Car Active Suspension Research Articles

A new adaptive fuzzy logic control for nonlinear car active suspension systems based on the time-delay

A new adaptive fuzzy logic control for nonlinear car active suspension systems based on the time-delay

Simulation analysis of quarter car active suspension control based on QBP-PID

Simulation analysis of quarter car active suspension control based on QBP-PID

Terminal Synergetic Controller for Car's Active Suspension System Using Dragonfly Algorithm

Terminal Synergetic Controller for Car's Active Suspension System Using Dragonfly Algorithm

Mathematical Modelling and PID Controller Implementation to Control Linear and Nonlinear Quarter Car Active Suspension

Mathematical Modelling and PID Controller Implementation to Control Linear and Nonlinear Quarter Car Active Suspension

Vibration control and performance analysis of full car active suspension system using fractional order terminal sliding mode controller

Vibration control and performance analysis of full car active suspension system using fractional order terminal sliding mode controller

Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension

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.

Adaptive fuzzy fault-tolerant control for active seat suspension systems with full-state constraints

In this paper, an adaptive fuzzy fault-tolerant control (FTC) scheme is proposed for active seat suspension systems. The addressed system contains electromagnetic actuator faults. In addition, the constraints of the displacements of the car seat, active suspension, and wheel, their vertical vibration speeds and current intensity are included. In the control design, since the systems have dynamic characteristics of complexities and spring non-linearities, the fuzzy logic systems are utilized to approximate the unknown nonlinear dynamics. By the aid of Barrier Lyapunov functions and based on the adaptive backstepping recursive design algorithm, a novel adaptive fuzzy FTC scheme is formulated. When the electromagnetic actuator fails, the developed control scheme can guarantee that all the vertical vibration states are stable. Eventually, the effectiveness of the presented control method is offered to illustrate by a random road surface.

A New Neural Network-Based Adaptive Time-Delay Control for Nonlinear Car Active Suspension System

A New Neural Network-Based Adaptive Time-Delay Control for Nonlinear Car Active Suspension System

Optimization of automobile active suspension system using minimal order

<p><span>This paper presents an analysis and design of linear quadratic regulator for reduced order full car suspension model incorporating the dynamics of the actuator to improve system performance, aims at benefiting: Ride comfort, long life of vehicle, and stability of vehicle. Vehicle’s road holding or handling and braking for good active safety and driving pleasure, and keeping vehicle occupants comfortable and reasonably well isolated from road noise, bumps, and vibrations are become a key research area conducted by many researchers around the globe. Different researchers were tested effectiveness of different controllers for different vehicle model without considering the actuator dynamics. In this paper full vehicle model was reduced to a minimal order using minimal realization technique. The entire system responses were simulated in MATLAB/Simulink environment. The effectiveness of linear quadratic regulator controller was compared for the system model with and without actuator dynamics for different road profiles. The simulation results were indicated that percentage reduction in the peak value of vertical and horizontal velocity for the linear quadratic regulator with actuator dynamics relative to linear quadratic regulator without actuator dynamics was 28.57%. Overall simulation results were demonstrated that proposed control scheme has able to improve the effectiveness of the car model for both ride comfort and stability.</span></p>

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 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.

Control of Nonlinear Active Suspension System Based on Fuzzy Model Predictive Controller

Recent years, active suspension system has been widely used in automobiles to improve the road holding ability and the riding comfort. This article presents a new fuzzy model predictive control for a nonlinear quarter car active suspension system. A nonlinear dynamical model of active suspension is established, where the nonlinear dynamical characteristic of the spring and damper are considered. Based on the proposed fuzzy model predictive control method is presented to stabilize the displacement of the active suspension in the presence of different road profiles. Parameters of the model predictive and Fuzzy logic control laws are designed to estimate the (Bump and Sinusoidal) road profile input in the active suspension. At last, the reliability of the fuzzy model predictive control method is evaluated by the MATLAB simulation tool. Simulation result shows that the fuzzy model predictive control method obtained the satisfactory control performance for the active suspension system.

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.

Efficiency of PID Controller Based on Genetic Algorithm for controlling a Quarter Car Suspension System

the purpose of this paper is to improve the performance of a car's active suspension system (a quarter model), with two degrees of freedom , a combination of components and mechanisms ensure both passengers' comfort and driving safety, the stability and ride performance depends generally on the quality of the suspension , and this is why car manufacturers are turning to an adjustable suspension that can be adapted to any type of road surface as it controls vertical movement for car wheels. In this work we show how proportional integral differential (PID) controller tuned by using two methods, Ziegler Nichols and genetic algorithms (GA) can used to achieve a good performance of the active suspension system, The simulation results show the robustness and effectiveness of genetic algorithm (GA) in providing good ride quality and good road handling, however the presence of a time delay.

PI Observer-Based Fault-Tolerant Tracking Controller for Automobile Active Suspensions

The objective of this paper is to retain the desirable dynamics performances and improve ride quality for active suspensions with the actuator faults and unknown road disturbances. To that end, a novel proportional-integral observer (PIO)-based fault-tolerant tracking controller (FTTC) design is proposed for automobile active suspensions (ASSs) encountered with actuator faults and parameter uncertainties. First, the Takagi-Sugeno (T-S) fuzzy model approach is adopted to establish T-S representation of the faulty ASSs by describing vehicle dynamics system as the weighed summation of a common linear system. Afterwards, a nominal robust H∞ output feedback controller is developed to enhance the suspension performances under fault-free mode, whose output response indicators are taken as the prescribed reference trajectories. Then, a PIO-based fault estimator is designed to predict both the system states and the unmeasurable actuator faults, synchronously. On basis of this designed observer, the expected PIO-FTTC is synthesized to track the prescribed reference trajectories, and further to make up for the system performance deteriorations aroused by the actuator faults. Finally, a simulative investigation demonstrates the effectiveness and feasibility of the proposed PIO-FTTC compared to existing control approach.

Enhanced Sliding Mode Control for a Nonlinear Active Suspension Full Car Model

This paper delivers findings on optimal robust control studies of nonlinear full car models. A nonlinear active suspension full car model is used, which considers the dynamic of a hydraulic actuator. The investigation on the benefit of using Sliding Mode Control (SMC) structure for the effective trade-off between road handling. The design of SMC in the chassis/internal subsystem is enhanced by modifying a sliding surface based on Proportional-Integral-Derivatives (PID) with the utilization of particle swarm optimization (PSO) algorithm in obtaining the best optimum value of control parameters. The switching control is designed through the Lyapunov function, which includes the boundedness of uncertainties in sprung masses that can guarantee the stability of the control design. The responses of the proposed controller have improved the disturbance rejection up to 60% as compared to the conventional SMC controller design and shown the high robustness to resist the effect of varying the parameter with minimal output deviations. The study proved that the proposed SMC scheme offers an overall effective performance in full car active suspension control to perform a better ride comfort as well as the road handling ability while maintaining a restriction of suspension travel. An intensive computer simulation (MATLAB Simulink) has been carried out to evaluate the effectiveness of the proposed control algorithm under various road surface conditions.

Whale-optimized fuzzy-fractional order controller-based automobile suspension model

The functionality of the Active Suspension System (ASS) concerning stability and ride quality offers scope in modelling and simulation of automobile active suspension model. This work explores the design and simulation of a five degree of freedom ASS based driver integrated half car model (i.e.) Active Half Car Driver (AHCD) model. A novel design approach which assesses the Fuzzy-Fractional Order PID (FFOPID) controller parameters using bio inspired Whale Optimization Algorithm (WOA) is presented. The WOA is employed to manipulate the parameters of FFOPID controller and its efficacy is investigated using a comparative study against the performance of FOPID, FFOPID and Particle Swarm Optimization (PSO) technique tuned FFOPID (PSO-FFOPID) controllers over a single bump and a random road. The ACHD model is also analyzed under different speed levels which discloses performance adaptability of the proposed WOA-FFOPID controller. The Frequency Weighted Root Mean Square and Vibration dose value as per ISO 2631-1 standards are calculated and the results thus acquired are compared comprehensively and are analyzed which confirms that the WOA-FFOPID centered AHCD model provides the best ride comfort by reducing the driver body vibration in vertical motion.

Kinematics and compliance analysis of active suspension system and Development of control algorithm to maximize ride comfort

It is important to understand the interrelationship between steering stability and NVH to adjust stiffness of the suspension to satisfy the aim of maximum ride comfort. This work aims to perform kinematic and compliance analysis on comprehensive quarter car model of McPherson suspension system to validate its reliability with respect to various road profiles and development of control algorithm to control its stiffness. Despite the wide usage of PID controllers, controllers based on Adaptive Neuro Fuzzy Inference System (ANFIS) are proved to give better results as they consider the non-linearity of active car suspension models. The modelling of the comprehensive non-linear quarter car active McPherson suspension system is done using MSC ADAMS and the control algorithm is implemented using MATLAB. The approach used is to perform parametric sensitivity analysis for prime parameters such as toe angle, camber angle and caster angle. The approach used to design a better robust control algorithm is achieved using µ-synthesis function of MATLAB.

Quarter car model optimization of active suspension system using fuzzy PID and linear quadratic regulator controllers

The primary objective of this paper is to improve the performance of a car's active suspension system and control the vibrations that occurred in the car's using two well-known control technologies, namely the Linear Quadratic Regulator (LQR) and fuzzy PID control. When the car suspension is designed, a quarter car model with two degrees of freedom is used. A complete control system is needed to provide the desired suspension performance and characteristics such as passenger comfort, road handling, and suspension deflection, this control system performed using the MATLAB/SIMULINK and includes three parts: input signals (actuator force and road profile), Controller part, and the suspension system model. The simulation results from the implemented Simulink models show a comparison between the uncontrolled suspension system and the suspension system with a fuzzy PID controller and the active suspension system of the car based on the linear-quadratic regulator, and it is explained thoroughly.

Optimal Hardware and Control Co-Design Applied to an Active Car Suspension Setup

For complex systems, it is not easy to obtain optimal designs for the hardware architecture and control configurations. Every design aspect influences the final performance, and often the interactions of the different components cannot be clearly determined in advance. In this work, a novel co-design optimization method was applied that allows the optimal placement and selection of actuators and sensors to be performed simultaneously with the determination of the control architecture and associated controller tuning parameters. This novel co-design method was applied to a state-space model of a downscaled active car suspension laboratory setup. This setup mimics a car driving over a specific road surface while active components in the suspension have to increase the driver’s comfort by counteracting unwanted vibrations. The result of this co-design optimization methodology is a Pareto front that graphically represents the trade-off between the maximum performance and the total implementation cost; the co-design results were validated with measurements of the physical active car suspension setup. The obtained controller tuning parameters are compared herein with existing controller tuning methods to demonstrate that the co-design method is able to determine optimal controller tuning parameters.