Vehicle Active Suspension Research Articles

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.

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.

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.

Stability Analysis and Controller Design of Networked Active Vehicle Suspension System Subject to Delays and Packet Losses

In this paper, the stability analysis and control design of networked control systems (NCS) subject to network induced delay and packet dropouts, the study is aiming to maximize the allowable upper bound of network induced delay together with the number of consecutive packet dropouts, The analysis method utilizes a new Lyapunov-Krasovskii (LKF) functional to provide less conservative results. The delay-dependent stability criteria based on improved Wirtinger inequality and reciprocally convex combination inequality are derived in terms of linear matrix inequalities (LMI). Finally, numerical examples are given to show the effectiveness of the proposed analysis method compared to other existing methods.

Modeling and Analysis of Active Full Vehicle Suspension Model Optimized Using the Advanced Fuzzy Logic Controller

The suspension system plays a major role in automobiles to improve passenger comfort, passenger safety and road handling. It isolates the body of a vehicle from road disturbances. The full vehicle would be subjected to disturbances from all four wheels or a full suspension model of the vehicle and, thus, a full-suspension model of the vehicle should be added to the idea of an enhanced control preview. The input data for the fuzzy logic controller (FLC) is the velocity and acceleration of the front and rear wheels. Controller outputs are considered to be active forces that improve driver comfort, safety and road handling characteristics. The objective of this work is to model and analyse an active full vehicle suspension. The model is optimized using advanced FLC to improve driver comfort, safety and road handling. The mathematical model for the active full vehicle suspension model has been derived. The necessary background for the Simulink fuzzy logic and FLC has been presented. All the simulations are carried out using MATLAB/SIMULINK, a high-performance numeric computation and visualization software package. The fuzzy logic-controlled active values have been compared with ordinary passive simulated values for road profiles. The result of the simulation show that the designed advanced FLC has improved ride comfort by effectively reducing the vehicle body displacement. There is also an appreciable reduction in velocity and acceleration with no increase in suspension travel.

Adaptive Fuzzy PID Control Strategy for Vehicle Active Suspension Based on Road Evaluation

The paper proposes a fuzzy proportion integral differential (PID) control strategy based on road estimation to meet the different performance requirements of active suspension for comfort and safety under different road conditions in view of the uncertainty of vehicle suspension parameters and random road perturbations. First, flexible neural tree (FNT) is used to estimate the vertical disturbance of road within uncertain vehicle parameters in which the Fourier transform of the autocorrelation function is used to fit the road power spectrum density (PSD). In order to compensate for the delay and realize the real-time estimate of road conditions, an online fuzzy evaluation strategy for road condition is developed by integrating the fitted road PSD and the real-time suspension performance. After that, an adaptive fuzzy PID control strategy is developed based on the adaptive suspension control performance, which is supposed to realize the adaptive adjustment of control parameters for different road conditions. The simulation experimental results show that the online fuzzy evaluation strategy can effectively reflect the change in road condition, and the proposed adaptive fuzzy PID control strategy can work for adjusting the parameters adaptively, according to the changes in road conditions and therefore meet the control performance requirements under different road conditions.

Active vehicle suspension control using road preview model predictive control and radial basis function networks

Active suspension systems in road vehicles are applied in order to mitigate the road-induced chassis vertical accelerations more effectively than standard passive suspensions, thus increasing comfort and handling. Such systems are greatly assisted by road preview schemes, consisting of special sensors usually based on laser scanners (e.g. LiDAR sensors), which detect road irregularities ahead of the vehicle and feed this information to a control system, designed to manipulate the active suspension accordingly. In this paper, a model predictive controller (MPC) with road preview incorporating radial basis function (RBF) models, is presented as a control scheme for a full car active suspension system. The employed RBF models can efficiently approximate the nonlinear behavior of the suspension system, thus improving performance over linear MPC methods. Special care is taken to alleviate the increased computational complexity entailed in the RBF models, in order to ensure that online implementation of the controller is feasible. The proposed scheme is evaluated on a detailed simulated full car model under various road excitation types, while making use of a realistic approach for incorporating LiDAR road scanner noise. Comparisons to a passive suspension system, as well as a standard MPC controller with a fully linear plant model, demonstrate the performance potential of using RBF prediction models in a road preview MPC context.

Modeling and Simulation of PID Controller-Based Active Suspension System for A Quarter Car Model

The most common automotive suspension systems have been used passive components with fixed damping coefficient and spring constant. They are two separate functions to reach a compromise between drive quality and handling performance. Because of the limit of structures, the passive suspension system could hardly enhance the two features at the same time. In recent years, the active suspension system has been widely used in a vehicle which can overcome the limitations of the passive suspension systems. Since there are many advantages to make the vehicle operate safer and quieter than the passive suspension system. The aim of this study is to develop a linear mathematical model of active and passive suspensions systems for quarter car models subjected to different road profiles using a Proportional Derivative Integral (PID) controller. After modeling and simulation of the suspension system using Matlab/Simulink, this study has been able to determine the parameter of PID controllers of active suspension in many different road surfaces and vehicle speeds to improve driving comfort. Another new point of this study is the Graphical User Interfaces to be a successful design. The users can enter the input parameters for Matlab processing and get the results, which can make system analysis faster and more convenient. The main outcomes of this paper are performed to investigate and compare the response between active and passive suspension systems. Through the analysis of the simulation results, this research has demonstrated the feasibility, reliability, and they can be applied to propose a further improvement of the kinematic and dynamic characteristics of the vehicle active suspension system.

CAN-Based Vibration Control for Networked Vehicle Active Suspension with Both Network-Induced Delays and Packet-Dropouts

Ride comfort and driving safety are highly vulnerable to the undesirable excessive vibrations caused by road surface irregularities and the imperfect in-vehicle network (IVN). The main contribution of this paper consists of proposing a near-optimal vibration control approach for networked vehicle active suspension under irregular road excitations in a discrete-time domain, in which the uncertain time delay and packet dropout in CAN are taken into consideration. More specially, by virtue of two buffers of the sensor-to-controller network channel and the controller-to-actuator network channel in CAN, by introducing a designed state-transformation-based method, the original vibration control problem under the constraints of the irregular road excitations and imperfect CAN is transformed into a two-point boundary value (TPBV) problem without advanced and delayed items. After that, the near-optimal vibration control approach is presented to isolate the vehicle body from the road excitations and compensate the time delay and packet dropout from CAN synchronously. The stability condition of the networked vehicle active suspension under the proposed vibration controller is obtained based on the Lyapunov function. In numerous scenarios with different road roughnesses and network-induced time delays and packet dropouts, the simulation results illustrate the effectiveness and superiority of the proposed near-optimal vibration controller.

Experimental Validation of LQR Weight Optimization Using Bat Algorithm Applied to Vibration Control of Vehicle Suspension System

To deal with multiple constraints of vehicle active suspension system (ASS) including road handling and passenger safety, this paper presents an optimal linear quadratic regulator (LQR) approach which employs bat algorithm (BA) for selection of optimal state and input penalty matrices of LQR. We formulate the conflicting control objectives of ASS, namely, ride comfort and passenger safety as a multi-constraint optimization problem and employ the BA for weight selection of LQR. The key advantage of the proposed approach is that the local optima problem is avoided by utilizing the frequency tuning and random walk technique in BA. The performance of the proposed approach is experimentally tested using hardware in loop (HIL) testing on a quarter car ASS for realistic road profiles. Moreover, the performance is benchmarked against grey wolf optimization tuned LQR. Experimental results assessed based on ISO 2631 standards highlight the significant improvement in the ride comfort and passenger safety.

Research on Vibration Reduction of Half-Vehicle Active Suspension Sys-tem Based on Time-delayed Feedback Control with Wheel Displacement

This paper proposes parameter controlling optimization of the time-delay feedback that is based on "equivalent harmonic excitation" in effective frequency band towards optimization of delay feedback control parameters for vehicle semi active suspension system. In this way, the optimal values of delay feedback control parameters based on different types of delay feedback control (displacement, velocity and acceleration) of wheels in the effective frequency band are obtained. Finally, through the stability analysis of vehicle semi-active suspension system based on different types of wheel delay feedback control and the numerical simulation analysis of time-domain response of suspension system performance evaluation index, the superiority of the time-delay feedback control strategy based on wheel displacement and the effectiveness and feasibility of the equivalent harmonic excitation optimization strategy are verified. Therefore, it provides a theoretical reference for the selection of time-delay feedback control strategy and the optimal design of time-delay feedback control parameters of vehicle active and semi-active suspension system.

A Design Synthesis Method for Robust Controllers of Active Vehicle Suspensions

This paper presents a design synthesis method for robust controllers of active vehicle suspensions (AVSs). Various control techniques have been applied to the design of AVSs for enhancing ride comfort and handling performance of ground vehicles. However, most of these model-based controller designs show poor robustness when the vehicle models are not accurate and operating conditions vary. To address the poor robustness problem of AVSs, a new controller is designed using the H∞ loop-shaping control technique. The controller targets robustness issues on vehicle models with parametric uncertainties and unmodelled dynamics. To facilitate the robust controller design, a design synthesis method is proposed: the H∞ loop-shaping controller design is formulated as a multi-objective optimization problem, the weighting functions’ parameters of the controller are treated as design variables, the expensive computing loads are handled by a parallel computing technique, and the solution of the optimization problem is the desired robust AVS controller. Simulation results demonstrate the benefits of the proposed AVS design.

Fuzzy logic and proportional integral derivative based multi-objective optimization of active suspension system of a 4×4 in-wheel motor driven electrical vehicle

This paper considers fuzzy logic and proportional integral derivative based multi-objective optimization of a non-linear active suspension system of a 4 × 4 in-wheel motor-driven electric vehicle by using the non-dominated sorting genetic algorithm II. The active suspension system of the electric vehicle and its controllers are optimized to achieve International Organization for Standardization2631-1 ride comfort and health criteria, also providing the actual working conditions such as roll angle and tire load transfers simultaneously for driving safety. In this regard, a non-linear full electrical vehicle model with quadratic tire and cubic suspension stiffnesses with 11 degrees of freedom and a seat-driver model with 5 degrees of freedom are implemented and optimized regarding seven objective functions determined from the root mean square of head and seat accelerations, crest factor , vibration dose value , the ratio of head and seat accelerations, the ratio of the upper torso and seat acceleration, root mean square upper torso acceleration, and root mean square of suspension, tire, and in-wheel motor displacements. The design variables of the optimization problem are chosen as the stiffnesses and damping coefficients of the suspension, in-wheel motors, and seat, as well as the parameters of the proportional derivative and fuzzy logic controllers. The obtained results demonstrate that significant improvements can be achieved by using a controller over the passive systems. It is also noted that the fuzzy logic controller improves ride comfort and the health criterion over proportional derivative system up to 13%, while the load transfer ratio index showed no adverse change between models concerning the rollover condition. The outcomes of this work clearly state that significant improvements, in terms of vibration exposure, can be achieved with the help of reducing the vibration amplitude of an in-wheel motor-driven electrical vehicle active suspension system by using multi-objective optimization considering a non-linear full vehicle model and realistic working conditions such as tire load transfer and vehicle body roll during cornering circumstances. Thus, obtained results are of utmost importance for manufacturers about the active suspension design process providing both a safe and comfortable driving of in-wheel driven electric vehicles.

Vibration Mitigation of Commercial Vehicle Active Tandem Axle Suspension System

Vibration Mitigation of Commercial Vehicle Active Tandem Axle Suspension System

Symmetry Control of Comfortable Vehicle Suspension Based on H∞

Suspension is an important part of intelligent and safe transportation; it is the balance point between the comfort and handling stability of a vehicle under intelligent traffic conditions. In this study, a control method of left-right symmetry of air suspension based on H∞ theory was proposed, which was verified under intelligent traffic conditions. First, the control stability caused by the active suspension control system running on uneven roads needs to be ensured. To address this issue, a 1/4 vehicle active suspension model was established, and the vertical acceleration of the vehicle body was applied as the main index of ride comfort. H∞ performance constraint output indicators of the controller contained the tire dynamic load, suspension dynamic stroke, and actuator control force limit. Based on the Lyapunov stability theory, an output feedback control law with H∞-guaranteed performance was proposed to constrain multiple targets. This way, the control problem was transformed into a solution to the Riccati equation. The simulation results showed that when dealing with general road disturbances, the proposed control strategy can reduce the vehicle body acceleration by about 20% and meet the requirements of an ultimate suspension dynamic deflection of 0.08 m and a dynamic tire load of 1500 N. Using this symmetrical control method can significantly improve the ride comfort and driving stability of a vehicle under intelligent traffic conditions.

Ant Colony Optimization Tuned Closed-Loop Optimal Control Intended for Vehicle Active Suspension System

In recent years, the suspension system in modern vehicles has played a key role both as far as driving safety and comfort is concerned. To satisfy these vehicle performance specifications, active suspension is currently studied and implemented in practice in recent decades. In contrast to passive suspensions, by introducing force into system, active suspension can alter the suspension dynamic in real-time. A design of a controller is needed for real-time tuning of the control force in an active suspension system (ASS) to fulfill the challenging control objectives of suspension system comprising road handling, ride convenience, and travel suspension. This research proposed a novel ant colony optimization (ACO) algorithm for solving multi-objective weight optimization problem of the linear quadratic regulator (LQR) for automobiles ASS. The optimization problem of ASS is to design a state-feedback controller (SFC) as a result ACO is used to find optimal LQR weights. Here both Q and R weight matrix of LQR is tuned. On a quarter-car ASS (QCASS) system, the effectiveness of ACO-tuned LQR is analytically checked with hardware in loop (HIL) analysis for an irregular road surface. Here, for experiment, ISO road D rough runway, bumpy path, and pulse-type road profile are taken into account. Experimental findings illustrate that the proposed procedure can substantially reduce the acceleration of the Car body due to irregular road profiles compared to classical tuned LQR and model predictive control (MPC). The proposed controller shows the profound impact on the efficiency of the control schemes for three different road profiles.

Adaptive Integral Terminal Sliding Mode Control for the Nonlinear Active Vehicle Suspension System under External Disturbances and Uncertainties

Suspension system is one of the most effective vehicle components that play an essential role in the stability and comfort of the vehicle. The passive suspension can not fully meet a car's stability and comfort requirements. Instead, an active suspension system has been proposed to improve these challenges. Active suspension minimizes the vibrations entering the body using a closed-loop control system. To this end, in this research, an integral terminal sliding mode control (integral TSMC) for an active nonlinear car suspension system under external disturbances and uncertainties is designed. First, the integral TSMC is designed to deal with the uncertainties and the external disturbances in the system when the upper bound is known. Next, an adaptation law is recommended to estimate the upper bound of uncertainties and external disturbances. The results show that the proposed integral TSMC improves the convergence rate and tracking error of the closed-loop system. The stability of the nonlinear control system is investigated and proven using Lyapunov's stability theory. The numerical results indicate a good robust performance and stability for the proposed controller for the nonlinear suspension system with different road profiles in the presence of uncertainties and external disturbances. From the results, it can also be understood that important measures such as ride comfort, road holding, and mechanical structural limitations are met using the proposed approach.

Improved LQG vibration control for multiple inputs of electric vehicle

To improve the ride comfort of electric vehicle, the vertical dynamic model of electric vehicle is established under the excitation of road surface and wheel hub motor. The road excitation is divided into random continuous road and convex road, and the influence of motor electromagnetic excitation on vibration is analyzed. To avoid the empirical value influence of each index weight coefficient on the main vibration control, the weight coefficient is determined by analytic hierarchy process (AHP), the LQG controller of vehicle active suspension is designed by using optimal control theory, and the comprehensive optimization of performance index is realized. The effectiveness of control effect is verified by comparing active and passive control based on single and double excitation.

Multi-Objective Constrained Optimizations Based Fuzzy Fault Tolerance Algorithm of Quarter Vehicle Suspensions

In this paper, a novel fuzzy fault tolerance algorithm is proposed for quarter vehicle active suspensions based on multi-objective constrained optimizations, which improves the riding comfort and driving safety. The objective function approach the optimal solutions via integrating multi-objective constraints as cost functions. By introducing the barrier function, the nonlinear active suspensions with multi-objective constraints are transformed into pure feedback systems without constraints. The problem is rather complicated yet challenging if the actuator faults are taken into account in quarter vehicle. The actuator faults are solved by the utilization of proportional actuation method. The unknown smooth dynamics based on physical truth are identified by exploiting the fuzzy logic knowledge. Meanwhile, the signals received in the suspensions do not violate the constraint boundary. Finally, the simulation results show that the algorithm is effective.

Proposal of Data-Based Preview Controller for Active Vehicle Suspension

Proposal of Data-Based Preview Controller for Active Vehicle Suspension