Longitudinal Velocity Control Research Articles

Longitudinal velocity control of autonomous driving based on extended state observer

Longitudinal velocity control of autonomous driving based on extended state observer

Application of dynamic desired headway based adaptive backstepping sliding mode control design to mixed traffic system

To optimize the mixed car-following behavior of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), an adaptive backstepping sliding mode control (ABSMC) strategy for longitudinal velocity and distance control model (namely, MCF-ABSMCM) is put forth. First, the variable desired headway model (VDHM) is designed based on the vehicle-to-vehicle and vehicle-to-infrastructure information. Also, an asymmetric stochastic car-following model (ASCM) is designed to realistically describe the stochastic factor as well as vehicle acceleration and braking behaviors in mixed car-following. Second, an adaptive cruise sliding mode control law with a backstepping approach is devised to precisely track the desired following distance based on the VDHM. After creating the basic diagram of the two-model traffic flow, the intelligent driver model (IDM) is used as a baseline to compare and analyze the traffic capacity. Lastly, the effectiveness of the MCF-ABSMCM approach is confirmed in terms of both the mixed traffic flow density wave evolution and the vehicle control strategy, and the MCF-ABSMCM is validated under various adversary inputs. The simulation results demonstrate that the mixed-flow queue can finish headway tracking and keep a safe distance when using the MCF-ABSMCM described. This control strategy has been validated in VISSIM to significantly improve the efficiency of traffic operations compared to IDM and ASCM.

Distributed Drive Autonomous Vehicle Trajectory Tracking Control Based on Multi-Agent Deep Reinforcement Learning

In order to enhance the trajectory tracking accuracy of distributed-driven intelligent vehicles, this paper formulates the tasks of torque output control for longitudinal dynamics and steering angle output control for lateral dynamics as Markov decision processes. To dissect the requirements of action output continuity for longitudinal and lateral control, this paper adopts the deep deterministic policy gradient algorithm (DDPG) for longitudinal velocity control and the deep Q-network algorithm (DQN) for lateral motion control. Multi-agent reinforcement learning methods are applied to the task of trajectory tracking in distributed-driven vehicle autonomous driving. By contrasting with two classical trajectory tracking control methods, the proposed approach in this paper is validated to exhibit superior trajectory tracking performance, ensuring that both longitudinal velocity deviation and lateral position deviation of the vehicle remain at lower levels. Compared with classical control methods, the maximum lateral position deviation is improved by up to 90.5% and the maximum longitudinal velocity deviation is improved by up to 97%. Furthermore, it demonstrates excellent generalization and high computational efficiency, and the running time can be reduced by up to 93.7%.

Projektovanje kretanja besposadnog guseničnog vozila po zadatoj putanji na osnovu ADRC regulatora i FIL simulacija

Introduction/purpose: The trajectory tracking control problem in an unmanned tracked vehicle (UTV) represents a challenging task, due to unknown and unmeasurable slippage dynamics which inevitably exists during movement. Therefore, the application of standard industrial control schemes is often limited. Methods: In this paper, an active disturbance rejection control (ADRC) scheme is proposed for the longitudinal (vehicle longitudinal velocity control) and lateral (vehicle course angle control) control channels of the UTV to collectively handle all the plant modeling uncertainties and acting slippage disturbances. Results: A step-by-step procedure for applying the ADRC algorithm for the specific case of UTV trajectory tracking is presented. It includes systematic design, discretization as well as performance analysis and validation utilizing FPGA-in-the-loop (FIL) simulations. Conclusions: The proposed FIL-based validation method reduces the gap between pure simulation design (which may be too idealized) and implementation on the real vehicle (which may be time-consuming). The obtained experimental results show the advantages of the proposed control structure over standard industrial PI/PID controllers in different working conditions.

Integrated control method for path tracking and lateral stability of distributed drive electric vehicles with extended Kalman filter–based tire cornering stiffness estimation

Aiming at the lack of adaptability of vehicle parameters under extreme conditions, this paper proposes an integrated control method for path tracking and lateral stability of distributed drive electric vehicles based on tire cornering stiffness adaptive model predictive control (MPC) scheme. The control method integrates active front steering and direct yaw control to improve the path tracking and lateral stability performance of distributed drive electric vehicles. Firstly, considering the influence of vertical load transfer, the tire cornering stiffness is estimated based on the extended Kalman filter (EKF) algorithm. Then, using this online updated tire cornering stiffness value, an adaptive MPC controller for path tracking and lateral motion stability of distributed drive electric vehicles is constructed. Meanwhile, a fuzzy sliding mode control (Fuzzy-SMC)–based longitudinal velocity controller is established to ensure the accuracy of velocity tracking. Also, according to the distributed driving characteristics of the controlled system, a tire torque distributor based on weighted pseudo-inverse (WPI) is designed with the minimum tire load rate as the optimization objective, where the road adhesion condition and the maximum output torque of the motor are considered as constraints. The simulation results show that the proposed integrated control method based on tire cornering stiffness adaptive model predictive control is robust and effective. Compared with constant cornering stiffness model predictive control–based control method, it can improve the vehicle path tracking accuracy and lateral motion stability under extreme conditions.

A Longitudinal Velocity CF-MPC Model for Connected and Automated Vehicle Platooning

To optimize a vehicle platoon system in terms of car-following behavior, a decentralized model predictive control (MPC) strategy for longitudinal velocity control was established (namely, CF-MPC). Firstly, considering the influence of car-following behavior on vehicle states, a longitudinal velocity control model for platoons of connected and automated vehicles (CAV) was designed. Based on that model, an upper-level MPC controller was built to obtain the desired acceleration of the vehicles. Secondly, a lower-level controller received the desired acceleration signal and converted it into the expected throttle opening/braking pressure, to control acceleration/deceleration. Then, the Lyapunov stability method was used to detect the stability conditions that the model should satisfy. Finally, three simulation procedures—constant speed, acceleration, and deceleration were tested, and the validity of the CF-MPC method was verified from the perspectives of a model strategy and a control strategy. The simulation results show that with the proposed CF-MPC method, CAV platoons quickly completed velocity tracking and maintained a safe distance, thereby improving traffic efficiency, fuel economy, driving safety, and transportation capacity.

Velocity-based Lateral Stability Control for Four-wheel Independently Actuated Electric Vehicles with Homogeneous Polynomial Approach

This paper presents a control strategy to enhance the lateral dynamics stability and handling performance of the four-wheel independently actuated (FWIA) electric vehicles (EVs). The vehicle longitudinal velocity uncertainty and controller saturation are considered, a double layers control scheme is adopted. In the upper layer, the homogeneous polynomial parameter-dependent approach is introduced to track the uncertainty problem, and a multi-objective controller is designed to obtain the desired external yaw moment. In the lower layer, an optimal force distribution method with considering the distribution error and tire workload is employed to allocate the desired external yaw moment into forces of the four in-wheel motors. Simulation results verify the effectiveness of the proposed control strategy.

Spectral feedback control of turbulent spectra in a wind tunnel

Longitudinal flow velocity control was conducted in the Andrew Fejer Unsteady Wind Tunnel to produce velocity spectra that emulate atmospheric turbulence. A louver mechanism placed at the downstream end of the test section determines the resistance to the flow by varying the louver deflection angle, and hence changes the test-section velocity. The test-section flow velocity was decomposed into an adjustable mean flow $$u_0$$ and a fluctuation $$u'$$ , which were respectively controlled by the mean louver deflection angle and the oscillations of the louvers about that mean angle. A linear interpolation of the static map of the test-section flow speed with respect to the louver deflection angle was used as an open-loop controller for the longitudinal flow velocity. A feedback loop that measures the velocity spectrum over a short time interval constructed a closed-loop controller, which controls the mean flow speed as well as the amplitudes over a range of individual frequencies within the fluctuation spectrum. The spectral feedback approach was tested by generating the longitudinal velocity components of von Karman turbulence spectra and Dryden turbulence spectra. The controller proved to be capable of controlling the gust frequency with a bandwidth of 7 Hz given the current hardware configuration.

Tire blow-out control for direct drive electric vehicles using reconfiguration of torque distribution and vertical load

This paper proposes a composite stability control strategy for tire blowout electric vehicle with explicit consideration of vertical load redistribution, subject to multi-constraints, uncertainty and redundant actuation. To readily implement system state and actuator increment constraints, a model predictive controller is designed to enhance vehicle stability performance and original lane keeping. Meanwhile, a sliding mode control-based longitudinal velocity controller is collaboratively proposed for maintaining the original longitudinal velocity after tire blowout in the freeway, which can reduce considerably the rear-end collision accidents. After that, a constrained weighting least square-based reconfigurable torque distributor is developed to realize the tracking of the virtual resultant yaw-moment and longitudinal tire force signals generated by the above controllers, considering system physical constraints and actuator capability simultaneously. Simulations with different tire blowout scenarios are conducted on the developed dynamic simulation platform to demonstrate the effectiveness of the designed control methods; furthermore, the influence of different tire blowout on the vehicle movement behaviors is discussed. Statistical results based on the system transient performance and control effort evaluating indictors highlight the considerable superiority of the developed model predictive controller compared with the linear quadratic regulator.

A game-theoretic framework for autonomous vehicles velocity control: Bridging microscopic differential games and macroscopic mean field games

This paper proposes an efficient computational framework for longitudinal velocity control of a large number of autonomous vehicles (AVs) and develops a traffic flow theory for AVs. Instead of hypothesizing explicitly how AVs drive, our goal is to design future AVs as rational, utility-optimizing agents that continuously select optimal velocity over a period of planning horizon. With a large number of interacting AVs, this design problem can become computationally intractable. This paper aims to tackle such a challenge by employing mean field approximation and deriving a mean field game (MFG) as the limiting differential game with an infinite number of agents. The proposed micro-macro model allows one to define individuals on a microscopic level as utility-optimizing agents while translating rich microscopic behaviors to macroscopic models. Different from existing studies on the application of MFG to traffic flow models, the present study offers a systematic framework to apply MFG to autonomous vehicle velocity control. The MFG-based AV controller is shown to mitigate traffic jam faster than the LWR-based controller. MFG also embodies classical traffic flow models with behavioral interpretation, thereby providing a new traffic flow theory for AVs.

Deterministic Promotion Reinforcement Learning Applied to Longitudinal Velocity Control for Automated Vehicles

Reinforcement learning is regarded as a potential method to be applied in automated vehicles, but the stability and efficiency of algorithms are concerns. To improve them, the deterministic promotion reinforcement learning method is put forward, which can promote the policy determinately. Correspondingly, the policy evaluation in critic and the exploration in actor are improved, which combines a normalization-based evaluation and a model-free search guide. The aim is finding the right action exploration direction by critic, then the direction is used to update and guide action exploration in actor. The modified method decreases the dependencies of exploring a good action for promotional updating and only makes deterministic promotion in policy. Consequently, the efficiency of the algorithm is improved without loss in stability. More notably, it can relieve the cold-start and circumvent the limitations in learning with constrained physical systems. To verify the proposed method, the longitudinal velocity control problem for automated vehicles is considered, which contains car-following and non-car-following conditions in a unitized form. The learning system is established in Carsim. Furthermore, some different reinforcement learning technologies are used to accelerate learning. Real-vehicle experiments for validation are also given. The results indicate that the proposed method can achieve permissible learning performance in the longitudinal velocity continuous control problem.

Hybrid modeling and predictive control of intelligent vehicle longitudinal velocity considering nonlinear tire dynamics

A hybrid model predictive control (HMPC) strategy is proposed in this paper to autonomously regulate intelligent vehicle longitudinal velocity considering nonlinear tire dynamics. Since the tire longitudinal dynamics, which has significant influence on vehicle longitudinal velocity control performance, exhibits highly nonlinear dynamical behaviors, the piecewise affine (PWA) identification is conducted firstly based on experimental data to accurately model the tire longitudinal dynamics. On this basis, due to that the intelligent vehicle needs to be operated in two distinct modes (drive and brake) for autonomous velocity regulation and because of the affine submodel switching behaviors of the PWA-identified tire model, the intelligent vehicle longitudinal dynamics control process considered in this work can be regarded as a hybrid system with both continuous variables and discrete operating modes. Thus, the mixed logical dynamical framework is further used to model the intelligent vehicle longitudinal dynamics, and a HMPC controller, which allows us to optimize the switching sequences of the operation modes (binary control inputs) and the torques acted on the wheels (continuous control inputs), is tuned based on online mixed-integer quadratic programming. Simulation results finally demonstrate the effectiveness of the proposed HMPC controller for intelligent vehicle longitudinal velocity regulation under typical driving conditions.

Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics

Longitudinal dynamics control is the basis for autonomous driving of intelligent vehicles, which have great significance to the development of intelligent transportation system (ITS). To solve the problems of traditional sliding mode control method when applied to intelligent vehicle longitudinal dynamics, such as large velocity tracking errors, strong chattering phenomenon and so on, a new sliding mode control strategy based on RBF (Radical Basis Function) neural network is presented in this paper. Firstly, a nonlinear mathematical model of the intelligent vehicle longitudinal motion is established by considering the dynamics of the engine, the torque converter, the automatic transmission and the brake system. On the basis of the system model, a variable structure control system with sliding mode is introduced to design a sliding mode variable controller with RBF neural network. This controller can adaptively adjust the switching gain and its stability is proved based on the Lyapunov theory. Finally, the effectiveness of the designed longitudinal velocity control strategy is verified by simulation under typical driving conditions. The simulation results show that the improved control algorithm can effectively suppress chattering, obtain the higher precision and stronger robustness than the traditional sliding mode control. Thus, the longitudinal motion control performance of intelligent vehicles is improved effectively.

Smart Longitudinal Velocity Control of Autonomous Vehicles in Interactions With Distracted Human-Driven Vehicles

With the development of commercialized autonomous vehicles (AVs), the interaction between AVs and human-driven vehicles has become increasingly important. Nevertheless, on the one hand, complex driver behaviors like distraction are hard to detect by AVs, which may lead to traffic accidents because of the late alert to the following vehicles. On the other hand, advanced techniques such as the real-time image or video processing and vehicle-to-vehicle (V2V) communications make it possible to let AVs receive monitoring signals from nearby vehicles, predict the latent risks, and make smart control to avoid the vehicles driven by distracted drivers. Hence, in this paper, we envisage a collaborative framework integrating human driver distraction monitoring, V2V communications, and AV velocity control. Then, we design the smart velocity control of AVs by taking into consideration the distraction behaviors of the drivers in the human-driven vehicles, and by formulating it as a feasible optimization problem based on model predictive control (MPC) strategies. Furthermore, we analyze the safety benefits that the collaborative framework could help improve on the condition of preserving traffic performance. Finally, we implement the contrast tests of real-time evaluation on driver distraction monitoring based on convolutional neural networks (CNNs) and perform simulations of smart velocity control strategies of the AV at avoiding the distracted driver and reducing rear-end collisions. Through the analysis and the simulations, we show our framework could increase the safety regions, reduce the rear-end collisions, and thus increase the safety of the whole transportation networks.

Stability Control for FWID-EVs With Supervision Mechanism in Critical Cornering Situations

This paper proposes a novel control strategy to improve the stability performance for four-wheel independent drive electric vehicles in critical cornering by introducing the supervision mechanism for yaw moment control and slip ratio regulation simultaneously. The proposed control strategy is a three-level structure: the first level is composed of a yaw moment controller and a longitudinal velocity controller; the second level is made up of a tyre force distributor; and the third level consists of a slip ratio regulator. To begin with, the supervision mechanism is developed to supervise vehicle driving states and determine whether the yaw moment controller should be activated. After that, the tyre force distributor is presented to assign the resultant force/moment generated by the first level to the four independent wheels, in which average distribution, dynamic load distribution, and optimal distribution schemes are separately discussed for comparison. Also, the slip ratio regulator is designed to regulate the slip ratio within the threshold. In the end, simulations performed on a nonlinear vehicle model with eight degree of freedom validated the effectiveness of the proposed control strategy and approaches.

Automatic landing system using neural networks and radio-technical subsystems

The paper focuses on the design of a new automatic landing system (ALS) in longitudinal plane; the new ALS controls the aircraft trajectory and longitudinal velocity. Aircraft control is achieved by means of a proportional-integral (PI) controller and the instrumental landing system – the first phase of landing (the glide slope) and a proportional-integral-derivative (PID) controller together with a radio-altimeter – the second phase of landing (the flare); both controllers modify the reference model associated with aircraft pitch angle. The control of the pitch angle and longitudinal velocity is performed by a neural network adaptive control system, based on the dynamic inversion concept, having the following as components: a linear dynamic compensator, a linear observer, reference models, and a Pseudo control hedging (PCH) block. The theoretical results are software implemented and validated by complex numerical simulations; compared with other ALSs having the same radio-technical subsystems but with conventional or fuzzy controllers for the control of aircraft pitch angle and longitudinal velocity, the architecture designed in this paper is characterized by much smaller overshoots and stationary errors.

Model-based airflow controller design for fire ventilation in road tunnels

This paper describes a new approach to design the proportional-integral-derivative (PID) controller of the longitudinal airflow velocity in road tunnels for fire situations. Our work shows clearly that the use of a proper model provides valid data for model-based tuning of tunnel controllers, which is demonstrated by real tunnel tests. The design uses the simplified mathematical model of airflow dynamics based on Bernoulli and continuity equations, which describe the airflow dynamics in one dimension. Optimizing controller parameters on site is very time consuming and this problem increases in the case of complex tunnels with several entrance and exit ramps, which typically have occurrences of traffic congestion. Our approach is based on the design of the controller through simulations, which use the mathematical model of airflow velocity in the tunnel. This approach spares a lot of work and time with the controller tuning within tunnel tests. Moreover, it can discover potential problems, which can occur during real instances of fire in the tunnel. The additional advantage of this approach is a possibility to simulate a scenario of errors and failures of some devices, which are important for reliable control of longitudinal airflow velocity. Although this approach is focused primarily on complex road tunnels, due to their complexity and significant time savings with the controller tuning, it can be also used for simpler tunnels with no ramps (usually highway tunnels) where the design of the airflow controller is not as complex compared to the case of road tunnels. This paper also includes a case study of the airflow controller design for the Blanka tunnel complex in Prague, Czech Republic, which is the largest city tunnel in Central Europe.

A novel pre-control method of vehicle dynamics stability based on critical stable velocity during transient steering maneuvering

The current research of direct yaw moment control (DYC) system focus on the design of target yaw moment and the distribution of wheel brake force. The differential braking intervention can effectively improve the lateral stability of the vehicle, however, the effect of DYC can be improved a step further by applying the control of vehicle longitudinal velocity. In this paper, the relationship between the vehicle longitudinal velocity and lateral stability is studied, and the simulation results show that a decrease of 5 km/h of longitudinal velocity at a particular situation can bring 100° increasing of stable steering upper limit. A critical stable velocity considering the effect of steering and yaw rate measurement is defined to evaluate the risk of losing steer-ability or stability. A novel velocity pre-control method is proposed by using a hierarchical pre-control logic and is integrated with the traditional DYC system. The control algorithm is verified through a hardware in-the-loop simulation system. Double lane change (DLC) test results on both high friction coefficient (μ) and low μ roads show that by using the pre-control method, the steering effort in DLC test can be reduced by 38% and 51% and the peak value of brake pressure control can be reduced by 20% and 12% respectively on high μ and low μ roads, the lateral stability is also improved. This research proposes a novel DYC system with lighter control effort and better control effect.

Fault-tolerant control of electric vehicles with in-wheel motors using actuator-grouping sliding mode controllers

Although electric vehicles with in-wheel motors have been regarded as one of the promising vehicle architectures in recent years, the probability of in-wheel motor fault is still a crucial issue due to the system complexity and large number of control actuators. In this study, a modified sliding mode control (SMC) is applied to achieve fault-tolerant control of electric vehicles with four-wheel-independent-steering (4WIS) and four-wheel-independent-driving (4WID). Unlike in traditional SMC, in this approach the steering geometry is re-arranged according to the location of faulty wheels in the modified SMC. Three SMC control laws for longitudinal velocity control, lateral velocity control and yaw rate control are designed based on specific vehicle motion scenarios. In addition the actuator-grouping SMC method is proposed so that driving actuators are grouped and each group of actuators can be used to achieve the specific control target, which avoids the strong coupling effect between each control target. Simulation results prove that the proposed modified SMC can achieve good vehicle dynamics control performance in normal driving and large steering angle turning scenarios. In addition, the proposed actuator-grouping SMC can solve the coupling effect of different control targets and the control performance is improved.

Design and Simulation of Car-Following Control System for Intelligent Vehicles

To improve the driving safety and comfort during car-following, a hierarchical car-following control system is designed. The upper-level controller determines the desired longitudinal velocity according to the vehicle state information from sensors and the minimum safety distance model based on time headway. The lower-level controller with switch logic between throttle and brake is employed for the longitudinal velocity control. The optimal PID parameters in the lower-level controller are tuned with orthogonal experimental design (OED) method, aiming at minimizing the comprehensive index about safety and comfort. Co-simulations of the car-following system are implemented in the simulation platform based on PreScan and Matlab/Simulink. The following vehicle is controlled to track a leading vehicle with a preset time-varying velocity, ensuring a safety distance between the two vehicles and improving driving comfort. The simulation results show that the car-following control system has a good performance on car-following and is suitable for intelligent vehicles.