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This paper proposes a novel robust path-following strategy for autonomous road vehicles based on type-2 fuzzy PID neural network (PIDT2FNN) method coupled to an Extended Kalman Filter-based Fuzzy Neural Network (EKFNN) observer. Uncertain Gaussian membership functions (MFs) are employed to self-adjust the universe of discourse for MFs using the adaptation mechanism derived from Lyapunov stability theory and Barbalat’s lemma. External disturbances are significant in autonomous vehicles by changing the driving condition. Furthermore, parametric uncertainties related to the physical limits of tires and the change of the vehicle mass may significantly affect the desired performance of autonomous vehicles. The robustness of the proposed controller against the parametric uncertainties and external disturbances is compared with one active disturbance rejection control (ADRC) algorithm, and a linear-quadratic tracking (LQT) method. The obtained results in terms of the maximum error and root mean square error (RMSE), demonstrate the effectiveness of the proposed control algorithm to reach the minimized path-tracking error.

A comparative study of model predictive control (MPC) schemes and robust$$H_{\infty }$$H∞state feedback control (RSC) method for trajectory tracking is proposed in this paper. The main objective of this paper is to compare MPC and RSC controllers’ performance in tracking predefined trajectory under different scenarios. MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire, which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode. RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison. Then, three test cases are built in CarSim-Simulink joint platform. Specifically, the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions. Besides, the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability. Furthermore, an extreme curve test is built where the road adhesion changes suddenly, in order to test the performance of both controllers under extreme conditions. Finally, the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

The in-wheel motor (IWM)-driven electric vehicles (EVs) attract increasing attention due to their advantages in dimensions and controllability. The majority of the current studies on IWM are carried out with the assumption of an ideal actuator, in which the coupling effects between the non-ideal IWM and vehicle are ignored. This paper uses the braking process as an example to investigate the longitudinal–vertical dynamics of IWM-driven EVs while considering the mechanical–electrical coupling effect. First, a nonlinear switched reluctance motor model is developed, and the unbalanced electric magnetic force (UEMF) induced by static and dynamic mixed eccentricity is analyzed. Then, the UEMF is decomposed into longitudinal and vertical directions and included in the longitudinal–vertical vehicle dynamics. The coupling dynamics are demonstrated under different vehicle braking scenarios; numerical simulations are carried out for various road grades, road friction, and vehicle velocities. A novel dynamics vibration absorbing system is adopted to improve the vehicle dynamics. Finally, the simulation results show that vehicle vertical dynamic performance is enhanced.

Obstacle avoidance strategy is important to ensure the driving safety of unmanned ground vehicles. In the presence of static and moving obstacles, it is challenging for the unmanned ground vehicles to plan and track the collision-free paths. This paper proposes an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control. A path planner is formed to generate the collision-free paths that avoid static and moving obstacles. The quintic polynomial curves are employed for path generation considering computational efficiency and ride comfort. Then, a robust fuzzy output-feedback controller is designed to track the planned paths. The Takagi–Sugeno (T–S) fuzzy modeling technique is utilized to handle the system variables when forming the vehicle dynamic model. The robust output-feedback control approach is used to track the planned paths without using the lateral velocity signal. The proposed obstacle avoidance strategy is validated in CarSim® simulations. The simulation results show the unmanned ground vehicle can avoid the static and moving obstacles by applying the designed path planning and robust fuzzy output-feedback control approaches.

Chassis control systems play a significant role in achieving the desired vehicle performance and stability during various severe maneuvers. A probabilistic estimation approach by hybridization of optimal robust control and a damped least-square backpropagation based neural networks (NN) is proposed to design a control system for dealing with unknown nonlinear dynamics of a passenger car. To this end, a four-wheel active steering (4WAS) model is employed and a multilayer perceptron (ML) feed-forward backpropagation neural network (FFBPNN) model is developed as an approximator. The optimal robust control is employed to regulate the yaw rate and side-slip angle of the vehicle to follow the desired vehicle response. The developed FFBPNN model is trained to distinguish the nonlinear dynamics of the vehicle and the corresponding optimal feedback gain during a wide range of operating conditions via the state variables. The robustness of the controller is evaluated using Lyapunov stability method. The performance of the proposed controller is analyzed considering the open-loop and closed-loop responses of the nonlinear vehicle model and a sliding mode controller to track the desired yaw rate and side-slip angle responses. The results obtained during severe maneuvers suggest that the proposed control method can substantially enhance the handling and stability performances of the vehicle.

This article proposed a reconfigurable control scheme for articulated vehicles’ stabilization by leveraging optimization-based control techniques. The central objective is to maintain a good lateral and yaw stability of the vehicle with optimal corrective brakes, meanwhile applicable to different actuation configurations. This is achieved by a two-layer control structure, where the high-level controller formulates as a model predictive control (MPC) tracking problem to generate corrective center-of-gravity (CG) yaw moment of each unit. The lower level controller utilizes the control allocation (CA) algorithm with real-time constraints to optimally calculate differential brakes at each wheel with maximum utilization-of-tires capacity. To evaluate its real-time performance, experimental validation is carried out on the electrified tractor-trailer with selective differential braking systems. It is observed that the controller is effective in dynamics control, meanwhile reconfigurable to various actuation configurations. Furthermore, the proposed system has great potential in production tractor-trailer systems due to the low cost and number of sensor requisites.

Ride comfort and road holding are two substantial performance criteria related to the suspension system of road vehicles. These performance criteria are critical in controller design and chassis stability of automated driving vehicles. Control demands in terms of accuracy, quick response and robustness to matched and mismatched uncertainties are suggestive of employing adaptive robust controllers. Herein, a state observer-based modified sliding mode interval fuzzy type-2 neural network (FT2NN) controller is designed to suppress the vibrations from a typical rough terrain imposed to the nonlinear suspension system of the vehicles. The nonlinear system dynamics are estimated using the universal approximation capacity of the neuro-fuzzy type-2 approach and the states are obtained by the adaptive robust state observer. The membership functions (MFs) of the fuzzy type-2 system are employed to deal with the uncertainties through variable mean and variances for upper and lower MFs. Furthermore, the proposed controller has the advantage of relaxing the condition for the approximation error boundaries while the estimation step is utilized to observe the boundaries adaptively. A new adaptive compensator is employed to withstand the effect of the external disturbance, the approximation errors related to the unknown nonlinear functions and state estimations. The results obtained from the proposed controller are suggestive of the higher effectiveness of the proposed controller compared to the tested Neuro-PID controller, and also the passive suspension system. The high-fidelity MSC ADAMS based co-simulations were implemented to validate the practicality of the proposed controller.

This paper presents a novel local motion planning framework in a hierarchical manner for autonomous vehicles to follow a trajectory and agilely avoid obstacles. In the upper layer, a new path-planning method based on the resistance network is applied to plan behaviors (e.g. lane keeping or changing), where the human-like factors can be included to simulate different driver styles, such as the aggressive, moderate, and conservative. The planned results (i.e. the lane-change command and the local planned path) will guide the lower-layer planner to decide the local motion. In the lower layer, for the sake of simplicity and alleviation of the computational burden, two separate model predictive controllers (MPC) based on a point-mass kinematic model are utilized for both longitudinal and lateral motion planning. Finally, a super-twisting sliding mode controller (STSMC) based motion tracker is designed to show the feasibility of the proposed decoupled planning method and decide the desired control actions of autonomous vehicles. Several scenarios are defined to comprehensively test and demonstrate the effectiveness and the real-time applicability of the new motion-planning framework. The results show that the proposed method performs very well in the planning and tracking process and takes less than $\text{25 ms}$ for the whole planning process, which can be easily implemented in real-world applications.

Distributed-drive electric vehicles (EVs) replace internal combustion engine with multiple motors, and the novel configuration results in new dynamic-related issues. This paper studies the coupling effects between the parameters and responses of dynamic vibration-absorbing structures (DVAS) for EVs driven by in-wheel motors (IWM). Firstly, a DVAS-based quarter suspension model is developed for distributed-drive EVs, from which nine parameters and five responses are selected for the coupling effect analysis. A two-stage global sensitivity analysis is then utilized to investigate the effect of each parameter on the responses. The control of the system is then converted into a multiobjective optimization problem with the defined system parameters being the optimization variables, and three dynamic limitations regarding both motor and suspension subsystems are taken as the constraints. A particle swarm optimization approach is then used to either improve ride comfort or mitigate IWM vibration, and two optimized parameter sets for these two objects are provided at last. Simulation results provide in-depth conclusions for the coupling effects between parameters and responses, as well as a guideline on how to design system parameters for contradictory objectives. It can be concluded that either passenger comfort or motor lifespan can be reduced up to 36% and 15% by properly changing the IWM suspension system parameters.

An online energy management controller is presented in this paper for a plug-in hybrid electric vehicle (PHEV), which is based on driving conditions recognition and genetic algorithm (GA). The proposed controller can be used in the real-time application. First, the studied multimode PHEV is modeled and four traction operation modes are introduced in detail. Second, the principal component analysis (PCA) algorithm is utilized to classify the real historical driving conditions data. Four types of driving conditions are constructed to describe the representative scenarios. Then, GA is applied to search the optimal values for seven control actions offline. These parameters for different driving conditions are preserved and can be activated online. Finally, the driving condition is identified online and the corresponding control actions are loaded and adopted. Simulation results indicate that the proposed approach is close to the globally optimal method, dynamic programming, and is superior to the charge-depleting/charge-sustaining technique. Also, hardware-in-the-loop experiment is built to validate the real-time characteristic of the proposed strategy.

This paper investigates the lane keeping control for four-wheel independently actuated autonomous vehicles. To guarantee the vehicle safety when the active-steering motor entirely fails, the steering manoeuvre is realized by the differential drive assisted steering (DDAS) that is generated by the differential moment between the front wheels. A novel adaptive multivariable super-twisting control strategy is proposed to realize the control objective in finite time, considering the multiple unknown and mismatched disturbances of the steering system with the chattering effect removed. In the sliding surface, a nonlinear function is designed to adaptively change the damping ratio of the closed-loop system so as to improve the transient performance of the lane keeping control in the faulty steering condition. The controller design has avoided the use of the lateral velocity which is usually hard to measure in practice. Instead, the lane keeping errors and their time derivatives are estimated with a high-order sliding mode observer based on a vision system. The finite-time convergence of the closed-loop system is proved by the Lyapunov method. Results of CarSim-Simulink simulations with the proposed control strategy compared with a tradition sliding mode controller based on a high-fidelity and full-car model have verified the effectiveness and robustness of the proposed controller in the lane keeping control via DDAS with the guaranteed high performance.

© 2018 Elsevier Ltd Driven by the increasing requirement for road conditions in the field of the controllable suspension system, this paper presents a novel nonlinear road-excitation classification procedure for arbitrary suspension control strategy. The proposed procedure includes four steps: the definition of controller parameters, selection of insensitive frequency ranges, calculation of superior features, and generation of the classifier. To better illustrate the proposed procedure, the clipped optimal control strategy is taken as an example in the simulation part. Simulation results reveal that the proposed method can accurately estimate road excitation level for various controller parameters, vehicle speeds, and vehicle models. Three contributions have been made in this paper: (1) A road classification procedure that can be used for road adaptive suspension control with any control algorithm is developed; (2) In order to improve classification accuracy, the concept of insensitive index which is based on the time-frequency analysis is proposed; (3) Experimental validation with a quarter vehicle test rig is performed, which has verified the effectiveness of the proposed method for the adaptive controllable suspension system.

This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim–Simulink simulation.

This paper proposes a novel observer-based sliding mode control (SMC) to enhance the performance of autonomous vehicles (AVs) rollover behavior under various road profile input. The model of half-car system is first established to describe the AVs rollover behavior by considering nonlinear dynamics of tire force and controllable suspension force under various movement conditions. Moreover, an unscented Kalman Filter (UKF) algorithm is proposed to identify the sprung mass. Combing with the interacting multiple model (IMM) approach and Markov Chain Monte Carlo (MCMC) theory, a novel interacting multiple model unscented Kalman Filters (IMMUKF) observer based is developed to estimate the movement state of AVs system. Then, an adaptive observer-based sliding mode control (AOSMC) strategy is proposed to constrain the AVs roll performance under the various external input. The stability of the proposed algorithm is proved by using Lyapunov function. Finally, simulations and validations are performed on a high-fidelity CarSim® software by using J-turn scenario under various road excitation, to validate the proposed algorithm for AVs system, and the results illustrate that the improved roll states are more than 15% compared with the traditional SMC algorithm.

The coupling effect between road excitation and an in-wheel switched reluctance motor (SRM) on vehicle ride comfort is numerically analysed. A hybrid control system consisting of fault tolerant H∞ suspension controller and SRM controller for an in-wheel SRM driven electric vehicle is proposed to improve the vehicle ride comfort and motor operation performance. By conducting numerical simulations based on the developed quarter-car active suspension model and switched reluctance motor model, it is observed that the road roughness is highly coupled with SRM airgap eccentricity and unbalanced residual vertical force. The SRM airgap eccentricity is influenced by the road excitation and becomes time-varying such that a residual unbalanced radial force is induced; which is one of the major causes of SRM vibration. To suppress SRM vibration and to prolong the SRM lifespan, while at the same time improving vehicle ride comfort, a fault tolerant controller based on output feedback H∞ control method is designed to reduce the sprung mass acceleration. Moreover, an SRM controller is adapted by using the combined Current Chopping Control (CCC) and Pulse Width Modulation control (PWM) to further improve the SRM performance. A comparison of passive suspension and suspensions with hybrid control method on the vehicle and SRM dynamic response under stochastic road excitation and bump road excitation is illustrated. The results indicate that the proposed hybrid control method can effectively reduce the SRM airgap eccentricity, residual unbalanced radial force and achieve better vehicle ride comfort.

This paper presents a novel observer-based control strategy to improve the vehicle roll behavior performance through magneto-rheological (MR) dampers under steering wheel input and various road excitation conditions. Since the vehicle roll with sudden steering input is an essential part of driving safety and possesses inherent nonlinearities, the full-car nonlinear Takagi-Sugeno (T-S) fuzzy model is first established to describe the vehicle roll dynamics considering nonlinear coupling dynamics of tire lateral force and MR damper force under road excitation input. Furthermore, a T-S model-based fuzzy observer is adapted to estimate the vehicle roll angle and roll rate. The stability conditions for the used T-S observer are calculated using linear matrix inequalities (LMIs), and the proposed observer is induced by solving the proposed LMI. Based on the Lyapunov function, sliding mode theory and prescribed performance function, a novel state observer-based prescribed performance control strategy is developed to constrain the controlled vehicle roll angle and roll rate state within the prescribed performance boundaries. Finally, the proposed techniques are validated through the J-turn and Fishhook tests conducted via a high-fidelity CarSim software platform.

A single-time velocity estimator-based reinforcement learning (RL) algorithm, integrated with a chaotic metaheuristic optimization technique is proposed in this article for the optimal path-planning of the nonholonomic robots considering a moving/stationary obstacle avoidance strategy. The additional contribution of the present study is by employing the Terramechanics principles to incorporate the effects of wheel sinkage into the deformable terrain on the dynamics of the robot aiming to find the optimal compensating force/torque magnitude to sustain a robust and smooth motion. The designed systematic control-oriented system incorporates a cost function of weighted components associated with the target-tracking and the obstacle avoidance. The designed velocity estimator contributes to the finite-state Markov decision process (MDP) in order to train the transition probabilities of the problem objectives. Based on the obtained results, the optimal solution for the Q-learning in terms of the adjusting factor for the minimized tracking error and obstacle collision risk propagation profiles is found at 0.22. The results further confirm the promising capacity of the proposed optimization-based RL algorithm for the collision avoidance control of the nonholonomic robots on deformable terrains.

This paper proposes a novel 2-step global sensitivity analysis algorithm to provide an in-depth sensitivity analysis of the vehicle parameters on the system responses. A 9 degree-of-freedom...

This paper presents a novel integrated control framework that includes simultaneous vehicle lateral stabilization and path tracking by considering the combined-slip effect, wheel dynamics, and tire force nonlinearities for driver-assistance systems. Active front steering and brakes are the actuators of the developed slip-aware receding horizon control system, in which the loss of cornering forces caused by longitudinal slip dynamics is considered in the prediction model. The controller monitors tire capacities and normal forces, corrects driver’s input, and adjusts wheel torques and steering to provide safe performance in path tracking (e.g., lane-keeping) while stabilizing the vehicle within its handling limits. The main advantage of the developed slip-aware driver-assistance controller is the ability in handling multi-actuation vehicle systems through its more accurate prediction model. The performance and computational efficiency of the integrated strategy is evaluated in hardware-in-the-loop real-time experiments, in various pure- and combined-slip maneuvers, under different road friction conditions. Stability of the controller and longitudinal tire force observer is also investigated. The real-time experiments and simulations reveal that the proposed control framework outperforms the existing algorithms in dealing with reduced tire capacities in harsh maneuvers as a consequence of simultaneous vehicle and wheel stabilization, and path tracking.

This paper presents novel algorithms for vibration control of the in-wheel motor (IWM) driven electric vehicles to improve its ride comfort and reduce IWM vibration. A quarter vehicle model is first developed based on a dynamic vibration absorbing structure (DVAS) driven by a switched reluctance motor (SRM). This model considers the coupled longitudinal-vertical dynamics and the unknown road profile as well as the unbalanced electromagnetic force induced by the SRM are treated as the excitation. The dynamics and boundary models of two commercially available semi-active dampers are then presented, which are used as the actuators of both the suspension and the DVAS structure. Based on the developed model, a hybrid controller with a hybrid acceleration driven damping algorithms is proposed to reduce the vibration of the sprung mass and the SRM. The controller parameters are subsequently determined by solving the multi-objective optimization problem with a multi-objective evolutionary optimization method. Numerical simulation results for random road and bumpy excitations are analyzed, and multi-body simulation is finally performed to validate the feasibility of the proposed controllers. Results indicate that the proposed hybrid controllers can effectively improve ride comfort and reduce the SRM vibration compared with the traditional suspension system with IWM.

This paper addresses the path-tracking performance improvement of unmanned surface vessels (USVs) through a novel adaptive immersion and invariance (I&I) based robust adaptive control algorithm. The substantial drawback related to the control of USVs is the uncertain dynamics of the vessel, and unexpected environmental disturbances due to surges or crosswinds that promote the instability. The asymptotic stability of the closed-loop USV system is guaranteed based on the I&I stability theory, and the adaptation laws for the uncertain parameters are derived accordingly. The design variables of I&I algorithm are varied in a convex optimization problem with constraints on the control inputs. The robustness of the proposed algorithm is further assessed for the target USV subject to uncertain hydrodynamic damping in surge, sway and yaw. The performance of the proposed control algorithm is tested against benchmark schemes of disturbance observer-based composite nonlinear feedback (DO-CNF) and robust LQR (RLQR). The results are suggestive of the improved performance of the USV to follow the intended trajectory while guaranteeing optimality in the sense of actuation constraints.

An adaptive neural network (ANN) control method for a continuous damping control (CDC) damper is used in vehicle suspension systems. The control objective is to suppress positional oscillation of the sprung mass in the presence of road irregularities. To achieve this, a boundary model is first applied to depict dynamic characteristics of the CDC damper based on experimental data. To overcome nonlinearity issues of the model system and uncertainties in the suspension parameters, an adaptive radial basis function neural network (RBFNN) with online learning capability is utilized to approximate unknown dynamics, without the need for prior information related to the suspension system. In addition, particle swarm optimization (PSO) technique is adopted to determine and optimize the parameters of the controller. Closed loop stability and asymptotic convergence performance are guaranteed based on Lyapunov stability theory. Finally, simulation results demonstrate that the proposed controller can effectively regulate the chassis vertical position under different road excitations. Furthermore, the control performance is determined to be better than that of the typical Skyhook controller.

This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: first, to enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; second, an improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion; third, the RBFNN and composite nonlinear feedback (CNF) based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-MATLAB simulations.

Vehicle performance is largely affected by the properties of the suspension system, where semi-active suspension has been widely used in mass production of vehicles owing to its characteristics such as internal stability and low energy consumption. To solve the contradiction between ride comfort and road handling, road estimation based semi-active suspension has received considerable attention in recent years. In order to provide accurate estimation for advanced control strategies applications, this paper aims to develop a new method that can provide precise road class estimation based on measurable suspension system response (i.e. sprung mass acceleration, unsprung mass acceleration and rattle space). The response signal is first decomposed using wavelet packet analysis, and features in both time and frequency domains are subsequently extracted. Then, minimum redundancy maximum relevance (mRMR) is utilized to select superior features. Finally, a probabilistic neural network (PNN) classifier is applied to determine road classification output. The most representative semi-active control strategy, i.e. skyhook control, is used to validate this method, and simulation results with varying conditions including different control parameters and sprung mass are compared. The results show that unsprung mass acceleration is most suitable for road classification, and more robust to varying conditions in comparison to other responses.

This paper presents a new method to address issues associated with vehicle system state estimation using an unscented Kalman filter (UKF) with considering full-car system and nonlinear tire force under various international standards organization (ISO) road conditions. Due to the fact that practical road information is complex and noise covariance cannot be treated as a constant, the influence of varying vehicle system process noise variance and measurement noise covariance on the estimation accuracy of the UKF is first discussed. To precisely estimate road information, a novel road classification method using measured signals (vertical acceleration of sprung mass and unsprung mass) of vehicle system is proposed. According to road excitation levels, different road process variances are defined to tune the vehicle system’s variance for application of UKF. Then, road classification and UKF are combined to form an adaptive UKF (AUKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under varying road conditions. Simulation results reveal that the proposed AUKF algorithm has higher accuracy for state estimation of a vehicle system under various ISO road excitation condition.

This paper provides a new method to solve the problem of suspension system state estimation using a Kalman Filter (KF) under various road conditions. Due to the fact that practical road conditions are complex and uncertain, the influence of the system process noise variance and measurement noise covariance on the estimation accuracy of the KF is first analysed. To accurately estimate the road condition, a new road classification method through the vertical acceleration of sprung mass is proposed, and different road process variances are obtained to tune the system’s variance for the application of the KF. Then, road classification and KF are combined to form an Adaptive Kalman Filter (AKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under various road conditions. Simulation results show that the proposed AKF algorithm can obtain a high accuracy of state estimation for a suspension system under varying International Standards ...

This study investigates adaptive neural network (NN) state feedback control and robust observation for an active suspension system that considers parametric uncertainties, road disturbances and actuator saturation. An adaptive radial basis function neural network is adopted to approximate uncertain non-linear functions in the dynamic system. An auxiliary system is designed and presented to deal with the effects of actuator saturation. In addition, since it is difficult to obtain accurate states in practice, an NN observer is developed to provide state estimation using the measured input and output data of the system. The state observer-based feedback control parameters with saturated inputs are optimised by the particle swarm optimisation scheme. Furthermore, the uniformly ultimately boundedness of all the closed-loop signals is guaranteed through rigorous Lyapunov analysis. The simulation results further demonstrate that the proposed controller can effectively suppress car body vibrations and offers superior control performance despite the existence of non-linear dynamics and control input constraints.

This paper investigates the lane keeping control for four-wheel independently actuated autonomous vehicles. To guarantee the vehicle safety when the active-steering motor entirely fails, the steering manoeuvre is accomplished by the differential drive assisted steering (DDAS), which is generated by the differential moment between the front wheels. A novel adaptive multivariable super-twisting control strategy is proposed to realize the control objective in finite time, considering the multiple unknown and mismatched disturbances of the steering system with the chattering effect removed. In the sliding surface, a nonlinear function is designed to adaptively change the damping ratio of the closed-loop system so as to improve the transient performance of the lane keeping control in the faulty condition. The finite-time convergence of the closed-loop system is proved by Lyapunov function technique. Results of CarSim-Simulink simulations with a high-fidelity and full-car model have verified the effectiveness and robustness of the proposed controller in the lane keeping control with DDAS and guaranteeing high performance.

International audience; In this paper an intelligent robust feedback control approach is proposed for the nonlinear disturbed suspension system. The nonlinear controller is formulated based on the integral higher order sliding mode with control parameters optimized using Particle Swarm Optimization technique. A novel classifier invariant to changes in control parameters is developed to detect the road class. Subsequently in the real time, based on system responses the road level is detected by the classifier and accordingly the optimized control parameters are selected to implement the controller. The closed loop stability of the proposed approach is established and simulation results for different road classed are presented to show improvement in passenger comfort.

A novel road estimation method using an adaptive Kalman filter and an adaptive super-twisting observer (AKF-ASTO) is presented, which can meet the requirements for road excitation information of advanced suspension system. A Kalman filter is utilized to estimate the velocity of unsprung mass and control force, and the covariance matrixes of both process noise and measurement noise are adaptively tuned by a novel road classifier. The estimated variable and control force are then processed by an adaptive super-twisting observer to reconstruct the road profile and the convergence of the ASTO is ensured by a Lyapunov analysis. Simulation results for a quarter vehicle model show that AKF-ASTO can estimate both the road profile and the system states with higher accuracy compared to the existing method. The proposed method can be used for the varying International Standardization Organization (ISO) road levels, solely requiring the measurement of the accelerations of the sprung and unsprung masses.

This paper presents a comprehensive comparison and analysis for the effect of time delay on the five most representative semi-active suspension control strategies, and refers to four unsolved problems related to semi-active suspension performance and delay mechanism that existed. Dynamic characteristics of a commercially available continuous damping control (CDC) damper were first studied, and a material test system (MTS) load frame was used to depict the velocity-force map for a CDC damper. Both inverse and boundary models were developed to determine dynamic characteristics of the damper. In addition, in order for an improper damper delay of the form t+τ to be corrected, a delay mechanism of controllable damper was discussed in detail. Numerical simulation for five control strategies, i.e., modified skyhook control SC, hybrid control (HC), COC, model reference sliding mode control (MRSMC), and integrated error neuro control (IENC), with three different time delays: 5 ms, 10 ms, and 15 ms was performed. Simulation results displayed that by changing control weights/variables, performance of all five control strategies varied from being ride comfort oriented to being road handling oriented. Furthermore, increase in delay time resulted in deterioration of both ride comfort and road handling. Specifically, ride comfort was affected more than road handling. The answers to all four questions were finally provided according to simulation results.

A new road estimation based suspension hybrid control strategy is proposed. Its aim is to adaptively change control gains to improve both ride comfort and road handling with the constraint of rattle space. To achieve this, analytical expressions for ride comfort, road handling, and rattle space with respect to road input are derived based on the hybrid control, and the problem is transformed into a MOOP (Multiobjective Optimization Problem) and has been solved by NSGA-II (Nondominated Sorting Genetic Algorithm-II). A new road estimation and classification method, which is based on ANFIS (Adaptive Neurofuzzy Inference System) and wavelet transforms, is then presented as a means of detecting the road profile level, and a Kalman filter is designed for observing unknown states. The results of simulations conducted with random road excitation show that the efficiency of the proposed control strategy compares favourably to that of a passive system.

The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN) is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

A new method for road profile estimation in time domain with the application of vehicle system response was presented in this paper, and the problem was transformed as a system identification issue for an inverse nonlinear quarter vehicle model. Firstly, the inverse vehicle dynamic model was trained with specifically chosen white noise signal, and then eight different types of membership functions (MF) for Adaptive Neuro Fuzzy Inference System (ANFIS) were compared. Finally, the comparison of three different methods: ANFIS, Recursive Least Square (RLS) and Group Method of Data Handling (GMDH) were researched with different vehicle speeds and different road levels in the simulation part. The results showed that ANFIS is better in comparison with RLS and GMDH and this method can be further applied for vehicle system analysis.Copyright © 2014 by ASME

The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.