Intelligent vehicle lateral controller design based on genetic algorithmand T-S fuzzy-neural network

Non-linearity and parameter time-variety are inherent properties of lateral motions of a vehicle. How to effectively control intelligent vehicle (IV) lateral motions is a challenging task. Controller design can be regarded as a process of searching optimal structure from controller structure space and searching optimal parameters from parameter space. Based on this view, an intelligent vehicle lateral motions controller was designed. The controller structure was constructed by T-S fuzzy-neural network (FNN). Its parameters were searched and selected with genetic algorithm (GA). The simulation results indicate that the controller designed has strong robustness, high precision and good ride quality, and it can effectively resolve IV lateral motion non-linearity and time-variant parameters problem.

Control for Underactuated Reentry Aircraft in Small Angle of Attack

The control problem for under-actuated reentry vehicle like HTV-2 is considered with small angle of attack.The control strategy for an aircraft with positive lateral control departure parameter relies on strong lateral stability,which declines with the decrease of the angle of attack.Thus,to control the lateral-directional motion in a stable state is hard and even impossible in some scenarios where the under-actuated reentry vehicle,like HTV-2,flies in a low angle of attack.To address this problem,the lateral-directional open-loop motion characteristics are analyzed.The results show that in an uncontrolled state,the lateral-directional motion can automatically converge to stabilization thanks to the aerodynamic damping effect.Therefore,a method of turning-off the lateral-directional control and inviting aerodynamic damping to control can achieve stability.The six-degree-of-freedom simulation show that the lateral-directional motion can be stabilized by the aerodynamic damping,and the lateral position error caused by the bank angle deviation is limited near the zero-rise angle of attack.The control strategy is effective.

Longitudinal and lateral control methods from single vehicle to autonomous platoon

To successfully implement the platoon control of connected and automated vehicles,it is necessary to address motion control issues to achieve longitudinal and lateral collaborative control.However,due to traffic capacity limitations and the complex traffic environment in which autonomous and human-driven vehicles coexist,autonomous platoon faces significant risks and challenges.This paper investigates longitudinal and lateral control issues from the perspective of a single vehicle up to a platoon,simulating the performance and suitability of various controllers.First,a longitudinal controller based on fuzzy logic and PID control is employed for speed tracking control of a single vehicle,followed by the adoption of an MPC controller based on the vehicle kinematics model to realize the lateral motion of a single vehicle.Second,the communication methods of the autonomous platoon are discussed,and the longitudinal controller that considers the platoon's various communication topologies is developed.Thirdly,a framework for robust integrated motion control is established,which combines the robust H-infinity longitudinal controller and the APF-based MPC lateral controller.Simulation results validate the effectiveness of the aforementioned controllers and reveal their limitations.

Ligand-induced,magic-size clusters enabled formation of colloidal all-inorganic II-VI nanoplatelets with controllable lateral dimensions

Achieving nanoconfinement-controlled synthesis of nanoplatelets(NPLs)via solution process under ambient condition remains a challenge.In this work,we developed a general ligand-induced strategy to synthesize colloidal stable all-inorganic semiconductor NPLs with controllable lateral dimensions.By introducing certain metal salts(cations:Zn^(2+)and In^(3+),anions:NO_(3)^(−),BF_(4)^(−),or triflate OTf−),wurtzite-structured(WZ-)CdS,CdSe,CdTe,and alloy Cd1−xZnxSe NPLs were directly synthesized in solution through the controlled diffusion of magic-size clusters(MSCs)at room temperature.Mechanism studies revealed that destabilization of MSCs and nanoconfined growth in templates facilitated the formation of NPLs.The present study not only provides a new synthetic route for the preparation of NPLs but also helps to provide insight into their probable formation mechanism and presents an important advance toward the rational design of functional nanomaterials.

Lateral control of autonomous vehicles based on learning driver behavior via cloud model

In order to achieve the lateral control of the intelligent vehicle, use the bi-cognitive model based on cloud model and cloud reasoning, solve the decision problem of the qualitative and quantitative of the lateral control of the intelligent vehicle. Obtaining a number of experiment data by driving a vehicle, classify the data according to the concept of data and fix the input and output variables of the cloud controller, design the control rules of the cloud controller of intelligent vehicle, and clouded and fix the parameter of cloud controller： expectation, entropy and hyper entropy. In order to verify the effectiveness of the cloud controller, joint simulation platform based on Matlab/Simulink/CarSim is established. Experimental analysis shows that： driver＇s lateral controller based on cloud model is able to achieve tracking of the desired angle, and achieve good control effect, it also verifies that a series of mental activities such as feeling, cognition, calculation, decision and so on are fuzzy and uncertain.

Lateral stability regulation of intelligent electric vehicle based on model predictive control

Purpose–This paper studies the lateral stability regulation of intelligent electric vehicle(EV)based on model predictive control(MPC)algorithm.Design/methodology/approach–Firstly,the bicycle model is adopted in the system modelling process.To improve the accuracy,the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics.Then the constraint of input and output in the model predictive controller is designed.Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.Findings–The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.Originality/value–The MPC schema and the objective function are established.The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model.The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range.The online estimation of tire stiffness is performed.The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time.This can ensure the accuracy of model.

Online learning-based model predictive trajectory control for connected and autonomous vehicles: Modeling and physical tests

Motivated by the promising benefits of connected and autonomous vehicles (CAVs) in improving fuelefficiency, mitigating congestion, and enhancing safety, numerous theoretical models have been proposed to plan CAVmultiple-step trajectories (time–specific speed/location trajectories) to accomplish various operations. However, limitedefforts have been made to develop proper trajectory control techniques to regulate vehicle movements to follow multiplesteptrajectories and test the performance of theoretical trajectory planning models with field experiments. Without aneffective control method, the benefits of theoretical models for CAV trajectory planning can be difficult to harvest. This studyproposes an online learning-based model predictive vehicle trajectory control structure to follow time–specific speed andlocation profiles. Unlike single-step controllers that are dominantly used in the literature, a multiple-step model predictivecontroller is adopted to control the vehicle’s longitudinal movements for higher accuracy. The model predictive controlleroutput (speed) cannot be interpreted by vehicles. A reinforcement learning agent is used to convert the speed value to thevehicle’s direct control variable (i.e., throttle/brake). The reinforcement learning agent captures real-time changes in theoperating environment. This is valuable in saving parameter calibration resources and improving trajectory control accuracy.A line tracking controller keeps vehicles on track. The proposed control structure is tested using reduced-scale robot cars.The adaptivity of the proposed control structure is demonstrated by changing the vehicle load. Then, experiments on twofundamental CAV platoon operations (i.e., platooning and split) show the effectiveness of the proposed trajectory controlstructure in regulating robot movements to follow time-specific reference trajectories.

Steering control strategy guide by two preview vision cues

Vision cues play an important role in states feedback in motion control.However,the existing driver steering models consider little about vision cues utilized by human drivers during their steering procedure.This paper presents a novel steering control strategy based on two preview points(far point and near point).The far point is used to compensate the steering wheel by predicting the upcoming curvature change with respect to the lane,while the near point as vision feedback,which is used to tune the steering wheel by estimating the errors of vehicle states and lane center.To obtain much smoother lateral acceleration during steering,a forward internal model is established using a second-order yaw dynamics system that captures the influence of yaw angular acceleration caused by steering wheel angle.The input parameter of the second-order system is the vision cues of both the near and far points,and the output parameters are the ideal yaw angle and yaw rate.To calculate suitable the steering wheel angle,an adaptive controller is designed using fuzzy sliding technology,which is used as the input of the vehicle system dynamics.Numerical simulation results show that the proposed method performs better than the existing driver steering models in case of imitating human drivers' behavior,and exhibits excellent adaption to the lane curvature change.