Automotive suspension systems play a critical role in improving the safety and comfort of automobiles. However, the active suspension system may have actuator faults during the actual operation, and to improve the safety of the suspension system, it is necessary in order to carry out fault-tolerant control research on the suspension system. In this paper, a guaranteed cost-based fault-tolerant controller is designed based on actuator failures of nonlinear suspension systems. Based on guaranteeing the suspension performance, the conditions for the asymptotically stable suspension system are given by linear matrix inequality. The controller is achieved by solving the linear matrix inequality function. Finally, the simulation is discussed. Depending on the simulation results, the faulty system can still meet the required performance index under the proposed control algorithm.

Reducing vehicle energy consumption is crucial for sustainable development, especially in the context of energy crises and environmental pollution. Energy regenerative suspension offers a promising solution, yet its practical implementation faces challenges like inertial mass issues, cost, and reliability concerns. This study introduces a novel suspension configuration, optimizing shock absorber technology with energy regenerative principles. The objective is to drastically cut energy consumption. Through a frequency domain analysis, this study identifies the root causes of increased energy consumption and worsened vibration in traditional suspensions. This study presents a comparative analysis of the frequency-domain characteristics between the novel suspension configuration and the traditional one. This study reveals that the new configuration exhibits a low-pass filtering effect on the shock absorber’s velocity, effectively minimizing vibrations in the low-frequency range, while mitigating their impact in the high-frequency range. This approach mitigates the trade-off between increased energy consumption and worsened vibration in the high-frequency range, making it a promising solution. Simulations show that this configuration significantly reduces acceleration by 7.04% and suspension power consumption by 10.47% at 60 km/h on the D-level road, while maintaining handling stability. This makes it a promising candidate for future energy-efficient suspension systems.

In order to guide the design of quasi-zero stiffness isolator with asymmetric stiffness and mismatched load based on X-shaped negative stiffness structure, the bifurcation and the chaos-occurring parameter region are investigated. The equilibrium point bifurcation with different load conditions and stiffness types is first obtained, and the invariant manifolds at specific parameters can be compared for four operating conditions based on a combination of two asymmetric stiffnesses and two mismatch loads. The displacement transmissibility and the direct current component of response are analyzed under these four operating conditions, and based on the results, it can be found that the four operating conditions can be divided into two categories, each with a similar pattern. Finally, the chaos-occurring parameter region is analyzed by means of largest Lyapunov exponent, and the maximum external excitation amplitude is used to indirectly evaluate the operation stability margin of these four operating conditions.

To address the critical challenges impeding the progress and utilization of electromechanical suspensions, this paper examines the negative consequences of inertial mass on the acceleration of the sprung mass and structural reliability issues. It then introduces and optimizes two novel suspension configurations featuring a series buffering and damping structure (SBDS). Subsequently, a two-degree-of-freedom electromechanical suspension model with SBDS is proposed and formulated. Utilizing the probability method, the paper also derives the vibration transmission characteristic formulas for the SBDS-integrated suspension. Theoretical analysis suggests that the integration of SBDS is advantageous in reducing the inertial force generated by the inertial mass in electromechanical suspensions. Additionally, the impact of parameter variations in SBDS on suspension characteristics is examined, providing theoretical guidance for the design of this structure. Building upon the proposed suspension theory incorporating the novel SBDS configuration, a butterfly spring buffer is employed to replace the connecting rod in the traditional electromechanical suspension design. Bench tests validate that the new electromechanical suspension configuration with SBDS addresses the issue of performance degradation in the high-frequency range due to inertial mass, thereby enhancing the overall performance of electromechanical suspensions and facilitating their further development and application.

Active suspension control technologies have become increasingly significant in improving suspension performance for driving stability and comfort. An RBF-based fractional-order SMC fault-tolerant controller is developed in this research to guarantee ride comfort and handling stability when faced with the partial loss of actuator effectiveness due to failure. To obtain better control performance, fractional-order theory and the RBF algorithm are discussed to solve the jitter vibration problem in SMC, and the RBF is exploited to obtain a more appropriate switching gain. First, a half-nonlinear active suspension model and a fault car model are presented. Then, the design process of the RBF-based fractional-order SMC fault-tolerant controller is described. Next, a simulation is presented to demonstrate the effectiveness of the proposed strategy. According to the simulation, the proposed method can improve performance in the case of a healthy suspension, and the fault-tolerant controller can guarantee the capabilities when actuators go wrong.

This paper presents an integrated control scheme for enhancing the ride comfort and handling performance of a four-wheel-independent-drive electric vehicle through the coordination of active suspension system (ASS) and anti-lock braking system (ABS). First, a longitudinal-vertical coupled vehicle dynamics model is established by integrating a road input model. Then the coupling mechanisms between longitudinal and vertical vehicle dynamics are analyzed. An ASS-ABS integrated control system is proposed, utilizing an H ∞ controller for ASS to optimize load transfer effect and a neural network sliding mode control for ABS implementation. Finally, the effectiveness of the proposed control scheme is evaluated through comprehensive tests conducted on a hardware-in-loop (HIL) test platform. The HIL test results demonstrate that the proposed control scheme can significantly improve the braking performance and ride comfort compared to conventional ABS control methods.

This study aims to improve the vehicle vertical dynamics performance in the sprung and unsprung state for in-wheel-motor-driven electric vehicles (IWMD EVs) while considering the unbalanced electric magnetic force effects. An integrated vibration elimination system (IVES) is developed, containing a dynamic vibration-absorbing structure between the IWM and the suspension. It also includes an active suspension system based on a delay-dependent H∞ controller. Further, a novel frequency-compatible tire (FCT) model is constructed to improve IVES accuracy. The mechanical-electrical-magnetic coupling effects of IWMD EVs are theoretically analyzed. A virtual prototype for the IVES is created by combining the CATIA, ADAMS, and MatLab/Simulink, resulting in a high-fidelity multi-body model, validating the IVES accuracy and practicability. Simulations for the IVES considered three different suspension structure types and time delay considerations were performed. Analyses in frequency and time domains for the simulation results have shown that the root mean square of sprung mass acceleration and the eccentricity are significantly reduced via the IVES, indicating an improvement in ride comfort and IWM vibration suppression.

In order to address issues related to performance degradation and low suspension reliability resulting from the inertia mass of regenerative suspension, a structure for attenuating inertial force and a control solution of regenerative suspension with the Series buffering and damping structure (SBDS) are proposed. Using a two-degree-of-freedom suspension model, the energy dissipation and inertial mass vibration enhancing areas of the regenerative suspension in the frequency domain are analyzed; The two-degree-of-freedom model for the regenerative suspension with the SBDS is established, and the frequency domain characteristics of the regenerative suspension with and without the SBDS are analyzed, which shows that the SBDS improves the sprung loaded mass acceleration, the dynamic deflection of suspension, and reduces inertial force. Additionally, it exhibits a low-frequency pass and high-frequency block effect on the shock absorber speed, leading to reduced power dissipation. By employing the skyhook damping control algorithm, the comparative analysis shows that using shock absorber speed as the control input is more effective, and the SBDS decreases suspension power dissipation. Bench comparison testing confirms the effective enhancement of regenerative suspension performance by the SBDS.

Active suspension plays a pivotal role in modern vehicles. In this paper, an adaptive PID controller of active suspension systems based on RBF neural network (RBF-NN) is developed. A quarter-car suspension system with two degrees of freedom is demonstrated. The values of proportional, integral, and derivate components are obtained by using Ziegler-Nichols(Z-N) tuning method and RBF-NN methods. The suspension system is perturbed using the sine function. Simulated in the Simulink environment is the quarter-car model. Passive suspension systems, adaptive PID controller utilizing the Z-N tuning approach, and adaptive PID based on the RBF-NN method for active suspension systems are compared. The active suspension with PID control based on the RBF-NN outperformed the active suspension with PID control utilizing the Z-N tuning approach and passive suspension, according to simulation data. The comparison demonstrates the proposed control method’s superior features

针对越野车辆对操纵稳定性与安全性要求较高的问题,提出了一种基于车辆实时运动状态参数对其操纵稳定性进行优化的模型预测控制器(MPC)与线性二次型调节器(LQR)的多模态混合控制器.首先,根据车辆运动学方程搭建车辆二自由度理论模型;然后根据理论模型求出其状态空间方程并对MPC,LQR控制器以及多模态混合控制器进行设计;随后搭建基于Carsim/Simulink的虚拟联合仿真平台,针对两种典型的操纵稳定性试验工况进行仿真验证与对照.结果表明,相较于单纯的MPC或LQR控制器,所设计的多模态混合控制器能够有效地提高越野车在恶劣路况下的操纵稳定性与安全性.

Based on the rotor rotation speed of the in wheel motor, an anti-lock braking system (ABS) control strategy with slip ratio self-adaptive characteristics is constructed for unknown and complex road surface adhesion conditions, which is capable of adaptively tracking the optimal slip ratio without predicting the road surface adhesion conditions and has strong adaptability to complex and variable road surface conditions. The simulation results verify the adaptability of the vehicle under switching adhesion conditions between dry asphalt and snowy road surfaces.

In this paper, the fault tolerant control problem of nonlinear active suspension is concerned. A RBF-based SMC fault tolerant controller is developed to improve the ride comfort and handling stability under partial loss of actuator effectiveness fault. At first, a quarter car nonlinear active suspension model and fault model are established. Next a RBF-based SMC adaptive controller is conducted for the faulty suspension system. Simulation is provided and simulation results show that the proposed method can effectively compensate for faults and improve the ride comfort. Then the proposed controller is verified by the simulation.

Suspension systems are critical parts of modern cars. In this study, a radial basis function neural networks-based adaptive PID optimal method is presented for vehicle suspension systems. To avoid the shortcoming that the parameters of PID control are determined by experience in the traditional method, to avoid the local optimality problem and the slow rate of convergence in the modern intelligence method, radial basis function neural networks are applied in this paper. First, a quarter-car suspension is presented. Then, the radial basis function neural networks are employed to obtain the parameters of proportional, integral, and derivate components that are used in PID control. The simulation is conducted later. Next, a comparison of the progress between uncontrolled suspension, the radial basis function-based PID control, the H∞ control method, and the FPM control method is presented. According to the simulation results, the proposed control method performs better than the others. This contrast reveals the superior characteristics of the suggested control strategy.

In this paper, a hybrid particle swarm optimization genetic algorithm LQR controller is used on a quarter car model with an active suspension system. The proposed control algorithm is utilized to overcome the shortcoming that the weight matrix Q and matrix R determined by experience in the traditional LQR control method. The proposed hybrid control method makes it possible to achieve the optimal control effect. A full-order state observer is proposed to observe the state of active suspension. A quarter car active suspension model and road input model are presented at first, and the LQR controller based on the hybrid particle swarm optimization genetic algorithm is utilized in the active suspension system control. Sprung mass acceleration, suspension deflection, and tire dynamic load are selected as the control effect evaluation index. Next, simulation results are presented. According to the results, compared with the passive suspension and active suspension with a traditional LQR control, there is an obvious reduction in the sprung mass acceleration, deflection, and tire dynamic load with an optimized controller under case 1 and case 2. Simultaneously, the system state fed back by the full-order state observer can effectively reflect the true state of the active suspension system.

In-wheel-motor-drive electric vehicles have attracted enormous attention due to its potentials of improving vehicle performance and safety. Road surface roughness results in forced vibration of in-wheel-motor (IWM) and thus aggravates the unbalanced electric magnetic force (UEMF) between its rotor and stator. This can further compromise vertical and longitudinal vehicle dynamics. This paper presents a comprehensive study to reveal the coupled vertical–longitudinal effect on suspension-in-wheel-motor systems (SIWMS) along with a viable optimization procedure to improve ride comfort and handling performance. First, a UEMF model is established to analyze the mechanical–electrical–magnetic coupling relationship inside an IWM. Then a road–tire–ring force (RTR) model that can capture the transient tire–road contact patch and tire belt deformation is established to accurately describe the road–tire and tire–rotor forces. The UEMF and the RTRF model are incorporated into the quarter-SIWMS model to investigate the coupled vertical–longitudinal vehicle dynamics. Through simulation studies, a comprehensive evaluation system is put forward to quantitatively assess the effects during braking maneuvers under various road conditions. The key parameters of the SIWMS are optimized via a multi-optimization method to reduce the adverse impact of UEMF. Finally, the multi-optimization method is validated in a virtual prototype which contains a high-fidelity multi-body model. The results show that the longitudinal acceleration fluctuation rate and the slip ratio signal-to-noise ratio are reduced by 5.07% and 6.13%, respectively, while the UEMF in the vertical and longitudinal directions varies from 22.2% to 34.7%, respectively, and is reduced after optimization. Thus, the negative coupling effects of UEMF are minimized while improving the ride comfort and handling performance.

During vehicle braking, when vehicles move on the road with unknown road roughness elevation and unknown tire/road friction coefficient, fewer sensors shall be used for vehicle braking closed-loop control and braking distance prediction to obtain the dynamic states of the vehicle suspension and tire systems. In this paper, a vehicle dynamic model is established in Carsim software. Modify lump LuGre friction model and road roughness elevation model of four tires are proposed based on matlab. When vehicles brake on the road with time-varying split-μ, a braking control algorithm established in this paper. The road roughness elevation and the braking force of each tire are supplied to the vehicle dynamic model in Carsim. A state estimate algorithm of suspension system is proposed. The scheme for minimum sensor of this estimator is determined. A state estimate algorithm of the tire/road friction using only tire angular velocity information is proposed. When vehicles brake on the road with different levels of roughness, the influence of the number of installation groups of the sensors, the tire vertical stiffness deviations, and the measurement noise on the estimation error of the estimator is analyzed. When the vehicle is driving on the road with unknown adhesive ability, based on the estimator of tire/road friction using only tire angular velocity information, the tire/road friction internal state, the changes of road adhesive ability, and the vehicle velocity are estimated well.

The quasi-zero stiffness (QZS) mechanism is typically implemented by paralleling negative and positive stiffness structures to enhance the vibration isolation effect. As the core component, a novel bistable X-shaped negative stiffness structure (XNSS) is designed. Based on the geometric relationship between angular and displacement coordinates, the single-layer and multilayer mechanical model is derived from the principle of virtual work. After the non-dimensional process, a set of system parameters is summarized. Ensuring that these system parameters satisfy constraints, the comprehensive effects on XNSS are studied in detail. The parallel connection of XNSS and linear stiffness mechanism can constitute a QZS vibration isolator, and the influence of system parameters on loading capacity, stability of equilibriums, and dynamic stiffness are discussed in detail. The amplitude-frequency response and displacement transmissibility of nonlinear QZS vibration isolation model show excellent vibration isolation performance, compared with the corresponding linear system. The results show that the static analysis of the XNSS system parameters can be a good guide to the design of the QZS vibration isolator in order to obtain better dynamic performance.

This paper investigates the problem of artificial fish swarm algorithm (AFSA) in LQR control of active suspension. To find the optimal solution, AFSA is used to overcome the shortcoming that the coefficients are determined based on experience in the LQR, which cannot guarantee the optimal solution. At first, the quarter car active suspension model and the road excitation model are given. Then the LQR controller and LQR controller with AFSA are designed. And at last, the simulation is conducted. Compared with the conventional LQR control, LQR controller with ASFA can concurrently improve both the ride comfort and driving control stability of vehicle.

With regard to the structural characteristics of the McPherson suspension system, when a vehicle is being driven on a rough road surface, the force direction of the suspension varies. This poses challenges to the vehicle’s driving safety and handling stability. Based on Lagrangian equations, this paper proposes a new nonlinear semi-vehicle suspension model and presents comparative studies, conducted through simulation, on the estimated accuracy and computational overhead of the small-computational-overhead extended Kalman filter (EKF) and unscented Kalman estimation (UKF) methods, and on the effectiveness of the skyhook sliding mode control (SHSMC) and nonlinear skyhook-sliding mode control (NSHSMC) semi-active suspension control methods. The response of the vehicle to the state estimation algorithm was evaluated through computer simulations using the Carsim vehicle dynamic software. The simulation results reveal that the vehicle dynamic states were satisfactorily estimated when the vehicle was driven on a rough road surface. Compared with the small-computational-overhead EKF algorithm, the estimated results of these variables based on the UKF algorithm have higher accuracy. However, the UKF algorithm requires longer computation time compared with the EKF algorithm. The SHSMC control algorithm achieved greater improvement for the vehicle’s drive handling stability in the 6–10-Hz vibration region compared with the NSHSMC control algorithm. In a high-frequency region over 10Hz, the semi-active suspension controlled by the SHSMC method had a more adverse effect on the driving comfort.

Efficient, safe, and comfortable electric vehicles (EVs) are essential for the creation of a sustainable transport system. Distributed-driven EVs, which often use in-wheel motors (IWMs), have many benefits with respect to size (compactness), controllability, and efficiency. However, the vibration of IWMs is a particularly important factor for both passengers and drivers, and it is, therefore, crucial for the successful commercialization of distributed-driven EVs. This article provides a comprehensive literature review and state-of-the-art vibration-source analysis and mitigation methods in IWMs. First, selection criteria are given for IWMs, and a multidimensional comparison for several motor types is provided. The IWM vibration sources are then divided into internally and externally induced vibration sources and discussed in detail. Next, vibration reduction methods, which include motor-structure optimization, motor controller, and additional control components, are reviewed. Emerging research trends and an outlook for future improvement aims are summarized at the end of this article. This article can provide useful information for researchers who are interested in the application and vibration mitigation of IWMs or similar topics.