. To this end, we consider the driver torque as an unknown input and treat the sideslip angle as an unmeasured state of the vehicle dynamics system. The core of our method lies in the application of a decoupling-based technique to design an unknown input observer. The primary objective of this UIO is to effectively decouple the influence of the unknown driver torque while mitigating the impact of modeling uncertainties, considered as unknown exogenous disturbances, on the lateral tire forces and the steering system. Using an LPV framework has allowed the time-varying nature of the vehicle longitudinal velocity to be effectively addressed. Via Lyapunov stability theory, we have established sufficient conditions, expressed in terms of LMI constraints, for the design of the LPV unknown input observer. The proposed simultaneous vehicle estimation method has been reformulated as a convex optimization problem, allowing to minimize the influence of modeling uncertainties. To show the effectiveness of the proposed UIO-based estimation method, we have conducted extensive HiL tests using the SHERPA dynamic simulator with a human driver. The real-time experiments demonstrate the effectiveness of the proposed method, especially with respect to related estimation results in the literature, within the cooperative driving control framework. The proposed LPV estimation method contributes to the advancement of the field of autonomous ground vehicles by providing practitioners with a robust tool for joint estimation of essential variables critical for effective vehicle control and safety in the context of human-machine cooperative driving.
... In recent times, the development of modern intelligent vehicles (IV) has been greatly propelled by the synergy of cutting-edge hardware, software, and artificial intelligence [1], [2]. Notably, autonomous/automated driving systems (ADS), the backbone technology of IV, have gained significant attention due to their numerous societal and economic benefits [3]. Among the foundational subsystems of ADS, the path-following control design has emerged as a critical research area. ...
The application of adaptive control techniques in the development of control systems for intelligent vehicles, especially for ground vehicle path-following controllers, has gained popularity due to their ability to handle large-scale parametric uncertainties. However, the use of a standard quadratic Lyapunov function in existing adaptive control-based path-following controllers can lead to poor transient performance, such as slow convergence and/or large overshoot. To address this limitation, this study proposes the use of a switching non-quadratic Lyapunov function to design a model reference adaptive path-following controller that aims to provide superior transient performance. The stability and signal convergence of the closed-loop system are demonstrated through a Lyapunov-like analysis. Through dSPACE ASM simulation, the effectiveness of the proposed controller is illustrated, which confirms improved tracking performance over a baseline solution.
... As discussed in the earlier section, a SMC with sliding surface notion is generally considered to have a certain degree of robustness toward the uncertainty and disturbances [22][23][24][25][52][53][54][55][56][57][58][59][60]. Additionally, to achieve this robustness, it has to deal with the chattering issue caused during the sliding condition on the surface, as shown in Fig. 4. The problem of chattering has been addressed in a variety of ways throughout the years, among which a fuzzy logic-based method is one. ...
Maneuvering of autonomous vehicles primarily relies on trajectory tracking control techniques to self-drive at predefined steering angle with parametric uncertainty and external perturbation. The steering mechanism in autonomous navigation must be reliable and extremely effective, even in challenging circumstances for nonlinear trajectory tracking and lane changing of autonomous vehicles. From a control point of view, fuzzy quasi-sliding mode control methodology was adopted to attenuate the effect of model parametric uncertainties, tire cornering stiffness, road bonding coefficient, measurement noises, longitudinal velocity and exogenous disturbances. The robust tracking control incorporates quasi-sliding mode control that ensures the lateral motion and yaw dynamics with nominal operating parameters by compensating nonlinear dynamics with model uncertainty and fuzzy logic approach accomplishes balance between chattering alleviation and tracking accuracy. The control algorithm is model-free and requires no prior knowledge of the bounds of uncertainties in the vehicle’s dynamic parameters. Lyapunov theory is used to prove the closed loop tracking control scheme global asymptotic stability. Finally, the efficiency and reliability of the suggested control strategy is confirmed through simulation based on three test scenarios and experimental validation conducted on dSPACE SCALEXIO hardware-in-loop platform for nonlinear trajectory tracking with input constraints.
... Most previous studies on wireless multi-access technology have been conducted to improve traffic safety and the travel comfort of passengers. While Internet-of-Vehicles (IoVs) technology has focused on the operational efficiency of the central control system [1][2][3], vehicle-to-vehicle (V2V) communication technology has concentrated on the driving strategy of individual AVs [4][5][6][7][8]. V2V devices, alongside existing advanced driver assistant systems (ADAS), identify different sensor measurements for the same target to ensure reliable connectivity [9]. ...
... The unit is set as [1/s]. Here, the relative speed of the surrounding vehicle can be calculated using (7). ...
With the advent of autonomous vehicles (AVs) and advanced driving assistance systems (ADAS), there has been a growing interest in studying driving behaviors within the field of transportation science. Given that the transition period of mixed traffic is expected to continue for more than 30 years, it is crucial to evolve AV technology to resemble human driving, especially in the freeway weaving sections. Lane-changing (LC) maneuvers in these sections could cause problems for traffic flow, such as traffic breakdown, oscillation, or bottleneck activation. This study proposes an interpretable LC implementation model for naturalistic driving behaviors of AVs based on vehicle-to-vehicle (V2V) communication. To achieve this objective, a systematic selection process is adopted to find optimal V2V features that resemble how human drivers assess LC situations. Based on the minimum redundancy maximum relevance (mRMR) algorithm, seven V2V features have been selected out of 25 candidates. Then, a support vector machine (SVM) is employed to investigate how these features exhibit in each of LC and lane-keeping (LK) situations. The proposed model was applied in a field case of a weaving section on freeway US 101. Performance measures of simple accuracy, precision, recall, and F1-score show high accuracy of 0.9814, 0.9150, 0.7955, and 0.8511, respectively. Subsequently, a strategy for naturalistic LC behaviors of AVs was simulated. The proposed model outperforms high prediction accuracy compared to other existing models. Particularly, errors in the lateral movements have significantly improved. These results suggest that the proposed model effectively simulates naturalistic LC behaviors based on V2V communication.
... Using Euler approximation, we can obtain the discrete-time representation of the continuous-time representation of the vehicle nonlinear dynamics system Nguyen et al. (2018): ...
... T is the state vector, y v is the measurable output signal, z υ is the is the controlled output variable, the control input is the steering angle u(k) = γ, and the vector of external disturbance is ω(k) = wθ T . The parameters values for vehicle system is given in Nguyen et al. (2018). ...
... x and by using the sector nonlinearity approach Nguyen et al. (2018), the T-S fuzzy model of the vehicle nonlinear system 1.23 can be given by 2 3 = 8 local models. By considering the limits v xmin = 2 (m/S), and, v xmax = 32 (m/S). ...
This chapter presents a robust nonlinear \( H_\infty \) Static Output Feedback control (SOF) design for steering control of the autonomous vehicle under actuator saturation, external disturbances, and taking into account the unavailability of the sideslip angle. The Takagi-Sugeno (T-S) approach is used to model both the lateral and lane keeping dynamics of autonomous vehicles. Based on the non-quadratic Lyapunov approach, a static output Feedback controller based on non-compensation parallel distributed technic the \( H_\infty \) stabilization conditions without saturation are developed. Then, a polytopic representation of the actuator saturation is investigated to express the robust non-qudratiquc stabilisation conditions of Vehicle lateral dynamic control with input constraints in term of LMIs. Finally, the simulation results show that the proposed controllers can stabilize the closed-loop system and present great lane-keeping and external disturbance rejection performances.KeywordsAutonomous vehicleVehicle lateral dynamic controlTakagi-Sugeno (T-S) modelNon-quadratic fuzzy Lyapunov functionActuator saturationStatic output feedback control (SOF)\( H_{\infty } \) performance
... In each AV, several intelligent systems including sensors, hardware, and software are required to automatically control and navigate the vehicle satisfying the passenger's requirements. Some 1 Adaptive Cruise Control (ACC) [1], Collision Avoidance Systems (CAS) [2], Steering Control Systems (SCS) [3], and Lane Change Assist Systems (LCAS) [4]. The impact of these systems on the quality of driving is so deniable that most vehicle manufacturers try to install them in recent models of products. ...
Comfort in Autonomous Vehicles (AVs) is a decisive aspect and plays an essential role in their advanced driving systems. As the comfort is directly influenced by the amount of acceleration and deceleration, a smooth longitudinal driving strategy can significantly improve the passenger’s acceptance level. Although some safe longitudinal strategies such as time-headway are introduced for AVs, the breakpoints in their speed generation models when approaching the front vehicle made discomfort behavior. In this paper, we proposed a continuous and differentiable reference speed model with a single equation to cover all possible relative distances. This model is constructed based on the well-known attributes of a hyperbolic tangent curve to smoothly change the speed of the host vehicle at the corner points. Moreover, the adjustable variables in our reference speed generator make it possible to choose between low and high-accelerate driving strategies. The experiments are performed based on several driving scenarios such as stop-and-go, hard-stop, and normal driving, and the results are compared with different reference speed models. The maximum improvement is obtained in the stop-and-go scenario, and on average, about 7.29 and 12.47% are achieved in terms of the magnitude of acceleration and jerk, respectively.
... The nonlinear terms existing in the dynamic model Eq. (51) are ( x 2 2 ). Hence, a natural choice of premise variables to derive the T-S fuzzy representation μ = x 2 for x 2 ∈ [ −1 1 ] . ...
This paper provides a new non-quadratic stabilization conditions based on adaptive fuzzy observer for a class of Takagi-Sugeno (T-S) fuzzy systems subject to external disturbances and both actuator faults and saturation. Firstly, an observer based fault tolerant control (FTC) is proposed, not only to estimate both system states and actuator faults but also to compensate for the actuator faults and to stabilize the faulty system with input constraints. The saturation effect is transformed into dead-zone nonlinearity and the generalized sector bound condition is used to estimate the attraction domain. To less the conservatism of the quadratic Lyapunov technique, a proper integral structure based on the non-quadratic function is investigated. The
H∞ criteria is considered and the robust stabilization conditions of the faulty closed-loop system are expressed as a linear matrix inequalities (LMIs) optimization problem. Finally, the robustness and the advantages of the proposed approach are demonstrated through a mixed CSTR and a numerical example.
... Furthermore, inappropriate or early steering system intervention often leads to discomfort while driving, resulting in premature LKAS deactivation. To avoid undesired steering actuation, some researchers have dedicated efforts to improving lateral displacement control using various methods, including Linear Quadratic Regulator (LQR) [11], Proportional Integral Derivative (PID) [12], Fuzzy Control [13], End-to-End Learning [14], Robust h ∞ Controller [15], and Model Predictive Control (MPC) [1] [16]. ...
Lane Keeping Assist System (LKAS) enhances comfort and safety while driving. It plays a significant role in the Advanced Driver Assistance System (ADAS) and future Automated Driving (AD). The LKAS solution aims to help the driver keep the vehicle within the road lines, preventing unintentional lane departure. Despite LKAS being an important solution for comfortable driving, robust LKAS steering control is still lacking, requiring constant driver intervention or premature LKAS deactivation. LKAS require optimal control solutions with real-time constraints. This paper comprehensively analyzes Model Predictive Control (MPC) for real-time LKAS applications. Classical and parameterized MPC schemes with distinct Quadratic Programming (QP) solvers are combined to evaluate LKAS closed-loop control performance and real-time constraints. A sideslip and lateral speed bicycle modes were used to evaluate classical, trivial, and exponential MPC schemes. Experimental results highlight the three MPC and QP-appropriate solutions with satisfactory reference tracking without steering command and real-time constraints violation.
... Moreover, [Guechi et al. 2012] used flatness properties to design an output feedback controller of a unicycle-type vehicle while sliding mode theory is applied for the estimation of the delay of the measurements. The same theory is applied in [Nguyen, Sentouh, and Popieul 2018], where they demonstrated a constrained Takagi-Sugeno control method using fuzzy Lyapunov control framework for automatic lane keeping. The lab of Stanford, under the lead of Prof. Gerdes, presents the MPC approach for vehicle stabilization at the limits of handling. ...
This thesis deals with the problem of designing Linear Parameter Varying
(LPV)-based Gain-scheduling controllers for the lateral control system, needed for a passenger vehicle to steer automatically in autonomous mode. The main objective of this thesis is
to suggest an automatic steering system that provides safety for the passenger and sustain
comfort while performing fast maneuvers according to the reference trajectory. The proposed
lateral control system architectures are based-on the a) Polytopic and b) the Gridded parameter space approaches to design such LPV dynamic output feedback controllers. Subsequently,
a study is conducted to design a controller to avoid method’s conservatism issues, assure Hinfinity performance guarantees while taking into account the error tracking dynamics. The main scenarios of lateral control this work aims at tackling, are the lane-tracking and the
switching of lanes. At first is treated solely the lane-keeping problem for varying longitudinal
speed and then, the transition between these scenarios. In the LPV framework, this transition
is modeled to adapt the controller’s performance in real-time according to the treated scenario.
The same application is also formulated as a real-time optimization problem, called Reference
Governor, that feeds a virtual reference for which the gain-scheduled controller can handle
both tracking and switching lanes maneuvers and closed-loop state constraints are respected.
The proposed control architectures are validated at first on high-fidelity simulators for several
scenarios. Moreover, the embedded control code is deployed on an automated electric Renault
Zoe’s software and tested in a real test track for low-speed turns and high-velocity curves.
Thus, the suggested methods are validated through an analysis of the collected experimental
results and proving in that way the encouraging performance.
