Magnetorheological Semi-active Suspension Research Articles

Research on robust backstepping sliding mode control strategy for magnetorheological semi-active suspension based on quantized input signals

Research on robust backstepping sliding mode control strategy for magnetorheological semi-active suspension based on quantized input signals

A Variable Horizon Model Predictive Control for Magnetorheological Semi-Active Suspension with Air Springs.

To improve the characteristics of traditional model predictive control (MPC) semi-active suspension that cannot achieve the optimal suspension control effect under different conditions, a variable horizon model predictive control (VHMPC) method is devised for magnetorheological semi-active suspension with air springs. Mathematical models are established for the magnetorheological dampers and air springs. Based on the improved hyperbolic tangent model, a forward model is established for the magnetorheological damper. The adaptive fuzzy neural network method is used to establish the inverse model of the magnetorheological damper. The relationship between different road excitation frequencies and the control effect of magnetorheological semi-active suspension with air springs is simulated, and the optimal prediction horizons under different conditions are obtained. The VHMPC method is designed to automatically switch the predictive horizon according to the road surface excitation frequency. The results demonstrate that under mixed conditions, compared with the traditional MPC, the VHMPC can improve the smoothness of the suspension by 2.614% and reduce the positive and negative peaks of the vertical vibration acceleration by 11.849% and 6.938%, respectively. Under variable speed road conditions, VHMPC improved the sprung mass acceleration, dynamic tire deformation, and suspension deflection by 7.191%, 7.936%, and 22.222%, respectively, compared to MPC.

A fuzzy skyhook control strategy optimized by particle swarm strategy for magnetorheological semi-active suspension

In order to solve the problem of unsatisfactory control effect caused by uncertain parameters in the fuzzy skyhook control method, a fuzzy skyhook control strategy optimized by particle swarm strategy is proposed. The magnetorheological (MR) damper is put on the bench for mechanical characteristics experiment, and the forward model of MR damper is established. The adaptive network fuzzy inference system (ANFIS) system in simulation software is used to build its inverse model to verify the accuracy of the forward model of MR damper. A quarter vehicle suspension model is established, and a fuzzy skyhook control strategy based on particle swarm optimization is designed. Numerical simulations are carried out under the excitation of random road. The acceleration, suspension dynamic deflection and tire dynamic load are used to evaluate its performance. Compared with the passive control, the root mean square (RMS) values of the acceleration for the fuzzy skyhook control strategy are improved by 41.9% and 37.6%, the RMS values of the suspension dynamic deflection for the fuzzy skyhook control strategy are improved by 53.3% and 48.0%, and the RMS values of the tire dynamic load for the fuzzy skyhook control strategy are improved by 17.6% and 14.5% under the B-Class and C-Class road excitations. The simulations and experimental results verify the effectiveness of the controller.

Adaptive neural network backstepping control for the magnetorheological semi-active air suspension system with uncertain mass and time-varying input delay

Aiming at rejecting external disturbance induced by the random road profile, this paper proposes an adaptive robust control strategy to address the control problem of semi-active air suspension system installed with magnetorheological dampers (MRD) for enhancing ride comfort and handling performance. To effectively depict the inherent nonlinear dynamics of the adjustable air spring, a radial basis function neural network (RBFNN) model is trained by the experimental data offline, and an adaptive learning algorithm is introduced to maintain the modeling accuracy. Moreover, to handle the prevalent sensitive parameter variation (such as sprung mass), a nonlinear estimator is designed to estimate the uncertain sprung mass. Furthermore, a delay compensator is integrated into the control law to mitigate the impact of the time-varying input delay caused by the actuator of MRD to restrain the chattering phenomena. Additionally, the stability of the closed-loop system is rigorously proven by employing a Lyapunov–Krasovskii functional, guaranteeing the boundedness of both tracking error and estimation error. Simulation results are displayed and analyzed, illustrating the feasibility and efficiency of the proposed control strategy in depressing the sprung mass acceleration and tire deflection, showing its significance in both control performance enhancement and chattering elimination.

Design and evaluation of self-tuning optimal control for vibration suppression of magnetorheological semi-active suspension

The objective of this paper is to enhance the vibration damping capabilities of magnetorheological (MR) semi-active suspension by utilizing a self-tuning LQG control method. Firstly, to determine the mechanical model of MR damper using the hyperbolic tangent model, mechanical experiments were performed with a damping force test machine under various input excitations. Experimental and simulation data were compared to confirm the accuracy of the mechanical model for MR damper. The MR damper is then incorporated into the vehicle’s semi-active suspension system to create a quarter-car suspension model. In order to tackle the problems associated with low control accuracy and poor dynamic adjustment in traditional LQG controllers where the weighting matrix coefficients are difficult to determine, a self-tuning LQG controller based on gravity search algorithm (GSA-LQG) was designed. Finally, the designed controller performance was evaluated by the simulation and experimental results obtained from the random and sinusoidal excitation roads. Experimental data reveal that when subjected to GSA-LQG control, the suspension control performance is considerably enhanced compared to the passive control, PID control and conventional LQG control suspension. Based on these outcome, it can be concluded that the proposed algorithm is effective and the experimental platform is feasible.

Technical study of magnetorheological semi-active suspension control strategy for vehicles based on reinforcement learning

The suspension system is an important part of the car, the main function is to alleviate the road surface does not level brought about by the shake, to ensure that the vehicle driving smoothness and comfort, so as to improve the vehicle handling performance and safety, improve the vehicle to reduce the vibration performance more and more become a hot topic in the industry. Among them, semi-active suspension is a popular suspension system. This paper explains the significance and classification of the role of the suspension, and gives a relevant introduction to the magnetorheological fluid, on the basis of which it combines the domestic and international research results, looks forward to the development trend of the semi-active suspension of the vehicle, analyzes the advantages and shortcomings of four important semi-active suspension control strategies, and finally introduces the reinforcement learning and combines the reinforcement learning with the existing control strategies of the semi-active suspension, and designs a reliable reward function to enhance the suspension's damping performance by taking into account the multifaceted driving factors. The reward function to enhance the damping ability of the suspension by considering multiple driving factors, which can provide a reference for further research on the control of magnetorheological semi-active suspensions for vehicles.

Research on performance magnetorheological semi-active suspension based on improved gray wolf algorithm-optimized linear quadratic regulator control

Research on performance magnetorheological semi-active suspension based on improved gray wolf algorithm-optimized linear quadratic regulator control

An adaptive backstepping sliding mode control strategy for the magnetorheological semi-active suspension

This paper proposes an adaptive backstepping sliding mode control strategy for addressing nonlinear issues in the semi-active suspension systems, such as uncertainties and external disturbances. Firstly, a hyperbolic tangent model is chosen for parameters identification of the magnetorheological (MR) damper, and a model for the semi-active suspension system is established. Secondly, a control strategy is designed by combining the backstepping and sliding mode control strategies, and adaptive methods are employed to mitigate external disturbances, enhance the robustness of the controller, and estimate system uncertainties. Finally, the stability and controllability of the closed-loop system are verified using Lyapunov theory. Under the road excitations of A-Class, B-Class, and speed bump, the dynamic characteristics of the passive control, backstepping sliding mode control, and adaptive backstepping sliding mode control strategies applied to the MR semi-active suspension are analyzed. The vertical acceleration of vehicle body, suspension dynamic deflection, and tire dynamic load are selected as evaluation indexes, the results indicate that this control strategy significantly improved the ride comfort and handling stability of the vehicle.

Hybrid damping control of magnetorheological semi-active suspension based on feedback linearization Kalman observer

Hybrid damping control of magnetorheological semi-active suspension based on feedback linearization Kalman observer

An adaptive backstepping control strategy based on radial basis function neural networks for the magnetorheological semi-active suspension

Owing to nonlinear issues such as external disturbances and uncertain parameters within the semi-active suspension system (SASS), the vibration amplitude of the suspension system tends to increase, and the time required for the suspension system to reach a steady-state response is prolonged. Hence, this paper proposes an adaptive backstepping control strategy based on radial basis function neural networks (RBF-NNs). Firstly, the damping force characteristics of the magnetorheological (MR) damper are tested, and the experimental data are utilized for parameters identification and fitting of the Bouc-Wen model. To establish a connection between the controller and the forward model of the MR damper, the forward model of the MR damper, the inverse model of the MR damper, and the model of the MR-SASS are constructed. Secondly, the backstepping controller and the adaptive backstepping controller based on RBF-NNs are designed. The stability and reliability of the closed-loop suspension system are verified through stability analysis using Lyapunov function. Finally, the dynamic characteristics of the passive control, backstepping control, and adaptive backstepping control strategies based on RBF-NNs applied to MR-SASS are analyzed under B-Class road excitation and speed bump road excitation. The acceleration, suspension dynamic deflection, and tire dynamic load are selected as the evaluation indices. The results demonstrate that the adaptive backstepping controller based on RBF-NNs significantly enhances the ride comfort of the SASS.

Performance analysis of vehicle magnetorheological semi-active air suspension based on S-QFSMC control

The performance of the suspension is a crucial criterion for evaluating both vehicle handling and passenger comfort. To enhance suspension performance, this study proposes the design of a Quantum Genetic Fuzzy Sliding Mode Controller (S-QFSMC) based on the Smith predictor estimator, building upon the foundation of the vehicle magneto-rheological semi-active air suspension. According to the physical model of the vehicle suspension, a mechanical model of a quarter-vehicle magneto-rheological semi-active air suspension with time delay is established. On this basis, a conventional sliding mode controller is designed, and quantum genetic algorithm and fuzzy control principles are employed to optimize the chattering issue associated with sliding mode control. The Smith predictor estimator is utilized to effectively compensate for the time delay in the suspension system. Subsequently, a simulation analysis of the vehicle suspension performance is conducted. The results indicate that, compared to passive suspension control, both the QFSMC controller and the S-QFSMC controller improve the suspension performance, with the S-QFSMC controller exhibiting superior comprehensive improvement. This validates the effectiveness of the designed controllers.

Explicit model predictive control of magnetorheological suspension for all-terrain vehicles with road preview

The integration of magnetorheological (MR) semi-active suspension systems in all-terrain vehicles (ATV) has garnered significant attention due to their ability to enhance damping performance and off-road capabilities. However, traditional control strategies result in poor control accuracy and limited vibration reduction effects when facing complex road excitations and impact disturbances. With technological advancements, enhanced vehicle environmental perception and road sensing capabilities have made it possible to implement model predictive control (MPC) for vehicle suspensions. Nevertheless, traditional MPC is limited in vehicle suspension applications due to its high computational complexity. To address these issues, this study introduces an explicit model predictive control based on road preview (EMPC-P). Firstly, road data obtained through a non-contact measurement method enables the system to perceive road excitation information in advance. Subsequently, a 7 Degree-of-Freedom (7-DOF) suspension model incorporating road excitations is constructed. By adhering to system constraints and employing a multiparameter optimization method, the control problem based on rolling optimization is transformed into an explicit polyhedral system. The offline precomputation of control state relations enhances the computational efficiency of the control system. Through this approach, the designed EMPC allows the vehicle suspension system to make optimal control decisions quickly and accurately in response to complex driving conditions, thus improving the damping effect of the system. Through a combined approach of simulation and experimental validation, the designed EMPC-P controller is compared with the Skyhook controller under preview and non-preview states, respectively. Empirical testing confirms that the EMPC-P exhibits superior damping effects, significantly improving vehicle ride comfort and handling stability.

Integrated control strategy of semi-active suspension and in-wheel motors for handling limit of electric race car

An integrated control strategy is proposed to deal with extreme driving conditions for race cars using magnetorheological (MR) semi-active suspension and in-wheel motors. A novel controllable boundary is introduced as the vehicle state supervisor based on the phase portrait of yaw rate and side slip angle. Compared with the traditional stable region, it can better cope with the aggressive behaviors of racing drivers. The proposed integrated control algorithm consists of a controllable boundary-based hierarchy direct yaw moment controller (HDYC) and acceleration-based scheduling MR damper controller. The HDYC will generate the correct yaw moment based on the sliding mode method when vehicle states exceed the critical area of the controllable region. The MR damper controller includes vehicle attitude control (VAC) mode and maneuverability control (MC) mode. The switch of control mode is determined by vehicle states as well. According to the analysis of load transfer, the acceleration scheduling strategy based on the “g-g” ellipse is utilized to generate the desired current command. Three typical scenarios are conducted through co-simulation of Carsim and Simulink to verify the control strategy, including accelerating and hard braking test, double lane change test, and duration circuit test. Especially, the limit of actuators is taken into consideration. The simulation results show that the proposed integrated control strategy could reduce the lap time and increase the maneuverability as well as stability of race cars, compared with traditional direct yaw moment control. The ultimate acceleration of the vehicle is improved significantly.

Research on the Multimode Switching Control of Intelligent Suspension Based on Binocular Distance Recognition

As the upgrade of people’s requirements for automotive driving comfort, conventional passive suspensions for cars have fallen short of existing demands due to their nonadjustable damping and stiffness, so semiactive suspensions and active suspensions have gained growing acceptance. Compared with active suspensions, semiactive suspensions offer the advantages of a low manufacturing cost and reliable structure, and thus have become the preferred choice for most vehicles. To optimize the control effect of semiactive suspensions under different working conditions, this paper completed the modeling of magnetorheological semiactive suspension system dynamics and road inputs; then, the design of binocular camera sensing algorithms was performed to obtain the real-time distance of the target using the point cloud ranging function, and the parameters required for suspension control were also obtained. This was followed by the completion of the control-mode-switching rules and the design of the suspension controller. According to the different control objectives, the mode could be divided into the obstacle-road mode, straight-road mode, and curved-road mode. The suspension controller included the BP-PID (neural network PID controller) controller and the force distributor. Finally, the effectiveness of the mode-switching rules and the control method was verified through system simulation and the hardware-in-the-loop test.

Robust predictive compensation control for lateral magnetorheological semi-active suspension of high-speed trains with time delay

ABSTRACT Aiming at reducing the effect of time delay on the control performance of magnetorheological (MR) damper semi-active suspension, a control strategy with strong robustness to time delay is investigated in this study, including the force controller of MR damper, the robust controller and the predictive compensator. The force controller of MR damper is built based on the hyperbolic tangent function; the robust controller is built based on the simplified model of car body lateral vibration and is designed by μ-synthesis method considering the effect of time delay; then the Smith predictive model and the lateral velocity predictor are designed, which form the predictive compensator of semi-active controller to reduce the effect of time delay. To validate the effectiveness of the proposed control strategy, several conventional semi-active controllers are designed as comparative objects. The simulation results show that the proposed semi-active control strategy can maintain good performance when time delay is in the range of 0–100 ms and has better robustness to time delay compared with the conventional control strategy.

Gray skyhook predictive control of magnetorheological semi-active seat suspension with time delay

Semi-active seat suspension with a magnetorheological (MR) damper has been a popular study issue in recent years. Since the response time delay of the MR damper can reduce the control effect and even make the vibration more severe, there is an urgent need to compensate for the response time delay. In this paper, gray prediction is introduced into the vibration control study of commercial vehicle seat suspension. A gray skyhook prediction controller is designed to compensate for the response time delay. This proposed controller does not rely heavily on controller parameter optimization and requires fewer state variables than other typical time delay compensation controllers. The mechanical property of the MR damper has been tested, modeled, and analyzed. A seat suspension dynamic model considering geometric nonlinearity is established based on the motion relationship between the components of the suspension. Subsequently, the effect of the response time delay on seat suspension vibration control has been verified by simulation. The results show that the control effect deteriorates significantly after considering the response time delay. Finally, prediction accuracy and vibration reduction performance of the designed gray skyhook predictive controller are verified by simulation and bench testing. The results illustrate that the gray skyhook predictive controller provides excellent time delay compensation and can significantly improve the ride comfort and handling stability of the driver. This study provides a reference for vibration control research of the MR seat suspension.

Nonlinear dynamic modeling and analysis of magnetorheological semi-active suspension for tracked vehicles

The magnetorheological technology has been widely used in automotive applications, whereas less research has been done on tracked vehicles. Hence, this research focuses on the dynamic modeling of the whole system of a tracked vehicle, for analyzing the effect of magnetorheological semi-active suspension on track tension and chassis vibration during driving. Applying the multibody system transfer matrix method and a dynamic recursive method, the dynamic models of the chassis subsystem and the double track subsystems are developed, respectively. These three subsystems are coupled by nonlinear contact forces, where the meshing shock, polygon effect, and rolling effect during contact are also taken into consideration. Subsequently, the normalized Bouc-Wen model is adopted to describe the nonlinear characteristics of rotary magnetorheological dampers. Then, a semi-active suspension control of the tracked vehicle is implemented with rotary magnetorheological dampers and the independent skyhook control method. Finally, numerical simulations are performed under different operating conditions. Comparing with the case of passive suspension, it reveals that, the magnetorheological semi-active suspension not only improves the distribution of the track tension but also effectively reduces the chassis vibration. It provides a theoretical basis for the design and application of magnetorheological semi-active suspensions for tracked vehicles.

Research on the Performance of Magnetorheological Semi-Active Suspension with Stepped By-Pass Valve based on Fuzzy PID Control

Research on the Performance of Magnetorheological Semi-Active Suspension with Stepped By-Pass Valve based on Fuzzy PID Control

Research on time-delay-dependent H∞/H2 optimal control of magnetorheological semi-active suspension with response delay

Considering the influence of the magnetorheological damper response time delay on the low-frequency vibration (body vibration) and multi-objective control requirements of the semi-active suspension system, a novel H∞/H2 controller (time-delay-dependent H∞/H2 optimal controller) is proposed in this paper. Based on the Lyapunov–Krasovskii function and robust control theory, a time-delay-dependent H∞/H2 controller in consideration of the MR damper response time delay is designed to reduce and remove the effects of time delay on the semi-active suspension system. To obtain better suspension control performances, the optimal selection strategy of anti-interference coefficients in the time-delay-dependent H∞/H2 controller is proposed. Then, the time-delay-dependent H∞/H2 optimal controller is structured built on the optimal selection strategy. With the application of matrix properties and the cone-compensation linearization algorithm, the nonlinear matrix inequality for controller design is linearized, and the feedback gain matrix of the controller is solved iteratively. Finally, comparative simulation and experimental research are conducted to verify the effectiveness and superiority of the designed optimal controller.

Delay-Dependent Sliding Mode Variable Structure Control of Vehicle Magneto-Rheological Semi-Active Suspension

The vehicle semi-active suspension with Magneto-Rheological Damper (MRD) has been a hot research topic of this decade, featuring the challenging task of the robust control with actuator time delay considerations. In this study, a delay dependent sliding mode variable structure control, based on the Linear Matrix Inequality (LMI), is proposed to suppress the vibration of the Magneto-Rheological Semi-Active Suspension (MRSS) control system. In accordance with the nonlinear characteristics of MRD, a dynamic model of automotive semi-active suspension system, considering time delay, is established. By defining a parameter-dependent Lyapunov switching functional, the conditions for asymptotic stability of closed-loop time delay system are derived, while the sliding mode variable structure control with reduced conservatism is designed. According to the method of LMI, the asymptotic stability problem of sliding mode is transformed into a feasibility problem, which can be solved by the solver ‘feasp’ in LMI toolbox. In addition, the calculation of the critical time delay of MRSS is expressed as a generalized eigenvalue optimization problem. For comparison purposes, three representative controllers, including a conventional sliding mode controller, a delay dependent controller, and a smith compensation, are studied. Simulation and real vehicle testing on bump and random road responses show that, the designed delay dependent controller can ensure the stability of the suspension system, weaken the influence of time delay on the control performance and effectively improve the ride comfort of the vehicle.