Theoretical and Experimental Investigation of Driver Noncooperative-Game Steering Control Behavior

This paper investigates two noncooperative-game strategies which may be used to represent a human driver's steering control behavior in response to vehicle automated steering intervention.The first strategy,namely the Nash strategy is derived based on the assumption that a Nash equilibrium is reached in a noncooperative game of vehicle path-following control involving a driver and a vehicle automated steering controller.The second one,namely the Stackelberg strategy is derived based on the assumption that a Stackelberg equilibrium is reached in a similar context.A simulation study is performed to study the differences between the two proposed noncooperativegame strategies.An experiment using a fixed-base driving simulator is carried out to measure six test drivers'steering behavior in response to vehicle automated steering intervention.The Nash strategy is then fitted to measured driver steering wheel angles following a model identification procedure.Control weight parameters involved in the Nash strategy are identified.It is found that the proposed Nash strategy with the identified control weights is capable of representing the trend of measured driver steering behavior and vehicle lateral responses.It is also found that the proposed Nash strategy is superior to the classic driver steering control strategy which has widely been used for modeling driver steering control over the past.A discussion on improving automated steering control using the gained knowledge of driver noncooperative-game steering control behavior was made.

Hazard-evaluation-oriented Moving Horizon Parallel Steering Control for Driver-Automation Collaboration During Automated Driving

Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden and ensuring the safety of human drivers. This paper presents a parallel steering control framework for an intelligent vehicle using moving horizon optimization.The framework considers lateral stability, collision avoidance and actuator saturation and describes them as constraints, which can blend the operation of a human driver and a parallel steering controller effectively. Moreover, the road hazard and the steering operation error are employed to evaluate the operational hazardous of an intelligent vehicle. Under the hazard evaluation,the intelligent vehicle will be mainly operated by the human driver when the vehicle operates in a safe and stable manner.The automated steering driving objective will play an active role and regulate the steering operations of the intelligent vehicle based on the hazard evaluation. To verify the effectiveness of the proposed hazard-evaluation-oriented moving horizon parallel steering control approach, various validations are conducted, and the results are compared with a parallel steering scheme that does not consider automated driving situations. The results illustrate that the proposed parallel steering controller achieves acceptable performance under both conventional conditions and hazardous conditions.

Nonlinear Derivative and Integral Sliding Control for Tracked Vehicle Steering with Hydrostatic Drive

In the steering process of tracked vehicle with hydrostatic drive,the motion and resistance states of the vehicle are always of uncertain and nonlinear characteristics,and these states may undergoe large-scale changes.Therefore,it is significant to enhance the steering stability of tracked vehicle with hydrostatic drive to meet the need of future battlefield.In this paper,a sliding mode control algorithm is proposed and applied to achieve desired yaw rates.The speed controller and the yaw rate controller are designed through the kinematics and dynamics analysis.In addition,the nonlinear derivative and integral sliding mode control algorithm is designed,which is supposed to efficiently reduce the integration saturation and the disturbances from the unsmooth road surfaces through a conditional integrator approach.Moreover,it improves the response speed of the system and reduces the chattering by the derivative controller.The hydrostatic tracked vehicle module is modeled with a multi-body dynamic software RecurDyn and the steering control strategy module is modeled by MATLAB/Simulink.The co-simulation results of the whole model show that the control strategy can improve the vehicle steering response speed and also ensure a smooth control output with small chattering and strong robustness.

Vehicle Active Steering Control Research Based on Two-DOF Robust Internal Model Control

Because of vehicle＇s external disturbances and model uncertainties,robust control algorithms have obtained popularity in vehicle stability control.The robust control usually gives up performance in order to guarantee the robustness of the control algorithm,therefore an improved robust internal model control（IMC） algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness.The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties.In order to separate the design process of model tracking from the robustness design process,the improved 2 degree of freedom（DOF） robust internal model controller structure is given from the standard Youla parameterization.Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm,on the basis of a nonlinear vehicle simulation model with a magic tyre model.Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance,which can enhance the vehicle stability and handling,regardless of variations of the vehicle model parameters and the external crosswind interferences.Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Steering Control of Wheeled Armored Vehicle with Brushless DC Motor

Considering the steering characters of one type of wheeled armored vehicle, a brushless direct current （DC） motor is adapted as the actuator for steering control. After investigating the known algorithms, one kind of algorithm, which combines the fuzzy logic control with the self-adapting PID control and the startup and pre-hrake control, is put forward. Then a test-bed is constructed, and an experiment is conducted. The result of experiment confirms the validity of this algorithm in steering control of wheeled armored vehicle with brushless DC motor.

Generalized Internal Model Robust Control for Active Front Steering Intervention

Because of the tire nonlinearity and vehicle＇s parameters＇uncertainties,robust control methods based on the worst cases,such as H_∞,μsynthesis,have been widely used in active front steering control,however,in order to guarantee the stability of active front steering system（AFS）controller,the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control.In this paper,a generalized internal model robust control（GIMC）that can overcome the contradiction between performance and stability is used in the AFS control.In GIMC,the Youla parameterization is used in an improved way.And GIMC controller includes two sections：a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters＇uncertainties and some external disturbances.Simulations of double lane change（DLC）maneuver and that of braking on split-μroad are conducted to compare the performance and stability of the GIMC control,the nominal performance PID controller and the H_∞controller.Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle＇s parameters variations,H_∞controller is conservative so that the performance is a little low,and only the GIMC controller overcomes the contradiction between performance and robustness,which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller.Therefore,the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system,that is,can solve the instability of PID or LQP control methods and the low performance of the standard H_∞controller.

Hierarchical CNNPID Based Active Steering Control Method for Intelligent Vehicle Facing Emergency Lane-Changing

To resolve the response delay and overshoot problems of intelligent vehicles facing emergency lane-changing due to proportional-integral-differential(PID)parameter variation,an active steering control method based on Convolutional Neural Network and PID(CNNPID)algorithm is constructed.First,a steering control model based on normal distribution probability function,steady constant radius steering,and instantaneous lane-change-based active for straight and curved roads is established.Second,based on the active steering control model,a three-dimensional constraint-based fifth-order polynomial equation lane-change path is designed to address the stability problem with supersaturation and sideslip due to emergency lane changing.In addition,a hierarchical CNNPID Controller is constructed which includes two layers to avoid collisions facing emergency lane changing,namely,the lane change path tracking PID control layer and the CNN control performance optimization layer.The scaled conjugate gradient backpropagation-based forward propagation control law is designed to optimize the PID control performance based on input parameters,and the elastic backpropagation-based module is adopted for weight correction.Finally,comparison studies and simulation/real vehicle test results are presented to demonstrate the effectiveness,significance,and advantages of the proposed controller.

Steering performance of an inverted pendulum vehicle with pedals as a personal mobility vehicle

From the viewpoints of environmental protection, support for the aged and ensuring the right to mobility, there is a need to develop a new type of mobility vehicle that provides more effective transportation. The authors propose an inverted pendulum vehicle with pedals as one of the forms of personal mobility vehicles （PMVs）. In this paper, the steering performance of the inverted pendulum vehicle with pedals is discussed based on experiments on a prototype. From the experimental results, it was confirmed that the errors from the five subjects for the target trajectory and the five-grade evaluation of the maneuverability were similar. Finally, we created an inverted pendulum vehicle with pedals to which was added a reaction actuator for the steering system. From the experimental results, it was found that setting appropriate feedback gains for the handle steering angle and its rate of rotation, which control the right and left wheel driving torques, resulted in greatly improved maneuverability. C 2013 The Chinese Society of Theoretical and Applied Mechanics. [doi：10.1063/2.1301309]

Fuzzy steering control to serpentine tracked robots in unknown environment

To solve the problem of multi-turning attitudes,unknown ground parameters and complex skidsteering control for serpentine tracked robots(STR),a fuzzy control method based on the estimation of track curvature and side slip angles is proposed.Based on the track-ground interaction,mathematical models are established for stable turning in the way of recursive homogeneous transformation.The navigation parameters such as rack curvature and side slip angles have approximately linear relationship with the yaw angles of articulate joints.Then attitude models in turning control are studied and summarized.Transforming strategies of attitude models are optimized.Based on the strategies,an adjustable fuzzy controller is designed.In the controller,an estimator is set to solve the problem of unknown and variable universe.The estimator predicts the track curvature by metabolic linear least squares estimate.Experiments have verified the feasibility and validity of the controller.

Theoretical Study of Double Cost Function Linear Quadratic Regulator(LQR)

Double cost function linear quadratic regulator (DLQR) is developed from LQR theory to solve an optimal control problem with a general nonlinear cost function. In addition to the traditional LQ cost function, another free form cost function was introduced to express the physical need plainly and optimize weights of LQ cost function using the search algorithms. As an instance, DLQR was applied in determining the control input in the front steering angle compensation control (FSAC) model for heavy duty vehicles. The brief simulations show that DLQR is powerful enough to specify the engineering requirements correctly and balance many factors effectively. The concept and applicable field of LQR are expanded by DLQR to optimize the system with a free form cost function.

Multi-constrained model predictive control for autonomous ground vehicle trajectory tracking

A multi-constrained model predictive control （ MPC ） algorithm for trajectory tracking of an autonomous ground vehicle is proposed and tested in this paper. First, to simplify the computa- tion, an active steering linear error model is applied in the MPC controller. Then, a control incre- ment constraint and a relaxing factor are taken into account in the objective function to ensure the smoothness of the trajectory, using a softening constraints technique. In addition, the controller can obtain optimal control sequences which satisfy both the actual kinematic constraints and the actuator constraints. The circular trajectory tracking performance of the proposed method is compared with that of another MPC controller. To verify the trajectory tracking capabilities of the designed control- ler at different desired speed, the simulation experiments are carried out at the speed of 3m/s, 5m/ s and 10m/s. The results demonstrate the MPC controller has a good speed adaptability.

Important Issues of Needle Insertion into Soft Tissue

Needle insertion procedures have been gaining in popularity with medical communities and patients over the recent years.Currently,their applications span a wide range of diagnostic and therapeutic procedures and there is still a growing tenden?cy toward integrating needle insertion into other procedures and surgeries.Its less invasive nature,which is performed locally on the body,largely explains this growing trend.This results in less intraoperative tissue damage and shorter post-operative recovery time.Many procedures like biopsy,deep brain stimulation,and cancer treatments are done using needles/eatheters.However,despite all the advantages of needle insertion procedures,the inherent complications resulting from them,such as tissue deformation,needle deflection,tissue inhomogeneity,patient variability,and associated uncertainty,can hardly be missed.Therefore,a needle insertion procedure requires that we address promising aspects and associated concerns.Against this backdrop,this paper provides a review of some of the main issues associated with a generic needle insertion procedure.

Steering control for underwater gliders

Steering control for an autonomous underwater glider （AUG） is very challenging due to its changing dynamic char- acteristics such as payload and shape. A good choice to solve this problem is online system identification via in-field trials to capture current dynamic characteristics for control law reconfiguration. Hence, an online polynomial estimator is designed to update the yaw dynamic model of the AUG, and an adaptive model predictive control （MPC） controller is used to calculate the optimal control command based on updated estimated parameters. The MPC controller uses a quadratic program （QP） to compute the optimal control command based on a user-defined cost function. The cost function has two terms, focusing on output reference tracking and move suppression of input, respectively. Move-suppression performance can, at some level, represent energy-saving performance of the MPC controller. Users can balance these two competitive control performances by tuning weights. We have compared the control performance using the second-order polynomial model to that using the filth-order polynomial model, and found that the tbrmer cannot capture the main characteristics of yaw dynamics and may result in vibration during the flight. Both processor-in-loop （PIL） simulations and in-lake tests are presented to validate our steering control performance.

Steering control in autonomous vehicles using deep reinforcement learning

A novel deep reinforcement learning-based steering control method of autonomous vehicles is proposed. A distortionless compressing method of action space is presented. Convolutional neural networks(CNNs) are designed to serve as an action policy. Driver experience is investigated and modeled to optimize policy of new actions exploration. Experimental results show that the proposed algorithm has better robustness and smoothness. Moreover, it is applicable to different roads, velocities or wire-control systems.

Driver-automation shared steering control considering driver neuromuscular delay characteristics based on stackelberg game

To promote the intelligent vehicle safety and reduce the driver steering workload,stackelberg game theory is adopted to design the shared steering control strategy that takes the driver neuromuscular delay characteristics into account.First,a shared steering control framework with adjustable driving weight is proposed,and a coupling interaction model considering the driver neuromuscular delay characteristics is constructed by using the stackelberg game theory.Moreover,the driver-automation optimal control strategy is deduced theoretically when the game equilibrium is reached.Finally,simulation and virtual driving tests are carried out to verify the superiority of the proposed method.The results illustrate that the raised method can enhance the vehicle safety with low driving weight intervention,and it can achieve better auxiliary effect with less control cost.In addition,the driver-in-the-loop test results show that the proposed strategy can achieve better performance in assisting drivers with low driving skills.

A Novel Movement Behavior Control Method for Carp Robot through the Stimulation of Medial Longitudinal Fasciculus Nucleus of Midbrain

Biological robot is a kind of creature controlled by human beings by applying intervention signals through control technology to regulate biological behavior.At present,the research on bio-robot mainly focuses on terrestrial mammals and insects,while the research on aquatic animal robot is less.Early studies have shown that the medial longitudinal fasciculus nucleus(NFLM)of carp midbrain was related to tail wagging,but the research has not been applied to the navigation control of the carp robot.The purpose of this study is to realize the quantitative control of the forward and steering behavior of the carp robot by NFLM electrical stimulation.Under the condition of no craniotomy,brain electrode was implanted into the NFLM of the carp midbrain,and the underwater control experiment was carried out by applying different electrical stimulation parameters.Using the ImageJ software and self-programmed,the forward motion speed and steering angle of steering motion of the carp robot before and after being stimulated were calculated.The experimental results showed for the carp robot that was induced the steering motion,the left and right steering motion of 30°to 150°could be achieved by adjusting the stimulation parameters,for the carp robot that was induced the forward motion,the speed of forward motion could be controlled to reach 100 cm/s.The research lays a foundation for the accurate control of the forward and steering motion of the aquatic animal robot.