A comparative analysis of model-based control methods applied for the active suspension system

Effective control of ride quality and handling performance are challenges for active vehicle suspension systems, particularly for off-road applications. Off-road vehicles experience large suspension displacements, where the nonlinear kinematics and damping characteristics of suspension elements are significant. These nonlinearities tend to degrade the performance of active suspension systems, introducing harshness to the ride quality and reducing off-road mobility. Typical control strategies rely on linear time-invariant models of the suspension dynamics. While these models are convenient, nominally accurate, and tractable due to the abundance of linear control techniques, they neglect the nonlinearities and time-varying dynamics present in real suspension systems. One approach to improving the effectiveness of active vehicle suspension systems, while preserving the benefits of linear control techniques, is to estimate and cancel the nonlinearities using feedback linearization. In this paper, the authors demonstrate an intelligent parameter estimation approach, using structured artificial neural networks, that continually "learns" the nonlinear parameter variations of a quarter-car suspension model. This estimation algorithm becomes the foundation for an intelligent feedback linearization (IFL) controller for active vehicle suspensions. Results are presented for real-time experimental tests and field evaluations on a military HMMWV. These results clearly demonstrate the viability and effectiveness of this approach as a tool for rapid, online development of vehicle suspension models and controllers.

Traditionally automotive suspension designs have been a compromise between the three conflicting criteria of road holding, load-carrying and passenger comfort. Active and semiactive suspension control methods have been considered as ways of increasing the freedom one has to specify independently the characteristics of load carrying, handling and ride quality. Consequently, these control methods have been enthusiastically investigated in the past decades. In this paper, active suspension control based on LMIs, including H∞ control and mixed H2/H∞ synthesis has been developed in this paper. The simulation results demonstrate that robust control method can suppress disturbance from road inputs, thus improving the ride comfort and maintaining the good road handling. The mixed H2/H∞ synthesis can provide both the robustness of H∞ control and the better performance of H2 (LQR) method.

During blueberry picking operations, changes in the blueberry harvester’s body posture (BHBP) caused by undulating farmland surfaces significantly affect the operational stability of the harvesting device. To mitigate these effects, an active suspension (AS) control system was developed. First, considering the coherence and time lag of the four-wheel tracks, the filtered white noise method was chosen to generate road excitations. Subsequently, the virtual prototype model of the blueberry harvester (BH) was built in ADAMS with the nonlinear properties of the passive suspension. A fuzzy proportion integral differential (PID) control algorithm and decoupled control strategy were employed to design the AS control system. Finally, ADAMS-MATLAB co-simulations and field tests were conducted. The results indicate that the measured farmland road profile conforms to standard D-level road excitations according to ISO8608, and the co-simulation model accurately predicts the BH’s dynamic response. Under AS control, the vertical acceleration, pitch acceleration, and roll acceleration of the BH decreased by 38.52%, 37.39%, and 34.29%, respectively, during simulations compared to the passive suspension. In field tests, these reductions were 36.51%, 33.84%, and 30.21%, respectively. The AS control system proposed in this study significantly improves the stability of the BH under farmland road excitation, effectively mitigating equipment impacts and wear caused by machine jolts while enhancing harvesting performance. This research addresses the gap in applying AS technology within the field of harvesting machinery, offering a novel technical approach for the development of vehicle control systems for harvesting machinery targeting blueberries and other shrub crops. It holds considerable theoretical value and practical significance.

In this paper, we present a backstepping based fuzzy logic (FL) scheme for the active control of vehicle suspension systems using the two-degrees of freedom or 1/4 car model. The full dynamics of a novel hydraulic strut are considered. The servo-valve dynamics are also included. A fuzzy logic system is used to estimate the nonlinear hydraulic strut dynamics. The backstepping fuzzy logic scheme is shown to give superior performance over passive suspension and active suspension control using conventional proportional-integral-derivative (PID) schemes. The FL system is adapted in such a way as to estimate online the unknown hydraulic dynamics and provide the backstepping loop with the desired servo-valve positioning so that the scheme becomes adaptive, guaranteeing bounded tracking errors and parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard adaptive backstepping techniques, no linear-in-the-parameters assumptions are needed.

Multi-objective control for active vehicle suspension with wheelbase preview

In this paper a fuzzy Sugeno rule based controller is used to manage a semi-active suspension of 1/4 vehicle model. Our semi-active suspensions have the characteristic to change the damping constant in order to reduce vertical oscillation of the vehicle, to restrain the shell vehicle acceleration and consequently to be more comfortable for passengers. On the other hand, if no control is present, the suspension works in passive mode, i.e. without damping parameters changing. The inputs of our fuzzy controller, three in total, are the dynamic wheel load described by five membership functions, its derivative and the vertical acceleration both described by three membership functions. By comparing the performances of the suspension with and without control, the Sugeno based model controller results in a more comfortable and safe system.

This chapter reviews computational-intelligence-involved approaches in active vehicle suspension control systems with a focus on the problems raised in practical implementations by their nonlinear and uncertain properties. After a brief introduction on active suspension models, the chapter explores state-of-the-art in fuzzy inference systems, neural networks, genetic algorithms, and their combination for suspension control issues. Discussion and comments are provided based on the reviewed simulation and experimental results. The chapter is concluded with remarks and future directions.

Active suspension can improve vehicle vibrations caused by road excitation. For trucks, the vehicle mass change is usually large, and changes in vehicle mass will affect the control performance of the active suspension. In order to solve the problem of active suspension control performance decreasing due to large changes in vehicle mass, this paper proposes an active suspension control method integrating online mass estimation. This control method is designed based on the mass estimation algorithm of the recursive least squares method with a forgetting factor (FFRLS) and the Linear Quadratic Regulator (LQR) algorithm. A set of feedback control matrices K is obtained according to different vehicle masses. Then, the mass estimation algorithm can estimate the actual vehicle mass in real-time during the vehicle acceleration process. According to the mass estimation value, a corresponding feedback control matrix K is selected from the control matrix set, and K is used as the actual control gain matrix of the current active suspension. With specific simulation cases, the vehicle vibration response is studied by the numerical simulation method. The results of the simulation process have shown that when the vehicle mass changes largely, the suspension dynamic deflection and tire dynamic deformation are significantly reduced while keeping a good vehicle body attitude control effect by using an active suspension controller integrated with online mass estimation. In the random road simulation, suspension dynamic deflection is reduced by 3.26%, and tire dynamic deformation is reduced by 5.91% compared with the original active suspension controller. In the road bump simulation, suspension dynamic deflection and tire dynamic deformation are also significantly reduced. As a consequence, the stability and comfort of the vehicle have been greatly enhanced.

This paper proposes the study of automobile active suspension system for the purpose of improving ride comfort to passengers and simultaneously improving the stability of vehicle by reducing vibration effects on suspension system. With the increased demand of fine control of parameters, the mechatronic controller is being used for increasing the comfort of vehicle with their application to the suspension system of the vehicle. This is the prime location for the consideration of inclusion of controller. The modeling and simulation is carried out in Simulink environment and the PID (Proportional Integral derivative) controller is introduced for improving the performance of the suspension system. The active control of the suspension system is being carried out by proper tuning of the controller. The dynamic performance of the integrated system is improved significantly by using the controller.

Active suspension have not been used widely due to its magnificent energy consuming. A kind of electromechanical energy-regenerative suspension is proposed in this paper. Suspension dynamic equations are modeled by MATLAB/Simulink. In order to get an optimal performance of suspension, several indices are discussed in this paper with three control curve on a motor MAP diagram. The results indicated that optimal control strategy depends on subjective object; As for energy recovery, performance contour is the best choice. If the objective is the balance of all indices, middle torque curve is the ideal choose. Also an active control suspension and energy-regenerative suspension are compared in this paper. It can be concluded that the active control suspension can obtain good dynamic effects but consume considerable energy.

In order to improve the vehicle ride comfort, the vehicle EHA (electro-hydrostatic actuator, EHA) active suspension structure has been proposed. The mathematical model of the 2 DOF active suspension and the mathematical model of the EHA active suspension system are established. The influence of the actuator (brushless DC motor) on the main force of the system is analyzed. A force tracking control strategy is designed, which includes the system outer ring LQG controller and the system inner loop motor speed & current controller. According to the influence of the brushless DC motor on the active suspension system, the simulation analysis of the suspension system under the control of the force tracking and the LQG control without considering the influence of the motor on the system is carried out. The results show that the force tracking control strategy can effectively track the active force of the system and improve the dynamic characteristics of the EHA active suspension.

This paper describes the design and control of the first generation active suspension for NASA's Chariot rover and Lunar Electric Rover (LER). Within the paper is a general overview of the needs and benefits of active suspensions for crew mobility systems on the lunar surface. In the spectrum of active suspensions, the Chariot system falls into the category of a series active or low bandwidth suspension. The passive suspension elements absorb the high frequency content of driving over rugged terrain and the active element sets the height of the suspension allowing the vehicle to conform to the terrain. This suspension system is capable of raising and lowering the vehicle, adjusting roll and pitch attitude for docking operations, leveling the chassis against gravity, and balancing the force across the six wheels during low speed operations. In addition to the existing system, initial results of an incremental design upgrade are discussed and future considerations for suspension systems for the lunar surface are described.

In this paper, the adaptive control problem is addressed for active vehicle suspension systems with the unknown nonlinearity as well as the unknown time-varying input delay. A dynamic gain control frame is proposed to suppress vehicle vibration and maintain the required suspension performance. Firstly, a dynamic gain extended state observer (ESO) is constructed to obtain the unknown velocity information and nonlinearity for the implementation of the control scheme. Moreover, a robust control method with a dynamic gain control term and a time delay compensation term is designed to mitigate the effect of unknown input time delay and uncertainty on system stability. By resorting to the Lyapunov-Krasovkii stability theory, the ultimate uniform boundedness of all signals in the closed-loop vehicle active suspension system is guaranteed. Finally, an example is presented as an illustration of the effectiveness of the proposed method.

Delay-resilient robust control of automobile active suspensions via deep reinforcement learning

Hierarchical [formula omitted] fuzzy-neural control of anti-lock braking system and active suspension in a vehicle