Fuzzy Chaos Control for Vehicle Lateral Dynamics Based on Active Suspension System

The existing research of the active suspension system（ASS） mainly focuses on the different evaluation indexes and control strategies. Among the different components, the nonlinear characteristics of practical systems and control are usually not considered for vehicle lateral dynamics. But the vehicle model has some shortages on tyre model with side-slip angle, road adhesion coefficient, vertical load and velocity. In this paper, the nonlinear dynamic model of lateral system is considered and also the adaptive neural network of tire is introduced. By nonlinear analysis methods, such as the bifurcation diagram and Lyapunov exponent, it has shown that the lateral dynamics exhibits complicated motions with the forward speed. Then, a fuzzy control method is applied to the lateral system aiming to convert chaos into periodic motion using the linear-state feedback of an available lateral force with changing tire load. Finally, the rapid control prototyping is built to conduct the real vehicle test. By comparison of time response diagram, phase portraits and Lyapunov exponents at different work conditions, the results on step input and S-shaped road indicate that the slip angle and yaw velocity of lateral dynamics enter into stable domain and the results of test are consistent to the simulation and verified the correctness of simulation. And the Lyapunov exponents of the closed-loop system are becoming from positive to negative. This research proposes a fuzzy control method which has sufficient suppress chaotic motions as an effective active suspension system.

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.

Stochastic sampled-data multi-objective control of active suspension systems for in-wheel motor driven electric vehicles

This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets.For this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities.Then,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership.Furthermore,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance requirements.Finally,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.

Particle swarm optimization-based algorithm of a symplectic method for robotic dynamics and control

Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this paper, a particle swarm optimization(PSO) method is introduced to solve and control a symplectic multibody system for the first time. It is first combined with the symplectic method to solve problems in uncontrolled and controlled robotic arm systems. It is shown that the results conserve the energy and keep the constraints of the chaotic motion, which demonstrates the efficiency, accuracy, and time-saving ability of the method. To make the system move along the pre-planned path, which is a functional extremum problem, a double-PSO-based instantaneous optimal control is introduced. Examples are performed to test the effectiveness of the double-PSO-based instantaneous optimal control. The results show that the method has high accuracy, a fast convergence speed, and a wide range of applications.All the above verify the immense potential applications of the PSO method in multibody system dynamics.

Increasing realism in modelling energy losses in railway vehicles and their impact to energy-efficient train control

The reduction of energy consumption is an increasingly important topic of the railway system.Energy-efficient train control(EETC)is one solution,which refers to mathematically computing when to accelerate,which cruising speed to hold,how long one should coast over a suitable space,and when to brake.Most approaches in literature and industry greatly simplify a lot of nonlinear effects,such that they ignore mostly the losses due to energy conversion in traction components and auxiliaries.To fill this research gap,a series of increasingly detailed nonlinear losses is described and modelled.We categorize an increasing detail in this representation as four levels.We study the impact of those levels of detail on the energy optimal speed trajectory.To do this,a standard approach based on dynamic programming is used,given constraints on total travel time.This evaluation of multiple test cases highlights the influence of the dynamic losses and the power consumption of auxiliary components on railway trajectories,also compared to multiple benchmarks.The results show how the losses can make up 50%of the total energy consumption for an exemplary trip.Ignoring them would though result in consistent but limited errors in the optimal trajectory.Overall,more complex trajectories can result in less energy consumption when including the complexity of nonlinear losses than when a simpler model is considered.Those effects are stronger when the trajectory includes many acceleration and braking phases.

A Hybrid Approach to Modeling and Control of Vehicle Height for Electronically Controlled Air Suspension

The control problems associated with vehicle height adjustment of electronically controlled air suspension （ECAS） still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical （MLD） modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller （HMPC） is tuned based on online mixed-integer quadratic optimization （MIQP）. Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology （MPT）, thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.

Output Feedback Dynamic Surface Controller Design for Airbreathing Hypersonic Flight Vehicle

This paper addresses issues related to nonlinear robust output feedback controller design for a nonlinear model of airbreathing hypersonic vehicle. The control objective is to realize robust tracking of velocity and altitude in the presence of immeasurable states, uncertainties and varying flight conditions. A novel reduced order fuzzy observer is proposed to estimate the immeasurable states. Based on the information of observer and the measured states, a new robust output feedback controller combining dynamic surface theory and fuzzy logic system is proposed for airbreathing hypersonic vehicle. The closedloop system is proved to be semi-globally uniformly ultimately bounded (SUUB), and the tracking error can be made small enough by choosing proper gains of the controller, filter and observer. Simulation results from the full nonlinear vehicle model illustrate the effectiveness and good performance of the proposed control scheme. © 2014 Chinese Association of Automation.

Combined Control Allocation and Sliding Mode Control in the Dynamic Control of a Vehicle with Eight In-Wheel Motors

An eight wheel independently driving steering（8 WIDBS）electric vehicle is studied in this paper.The vehicle is equipped with eight in-wheel motors and a steer-by-wire system.A hierarchically coordinated vehicle dynamic control（HCVDC）system,including a high-level vehicle motion controller,a control allocation,an inverse tire model and a lower-level slip/slip angle controller,is proposed for the over-actuated vehicle system.The high-level sliding mode vehicle motion controller is designed to produce desired total forces and yaw moment,distributed to longitudinal and lateral forces of each tire by an advanced control allocation method.And the slip controller is designed to use a sliding mode control method to follow the desired slip ratios by manipulating the corresponding in-wheel motor torques.Evaluation of the overall system is accomplished by sine maneuver simulation.Simulation results confirm that the proposed control system can coordinate among the redundant and constrained actuators to achieve the vehicle dynamic control task and improve the vehicle stability.

Decentralized Dynamic Event-Triggered Communication and Active Suspension Control of In-Wheel Motor Driven Electric Vehicles with Dynamic Damping

This paper addresses the co-design problem of decentralized dynamic event-triggered communication and active suspension control for an in-wheel motor driven electric vehicle equipped with a dynamic damper. The main objective is to simultaneously improve the desired suspension performance caused by various road disturbances and alleviate the network resource utilization for the concerned in-vehicle networked suspension system. First, a T-S fuzzy active suspension model of an electric vehicle under dynamic damping is established. Second,a novel decentralized dynamic event-triggered communication mechanism is developed to regulate each sensor's data transmissions such that sampled data packets on each sensor are scheduled in an independent manner. In contrast to the traditional static triggering mechanisms, a key feature of the proposed mechanism is that the threshold parameter in the event trigger is adjusted adaptively over time to reduce the network resources occupancy. Third, co-design criteria for the desired event-triggered fuzzy controller and dynamic triggering mechanisms are derived. Finally, comprehensive comparative simulation studies of a 3-degrees-of-freedom quarter suspension model are provided under both bump road disturbance and ISO-2631 classified random road disturbance to validate the effectiveness of the proposed co-design approach. It is shown that ride comfort can be greatly improved in either road disturbance case and the suspension deflection, dynamic tyre load and actuator control input are all kept below the prescribed maximum allowable limits, while simultaneously maintaining desirable communication efficiency.

Adaptive dynamic surface control for air-breathing hypersonic vehicle

This paper describes an adaptive control approach for an air-breathing hypersonic vehicle. The control objective is to provide robust altitudes and velocity tracking in the presence of model uncertainties and varying disturbances. A fuzzy-neural disturbance observer is developed to estimate uncertainties and disturbances, and the adaptive controller is synthesized by the dynamic surface approach combing with the observer. The tracking error at the steady state can be guaranteed to converge to inside of a small residue set which the size of the set can be an arbitrary small value. Simulation results demonstrate the effectiveness of the presented approach.

Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds

The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation （CA） of the four-wheel-distributed trac- tion/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control （MC） problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive con- trol （NMPC） algorithm is introduced to handle the non- linear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control （TSMC） based path fol- lower. As revealed through off-line simulations, the hier- archical methodology brings nearly 1700% improvement in computational efficiency without loss of control per- formance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lane- change （DLC） test results show that by using the optimalpredictive controller, the root-mean-square （RMS） values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration （OPA） driver model with the non-preview speed-tracking method. Additionally, the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9~. This research proposes a novel motion controller, which provides the full drive-by-wire vehicle with better lane-keeping and colli- sion-avoidance capabilities during autonomous driving.

Post-Impact Motion Planning and Tracking Control for Autonomous Vehicles

There is an increasing awareness of the need to reduce traffic accidents and fatality due to vehicle collision.Post-impact hazards can be more serious as the driver may fail to maintain effective control after collisions.To avoid subsequent crash events and to stabilize the vehicle,this paper proposes a post-impact motion planning and stability control method for autonomous vehicles.An enabling motion planning method is proposed for post-impact situations by combining the polynomial curve and artificial potential field while considering obstacle avoidance.A hierarchical controller that consists of an upper and a lower controller is then developed to track the planned motion.In the upper controller,a time-varying linear quadratic regulator is presented to calculate the desired generalized forces.In the lower controller,a nonlinear-optimization-based torque allocation algorithm is proposed to optimally coordinate the actuators to realize the desired generalized forces.The proposed scheme is verified under comprehensive driving scenarios through hardware-in-loop tests.

A Lyapunov-based three-axis attitude intelligent control approach for unmanned aerial vehicle

A novel Lyapunov-based three-axis attitude intelligent control approach via allocation scheme is considered in the proposed research to deal with kinematics and dynamics regarding the unmanned aerial vehicle systems.There is a consensus among experts of this field that the new outcomes in the present complicated systems modeling and control are highly appreciated with respect to state-of-the-art.The control scheme presented here is organized in line with a new integration of the linear-nonlinear control approaches,as long as the angular velocities in the three axes of the system are accurately dealt with in the inner closed loop control.And the corresponding rotation angles are dealt with in the outer closed loop control.It should be noted that the linear control in the present outer loop is first designed through proportional based linear quadratic regulator(PD based LQR) approach under optimum coefficients,while the nonlinear control in the corresponding inner loop is then realized through Lyapunov-based approach in the presence of uncertainties and disturbances.In order to complete the inner closed loop control,there is a pulse-width pulse-frequency(PWPF) modulator to be able to handle on-off thrusters.Furthermore,the number of these on-off thrusters may be increased with respect to the investigated control efforts to provide the overall accurate performance of the system,where the control allocation scheme is realized in the proposed strategy.It may be shown that the dynamics and kinematics of the unmanned aerial vehicle systems have to be investigated through the quaternion matrix and its corresponding vector to avoid presenting singularity of the results.At the end,the investigated outcomes are presented in comparison with a number of potential benchmarks to verify the approach performance.

Longitudinal speed control algorithm to improve the vehicle stability and mobility on a sharp curve

A new longitudinal speed control strategy is proposed. It used the lateral jerk information as the feedback to predict the longitudinal acceleration and deceleration, which is especially beneficial to a sharp curve turning at a high driving speed. The performance of the controller is validated with Matlab/Simulink. The simulation results indicate that compared with the traditional vehicle, the G-vector controller can reduce the turning radius and improve the vehicle stability and agility obviously. And the proposed longitudinal speed control strategy could trade-off the longitudinal and lateral acceleration by using the tire force during the turning.

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.

Dynamic Event-Triggered Scheduling and Platooning Control Co-Design for Automated Vehicles Over Vehicular Ad-Hoc Networks

This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input.Under such a platoon model,the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader,while attaining improved communication efficiency.Toward this aim,a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed.One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status.Then,a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived.Finally,simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.

MPC-based path tracking with PID speed control for high-speed autonomous vehicles considering time-optimal travel

In order to track the desired path as fast as possible,a novel autonomous vehicle path tracking based on model predictive control(MPC)and PID speed control was proposed for high-speed automated vehicles considering the constraints of vehicle physical limits,in which a forward-backward integration scheme was introduced to generate a time-optimal speed profile subject to the tire-road friction limit.Moreover,this scheme was further extended along one moving prediction window.In the MPC controller,the prediction model was an 8-degree-of-freedom(DOF)vehicle model,while the plant was a 14-DOF vehicle model.For lateral control,a sequence of optimal wheel steering angles was generated from the MPC controller;for longitudinal control,the total wheel torque was generated from the PID speed controller embedded in the MPC framework.The proposed controller was implemented in MATLAB considering arbitrary curves of continuously varying curvature as the reference trajectory.The simulation test results show that the tracking errors are small for vehicle lateral and longitudinal positions and the tracking performances for trajectory and speed are good using the proposed controller.Additionally,the case of extended implementation in one moving prediction window requires shorter travel time than the case implemented along the entire path.

Model Based Adaptive Control and Disturbance Compensation for Underwater Vehicles

Underwater vehicles are being emphasized as highly integrated and intelligent devices for a significant number of oceanic operations. However, their precise operation is usually hindered by disturbances from a tether or manipulator because their propellers are unable to realize a stable suspension. A dynamic multi-body model-based adaptive controller was designed to allow the controller of the vehicle to observe and compensate for disturbances from a tether or manipulator. Disturbances, including those from a tether or manipulator, are deduced for the observation of the controller. An analysis of a tether disturbance covers the conditions of the surface, the underwater area, and the vehicle end point. Interactions between the vehicle and manipulator are mainly composed of coupling forces and restoring moments.To verify the robustness of the controller, path-following experiments on a streamlined autonomous underwater vehicle experiencing various disturbances were conducted in Song Hua Lake in China. Furthermore,path-following experiments for a tethered open frame remote operated vehicle were verified for accurate cruising with a controller and an observer, and vehicle and manipulator coordinate motion control during the simulation and experiments verified the effectiveness of the controller and observer for underwater operation. This study provides instructions for the control of an underwater vehicle experiencing disturbances from a tether or manipulator.

Local Path Planning and Tracking Control of Vehicle Collision Avoidance System

Automotive collision avoidance technology can effectively avoid the accidents caused by dangerous traffic conditions or driver's manipulation errors.Moreover,it can promote the development of autonomous driving for intelligent vehicle in intelligent transportation.We present a collision avoidance system,which is composed of an evasive trajectory planner and a path following controller.Considering the stability of the vehicle in the conflict-free process,the evasive trajectory planner is designed by polynomial parametric method and optimized by genetic algorithm.The path following controller is proposed to make the car drive along the designed path by controlling the vehicle's lateral movement.Simulation results show that the vehicle with the proposed controller has good stability in the collision process,and it can ensure the vehicle driving in accordance with the planned trajectory at different speeds.The research results can provide a certain basis for the research and development of automotive collision avoidance technology.

Trajectory tracking control for underactuated unmanned surface vehicles with dynamic uncertainties

The trajectory tracking control problem for underactuated unmanned surface vehicles(USV) was addressed, and the control system took account of the uncertain influences induced by model perturbation, external disturbance, etc. By introducing the reference, trajectory was generated by a virtual USV, and the error equation of trajectory tracking for USV was obtained, which transformed the tracking problem of underactuated USV into the stabilization problem of the trajectory tracking error equation. A backstepping adaptive sliding mode controller was proposed based on backstepping technology and method of dynamic slide model control. By means of theoretical analysis, it is proved that the proposed controller ensures that the solutions of closed loop system have the ultimate boundedness property. Simulation results are presented to illustrate the effectiveness of the proposed controller.