Active Suspension Control Research Articles

Multidirectional motion coupling based extreme motion control of distributed drive autonomous vehicle

To improve the multidirectional motion control accuracy and driving stability of Distributed Drive Autonomous Vehicles (DDAVs) under extreme conditions, the extreme speed estimation method based on dynamic boundary and the multidirectional motion coupling control law design method based on multi degrees of freedom vehicle dynamic model are proposed. The extreme speed estimation method identifies the stable state of DDAVs by the dynamic boundary composed of yaw rate, sideslip angle and roll angle, and then estimates the extreme speed of the vehicle. The design method of multidirectional motion coupling control law adopts eight-degrees-of-freedom (8-DOF) vehicle dynamic model to design path tracking control law, speed tracking control law, yaw stability control law, and active suspension control law at the same time, so as to realize multidirectional motion coupling control. Based on the above method, the Multidirectional Motion Coupling Control System (MMCCS) of DDAVs is designed. The effectiveness of the proposed method is verified by double-line-shifting and serpentine driving simulations under different road adhesion conditions. The superiority of the method is proved by comparing the existing integrated control method.

Active Suspension Control of Quarter-Car System With Experimental Validation

A reliable, efficient, and simple control is presented and validated for a quarter-car active suspension system equipped with an electro-hydraulic actuator. Unlike the existing techniques, this control does not use any function approximation, e.g., neural networks (NNs) or fuzzy-logic systems (FLSs), while the unmolded dynamics, including the hydraulic actuator behavior, can be accommodated effectively. Hence, the heavy computational costs and tedious parameter tuning phase can be remedied. Moreover, both the transient and steady-state suspension performance can be retained by incorporating prescribed performance functions (PPFs) into the control implementation. This guaranteed performance is particularly useful for guaranteeing the safe operation of suspension systems. Apart from theoretical studies, some practical considerations of control implementation and several parameter tuning guidelines are suggested. Experimental results based on a practical quarter-car active suspension test-rig demonstrate that this control can obtain a superior performance and has better computational efficiency over several other control methods.

Secondary yaw control to improve curving vs. stability trade-off for a railway vehicle

Active primary/secondary suspensions have been proposed to solve the trade-off between curving and stability in railway vehicles. One concept is known as secondary yaw control (SYC), which consists of applying a controllable yaw torque between the carbody and the two bogies. This has been studied in the past mainly to enhance the vehicle's curving ability, but this paper extends the idea by examining the implications of designing a bogie with soft yaw stiffness between the bogie frame and the wheelsets and using SYC to provide active stabilisation. To this aim, a state feedback control law is designed according to the LQR technique. The paper presents the general concept of the active suspension control to be investigated and the specific control strategies applied. Then the effectiveness of the proposed actuation concept is investigated by means of numerical simulations performed on mathematical models of the passive and actively controlled vehicles implemented in a fully nonlinear multi-body simulator.

Anti-Saturation-Based Adaptive Sliding-Mode Control for Active Suspension Systems With Time-Varying Vertical Displacement and Speed Constraints

In this article, an adaptive sliding-mode control scheme is developed for a class of uncertain quarter vehicle active suspension systems with time-varying vertical displacement and speed constraints, in which the input saturation is considered. The integral terminal SMC is adopted to improve convergence accuracy and avoid singular problems. In addition, neural networks are used to model unknown terms in the system and the backstepping technique is taken into account to design the actual controller. To guarantee that the time-varying state constraints are not violated, the corresponding Barrier Lyapunov functions are constructed. At the same time, a continuous differentiable asymmetric saturation model is developed to improve the stability of the system. Then, the Lyapunov stability theory is used to verify that all signals of the resulting system are semi globally uniformly ultimately bounded, time-varying state constraints are not violated, and error variables can converge to the small neighborhood of 0. Finally, results of the simulation of the designed control strategy are given to further prove the effectiveness.

Modeling, analysis and PID controller implementation on suspension system for quarter vehicle model

The aim of this research is to acquire a mathematical model for the quarter vehicle model's passive and active suspension system and to build an active and passive suspension control for a quarter-vehicle model. The passive design of the suspension scheme is a compromise between vehicle handling capacity and passenger ride comfort. Passive suspension provides one of these two circumstances that conflict. Current car suspension systems use passive components only by using a coefficient of spring and damping with a fixed coefficient and two degrees of freedom. In the design and manufacture of cars, passenger comfort is a very significant parameter. The objective of the active suspension system is to enhance both the comfort of the ride and the handling of the highway by directly regulating the actuators of suspension power. In the active suspension system, the Proportional Integral Differential Controller (PID) method is applied. Various kinds of highway profiles and controllers such as P, PD, PI, and PID controllers evaluate the efficiency of the active suspension system comparing it with passive suspension scheme. This efficiency will be determined by using the MATLAB and SIMULINK to perform computer simulations.

A reinforcement learning backstepping‐based control design for a full vehicle active Macpherson suspension system

This paper presents a reinforcement learning backstepping-based control scheme for the design of a full vehicle active suspension system such that both the superior ride comfort and stabilization are achieved. It is well known that the traditional backstepping control scheme is suitable to solve higher order nonlinear system based on the strict-feedback form; however, the disadvantage of the procedure requires computing the derivatives of virtual control signals, which is a very complex task. Therefore, a reinforcement learning scheme using the deep deterministic policy gradient (DDPG) control strategy is developed to replace the process of finding virtual control force in backstepping method. It not only avoids the complexity of analytically calculating the derivatives of the virtual control signals, but also retains the systems robustness when there exist random disturbances from road irregularities. To verify the performance of the proposed active suspension control scheme, the random road unevenness profiles according to ISO 8608 is considered as vertical and lateral disturbance input excitation of a full-vehicle suspension system. Compared with conventional passive suspension system and backstepping control scheme, the proposed approach can be demonstrated to not only effectively improve ride comfort under random road excitation, but also improve the transient response and robustness.

Interval fuzzy robust non-fragile finite frequency control for active suspension of in-wheel motor driven electric vehicles with time delay

This paper proposes a fuzzy non-fragile finite frequency H∞ control algorithm for the active suspension system (ASS) of the electric vehicles driven by in-wheel motors with an advanced dynamic vibration absorber (DVA). Firstly, an interval type-2 Takagi-Sugeno (T-S) fuzzy model is established to formulate the nonlinear time-delay ASS with the uncertainties of sprung mass, unsprung mass, suspension stiffness, and tire stiffness. Secondly, a differential evolution (DE) algorithm is adopted to optimize the parameters of vehicle suspension and DVA. Thirdly, a non-fragile finite frequency H∞ control controller is developed under the consideration of controller perturbation and input delay to improve the comprehensive performance of the chassis under the finite frequency external disturbances. Finally, simulation tests are carried out to verify the effectiveness of the proposed controller.

Path tracking and stability integrated control of intelligent vehicles under steering collision avoidance

In order to improve the path tracking performance and stability of intelligent vehicles during steering and collision avoidance working condition, an integrated control method combining four-wheel steering, active suspension and direct yaw-moment is proposed. Firstly, a unified chassis dynamics model with 18 degrees-of-freedom (DOF) is established, which includes steering, braking and suspension subsystems. Phase plane analysis results for both traditional 2-DOF bicycle model and the 18-DOF model are compared. The results show that although the uneven road has a certain influence on the phase portraits of sideslip angle and sideslip angular velocity of the 18-DOF model, the overall trend is consistent with those of the 2-DOF model. Secondly, a general control framework for steering and collision avoidance of autonomous vehicle is put forward, consisting of the steering control for path tracking, and active suspension and direct yaw-moment control for stability. The collision avoidance reference path is planned by the fifth-degree polynomial, and three types of steering control methods including optimal control front wheel steering, optimal control four-wheel steering and front wheel steering combined with feedforward and feedback control, are adopted for comparison. The main contribution of this paper is that the role of active suspension is considered in the path tracking and stability integrated control of intelligent vehicles under steering collision avoidance. The active suspension subsystem uses optimal control method by two different kinds of weight coefficients, which can be switched between “comfort mode” and “sport mode” based on the vehicle lateral acceleration. According to the phase portraits of vehicle center of gravity sideslip angle and sideslip angular velocity, the direct-yaw moment control system adopts proportional differential control method based on the distance from the state point to the stability boundary. Finally, simulations and comparative analysis are compared among four different control systems on high and low adhesion roads. The research results indicate that the integrated control system can improve the vehicle path tracking performance and stability significantly.

Adaptive robust finite-time control for active suspension systems via disturbance observation

To improve the ride comfort of vehicles, active suspension control strategy has received widespread attention. Although based on the finite-time and sliding mode control (SMC) technology design scheme, the convergence problem has been solved, the controller chattering and the poor control effect in the face of random interference will definitely limit its application in practice. To address such limitation, considering the variety of driving conditions, based on finite-time and SMC technology, an Adaptive Finite-time High-order SMC Active Suspension control Systems via disturbance observation (AFHASS) is proposed. Firstly, to improve the ride comfort of vehicle, a quarter dynamic model is established for vehicle active suspension; Then, given the advantages of finite-time and SMC technology, an AFHASS method is designed, and based on the Lyapunov analysis function constructed, the stability of the presented AFHASS control strategy has been proved; Finally, the effectiveness of the proposed AFHASS algorithm is validated by performing simulation, and compared with the different methods. Taking the finite-time control algorithm as a benchmark, under the C grade road roughness, the root mean square of vertical displacement has been improved by 29% and the energy consumption has been improved by 8%. The results are presented and discussed to illustrate the advantage and effectiveness of the proposed AFHASS algorithm in terms of energy consumption, vertical performance and controller chattering.

Bio-inspired structure reference model oriented robust full vehicle active suspension system control via constraint-following

It is hard to realize the bio-inspired structure (BIS) directly in the vehicle suspension due to space limitation, even though the beneficial nonlinearity of BIS for anti-vibration has been extensively proved. This paper adopts active suspension control to take the advantage of BIS on anti-vibration to improve vehicle comfort, i.e., formulating the ideal BIS dynamics model as nonholonomic servo constraints and then designing the control via constraint-following to drive the dynamics of full vehicle active suspension system (FVASS) to follow the servo constraints. It is of novelty for this study to introduce the constraint-following controls (CFC) into the linear FVASS to follow the servo constraints, considering possibly fast time-varying uncertainties including large mismatched portions. The CFC views the nonlinear control problem from a new perspective of servo constraint. This leads to a simple and natural control that is capable of ‘exactly’ following the servo constraints of nonlinear BIS in the second-order form without the requirement of linearization and/or nonlinear cancellation. Furthermore, a robust CFC is designed based on an uncertainty decomposition technique to consider possibly fast time-varying uncertainties including large mismatched portions, which are unavoidable in underactuated systems but are usually ignored or assumed very small in existing studies. The proposed robust CFC is proved to be able to fulfill the control task with controllable constraint-following error by the Lyapunov stability analysis, even in the presence of uncertainties. Experimental and simulation results reveal the validity of the proposed approach in improving vehicle suspension performance.

Active suspension control with consensus strategy for dynamic posture tracking of wheel-legged robotic systems on uneven surfaces

This work presents a dynamic posture tracking control strategy for wheel-legged systems on uneven surfaces. Based on the kinematic model of a wheel-legged robotic system, the expected positions for the end-effectors of wheel-legs are calculated according to posture references and sensor feedback. The position control problem for a general wheel-leg is investigated for the active mechanism to imitate a passive suspension and respond to the external contact forces. The position tracking accuracy of the wheel-leg is sacrificed to enhance the compliance performance under rough terrain. Because of the unique contact state with the uneven ground for each wheel-leg, the position responses are different. As a result, the forces from the wheel-legs to the fuselage are inconsistent, which leads to the risk of posture oscillations. Equipping the wheel-legs with an undirected communication network, a consensus scheme for the robotic system is developed with proven global asymptotic stability to improve the posture tracking property. A novel robotic system is established with Stewart-structured wheel-legs, which are connected by a user datagram protocol network. Comparative experimental results are carried out on the physical prototype to validate the effectiveness of the proposed approach.

Simulation-Based Testing of Subsystems for Autonomous Vehicles at the Example of an Active Suspension Control System

Automated driving functions are expected to increase both the safety and ride comfort of future vehicles. Ensuring their functional safety and optimizing their performance requires thorough testing. Costs and duration of tests can be reduced if more tests can be performed numerically in a feasible simulation framework. This simulation setup must include all subsystems of the autonomous vehicle, which significantly interact with the system under test. In this paper, a modular model chain is presented, which is developed for testing systems with an impact on vehicle motion. It includes trajectory planning, motion control, and a model of the vehicle dynamics in a closed loop. Each subsystem can easily be exchanged to adapt the model chain with respect to the simulation objectives. As a use case, the testing of an active suspension control system is discussed. It is designed directly for use in autonomous cars and uses inputs from the vehicle motion planning subsystem for planning the suspension actuator motion. Using the presented closed-loop model chain, the effect of different actuator control strategies on ride comfort is compared, such as curve tilting. Furthermore, the impact of the active suspension system on lateral vehicle motion is shown.

Integrated Control of Active Front Wheel Steering and Active Suspension Based on Differential Flatness and Nonlinear Disturbance Observer

Mechanical elastic wheel (MEW) has the advantage of explosion-proof, which helps to guarantee the safety and maneuverability of the automobile. Aiming to improve the stability of vehicles matching with MEW, this paper proposes an integrated control strategy that combines the active front steering and the active suspension systems. The integrated controller consists of feed-forward and feedback controllers. Firstly, the differential flatness system of the vehicle is constructed concerning the coupling among yaw, lateral, and roll motion. And the flatness based feed-forward control inputs of the desired front wheel angle and active roll moment are derived. At the same time, the backstepping-adaptive feedback controllers for yaw rate and roll angle are designed. Then, the feed-forward and feedback controllers are coordinated and their weight coefficients are adjusted according to the sideslip angle. Furthermore, this paper also adopts a nonlinear disturbance observer to estimate the vertical load of MEW. The influence of the vertical load is fully considered when estimating the equivalent cornering stiffness of each MEW, so as to realize the online adjustment of the controller parameters. Finally, the maneuver tests of step input steering and double lane change are simulated, respectively. The results show that the proposed control strategy yields a better control effect than the fuzzy-PID controller, and the performance of the vehicle matching with MEW has been significantly improved.

Self-learning control of model uncertain active suspension systems with observer–critic structure

This paper presents a self-learning control algorithm for model uncertain suspension systems using single network adaptive critic (SNAC) approach. First, a differential neural network (DNN) observer in conjunction with the weight updating law is established to observe the uncertain dynamic. Then, the nominal optimal value function is approximated by a critic NN whose weight is updated by a novel design learning law driven by the filtered parameter error. The online self-learning control policy is thus derived by approximately solving the Hamilton–Jacobi–Bellman (HJB) equation based on SNAC technique. The Lyapunov approach is synthesized to ensure the convergent characteristics of the entire closed-loop system composed of the DNN observer and the self-learning control policy. Computer simulation of a quarter car suspension system is established to verify the effectiveness of the proposed approach. Simulation results illustrated that the designed method can ensure the good performance in terms with the road hold and ride quality. In addition, independent of model and online self-learning characteristics make it possible to design a high-performance vehicle active suspension controller.

Simulation-Based Development for Active Suspension Control for Automated Driving Vehicles—Evaluation of Transferability to Real-World Testing

Simulation-Based Development for Active Suspension Control for Automated Driving Vehicles—Evaluation of Transferability to Real-World Testing

Adaptive sliding mode-based active disturbance rejection control for vehicle suspension control

This article presents an adaptive sliding mode-based active disturbance rejection control strategy for the quarter car active suspension control. The suspension system is problematic for its nonlinearities from the dampers, springs, and irregular excitation from the road surface. This study considers this complex behavior and nonlinear characteristics of the suspension system and uses an adaptive sliding mode control law and an extended state observer to estimate and eliminate them from the controlled system. The central concept is to combine the advantages of adaptive sliding mode control to accurately track the reference trajectory with the ability of active disturbance rejection control to reject the parameter uncertainties and external disturbances. The proposed control scheme is verified in simulation studies with parameter uncertainties, nonlinearities, and external disturbances. The simulation results show that the proposed control scheme can significantly improve vehicles’ ride comfort and road-handling ability, owing to the extended state observer’s estimation capability. A comparison of the proposed strategy is also presented with a linear active disturbance rejection control and a conventional proportional–integral–derivative controller. Simulation results show that adaptive sliding mode-based active disturbance rejection improves the robustness against modeling errors, nonlinearities, and disturbances.

Active suspension control strategy of heavy rescue vehicle based on multi-sensor information fusion

This study proposes the active suspension control strategy of heavy rescue vehicles based on multi-sensor information fusion to address the problem of poor ride comfort and stability of heavy rescue vehicles on unstructured pavement. Firstly, an attitude fusion algorithm with acceleration compensation is designed. According to the driving speed, the appropriate cut-off frequency of acceleration filter is selected, and the extended Kalman filter is designed to complete the optimal estimation of position and attitude information. Secondly, the vehicle nine degree of freedom model is established, and the inverse matrix of attitude control is solved to obtain the suspension actuator output. Lastly, an active suspension control strategy based on multi-sensor information fusion is designed. The body attitude information after the information fusion is used for the control of active suspension system to realize the real-time adjustment of body attitude. Test results show that the root mean square values of vertical displacement, pitch angle, and roll angle under active suspension are reduced by 37.01%, 26.96%, and 38.90%, respectively, compared with those under in passive suspension. This reduction improves the ride comfort and stability of the heavy rescue vehicles on unstructured pavement.

Feasibility and performance analyses for an active geometry control suspension system for over-actuated vehicles

Feasibility and performance analyses for an active geometry control suspension system for over-actuated vehicles

Adaptive ride height controller design for vehicle active suspension systems with uncertain sprung mass and time‐varying disturbance

AbstractIn this article, we address the problem of ride height tracking control of the automotive active air suspension (AAS) system with uncertain sprung mass and external time‐varying disturbances. The proposed adaptive robust control approach guarantees that (i) the ride height converges towards, and stays within, an arbitrarily small neighborhood of the desired height, attaining uniform ultimate boundedness; (ii) the closed‐loop AAS system is (1) adaptive to uncertain sprung mass caused by the change of payload through the design of a projector‐based mass estimator; and, (2) robust to external time‐varying disturbance by developing a nonlinear disturbance observer. At last, co‐simulation (AMESim‐Matlab/Simulink) and experimental tests on a self‐fabricated quarter‐car test rig are conducted to demonstrate the effectiveness and performance of the proposed method.

Double-channel event-triggered adaptive optimal control of active suspension systems

Double-channel event-triggered adaptive optimal control of active suspension systems