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Proportional integral derivative (PID) control technique is the most common control algorithm applied in various engineering applications. Also, particle swarm optimization (PSO) is extensively applied in various optimization problems. This paper introduces an investigation into the use of a PSO algorithm to tune the PID controller for a semi-active vehicle suspension system incorporating magnetorheological (MR) damper to improve the ride comfort and vehicle stability. The proposed suspension system consists of a system controller that determine the desired damping force using a PID controller tuned using PSO, and a continuous state damper controller that estimate the command voltage that is required to track the desired damping force. The PSO technique is applied to solve the nonlinear optimization problem to find the PID controller gains by identifying the optimal problem solution through cooperation and competition among the individuals of a swarm.

There is an increasing customer demand for adjustable chassis control features which enable adaption of the vehicle comfort and driving characteristics to the customer requirements. One of the most promising vehicle control systems which can be used to change the vehicle characteristics during the drive is the semi-active suspension system. This paper presents a Rule-Optimized Fuzzy Logic controller for semi-active suspension systems which can continuously adjust itself not only according to the road conditions but also to the driver requirements. The proposed controller offers three different control modes (Comfort, Normal and Sport) which can be switched by the driver during driving. The Comfort Mode minimizes the accelerations imposed on the driver and passengers by using a softer damping. On the other hand, the increased damping in Sport Mode provides better road holding capability, which is critical for sporty handling.

This paper presents a performance analysis study for the Rule-Optimized controller of a semi-active suspension system. The Rule-Optimized controller is based on a Fuzzy Logic control scheme which offers new opportunities in the improvement of vehicle ride performance. An eleven degree of freedom full vehicle ride dynamics model is developed and validated through laboratory tests performed on a hydraulic four-poster shaker. An optimization process is applied to obtain the optimum Fuzzy Logic membership functions and the optimum rule-base of the semi-active suspension controller. The global optima of the cost function which considers the ride comfort and road holding performance of the full vehicle is determined through discrete optimization with Genetic Algorithm (GA).

In this paper, a nonlinear semi-active vehicle suspension system using MR fluid dampers is investigated to enhance ride comfort and vehicle stability. Fuzzy logic and fuzzy self-tuning PID control techniques are applied as system controllers to compute desired front and rear damping forces in conjunction with a Signum function method damper controller to assess force track-ability of system controllers. The suggested fuzzy self-tuning PID operates fuzzy system as a PID gains tuner to mitigate the vehicle vibration levels and achieve excellent performance related to ride comfort and vehicle stability. The equations of motion of four-degrees-of-freedom semi-active half-vehicle suspension system incorporating MR dampers are derived and simulated using Matlab/Simulink software.

The accuracy of state estimation and optimal control for controllable suspension system is a challenging task for the vehicle suspension system under various road excitations. How to effectively acquire suspension states and choose the reasonable control algorithm become a hot topic in both academia and industry. Uncertainty is unavoidable for the suspension system, e.g., varying sprung or unsprung mass, suspension damping force or spring stiffness. To tackle the above problems, a novel observer-based control approach, which combines adaptive unscented Kalman filter (AUKF) observer and model predictive control (MPC), is proposed in the paper. A quarter semi-active suspension nonlinear model and road profile model are first established. Secondly, using the road classification identification method based on system response, an AUKF algorithm is employed to estimate accurately the state of suspension system.

Following the developments in controlled suspension system components, the studies on the vertical dynamics analysis of vehicles increased their popularity in recent years. The objective of this study is to develop a semi-active suspension system controller using Adaptive-Fuzzy Logic control theories together with Kalman Filter for state estimation. A quarter vehicle ride dynamics model is constructed and validated through laboratory tests performed on a hydraulic four-poster shaker. A Kalman Filter algorithm is constructed for bounce velocity estimation, and its accuracy is verified through measurements performed with external displacement sensors. The benefit of using adaptive control with Fuzzy-Logic to maintain the optimal performance over a wide range of road inputs is enhanced by the accuracy of Kalman Filter in estimating the controller inputs. A gradient-based optimization algorithm is applied for improving the Fuzzy-Logic controller parameters.

Nonparametric models do not require any assumptions on the underlying input/output relationship of the system being modeled so that they are highly useful for studying and modeling the nonlinear behaviour of Magnetorheological (MR) fluid dampers. However, the application of these models in semi-active suspension is very rare and most theoretical works available on this topic address the application of parametric models (e.g. Modified Bouc-Wen model). In this paper, a nonparametric MR damper model based on the Restoring Force Surface technique is applied in vehicle semi-active suspension system. It consists of a three dimensional interpolation using Chebyshev orthogonal polynomial functions to simulate the MR damper force as a function of the displacement, velocity and input voltage. Also, a damper controller based on a Signum function method is proposed, for the first time, for use in conjunction with the system controller of a semi-active vehicle suspension.

Vehicle needs suspension and steering systems with different features to fit different driving conditions. In normal straight driving condition, soft suspension and heavy steering systems are needed to achieve better ride comfort and straight line driving stability; in turning conditions, hard suspension and lightweight steering systems are needed to get better handing stability. The semi-active suspension system with Magneto-Rheological dampers can improve the ride comfort and handling performance of vehicle. Electrical power steering system is developed rapidly due to its portable and flexible operations as well as stable steering performance.

This paper presents a new and effective control concept for semi-active suspension systems. The proposed controller uses a Fuzzy Logic scheme which offers new opportunities in the improvement of vehicle ride performance. The Fuzzy Logic scheme tunes the controller to treat the conflict requirements of ride comfort and road holding parameters within a specified range of the suspension deflection. An eleven degree of freedom full vehicle ride dynamics model is constructed and validated through laboratory tests performed on a hydraulic four-poster shaker. A new optimization process for obtaining the optimum Fuzzy Logic membership functions and the optimum rule-base of the proposed semi-active suspension controller is proposed. Discrete optimization has been performed with a Genetic Algorithm (GA) to find the global optima of the cost function which considers the ride comfort and road holding performance of the full vehicle.

Abstract The aim of this study is to achieve the target transient posture of a vehicle according to the user’s steering operation. The target behavior was hypothesized to be a roll mode in the diving pitch, even during steering inputs on rough surfaces, in order to improve subjective evaluation. As a result of organizing the issues of feedforward control (FF) and feedback control (FB), we hypothesized that it would be appropriate to follow the ideal posture. The model following damping control (MFDC) was newly proposed by the authors as a solution to a control algorithm based on model-following control. The feature employs skyhook control (SH), which follows the deviation between the behavior of the reference model, which generates a target behavior with no input from the road surface, and the actual behavior of the vehicle. Numerical analyses were performed to verify the followability of the target behavior and the effect of roll damping performance.

Abstract Due to their large volume structure, when a heavy vehicle encounters sudden road conditions, emergency turns, or lane changes, it is very easy for vehicle rollover accidents to occur; however, well-designed suspension systems can greatly reduce vehicle rollover occurrence. In this article, a novel semi-active suspension adaptive control based on AdaBoost algorithm is proposed to effectively improve the vehicle rollover stability under dangerous working conditions. This research first established a vehicle rollover warning model based on the AdaBoost algorithm. Meanwhile, the approximate skyhook damping suspension model is established as the reference model of the semi-active suspension. Furthermore, the model reference adaptive control (MRAC) system is established based on Lyapunov stability theory, and the adaptive controller is designed.

Abstract This article proposes a disturbance observer-based event-triggered H ∞ controller for a semi-active seat suspension that equips an advanced electromagnetic damper (EMD) system. Automated driving is one of the leading technologies of the automotive industry. However, automated vehicles (AVs) may increase the incidence of motion sickness (MS) and deteriorate motion comfort. This article investigates a semi-active seat suspension system and an advanced controller to improve the motion comfort of AVs. The disturbance force of the seat suspension has considerable influence on the system dynamic, and applying a constant model to describe the real-time disturbance force is unreliable. Therefore, a disturbance observer is designed to estimate the seat suspension disturbance force, and it is used to compensate the controller. The Bouc-Wen model is selected to compare with the disturbance observer and validate its effectiveness.

Abstract In this work, the three-setting switchable damper (SD) is selected because of its cost-effectiveness compared with semi-active (SA) and active suspension systems. This article aims to compare fuzzy (FLC) and linear quadratic regulator (LQR) control strategies for mechatronic three-setting SD and active suspensions in terms of ride comfort considering the switching dynamics nonlinearities of the SD. A four-degree-of-freedom half-car model is utilized for this study. The switchable inerter (SI) is included in these systems to investigate its effect on the ride performance. A comparison between the active suspension and three-setting SD suspension systems with and without SI is assessed. The optimal parameters for passive suspension are evaluated. Results showed that the fuzzy control strategy gives a better ride comfort than the LQR up to 6.9% and 14% for three-setting SD and active suspension systems, respectively.

Abstract When the vehicle is under braking condition in the longitudinal motion, the vehicle body will tilt due to the inertial force in motion. A high amplitude will result in uncomfortable feelings of the occupant, such as nervousness or dizziness. To solve the problem, this article presents an adaptive damping system (ADS), which combines the vehicle anti-pitch compensation control with the mixed skyhook (SH) and acceleration-driven-damper (ADD) control algorithm. This ADS can not only improve the vibration effect of the vertical motion for the vehicle but also consider the longitudinal motion of the vehicle body. In addition, a new damper mechanical hardware-in-the-loop test bench is built to verify the effectiveness of the algorithm.

Abstract The aim of this study is to develop an Add-On Feature that could support the semi-active suspension system controller during longitudinal dynamics maneuvers. The Add-On Feature called Initial Pitch Control (IPC) is activated during launching, shifting, and braking to enhance the pitch motion characteristics and road-holding capability. A sixteen degrees-of-freedom (DoF) vehicle mathematical model represents the vertical and longitudinal dynamics developed and validated via laboratory and road tests. A hydraulic four-poster test rig is used to carry out the laboratory tests for the vertical dynamics verification, while the longitudinal dynamic verification is achieved through the performed tests on a highway track. In order to design the IPC algorithm, the Rule-Optimized (RO) semi-active suspension controller, an Anti-lock Braking System (ABS) controller, and seven gears Dual-Clutch Transmission (DCT) controller are implemented in the vehicle model.

Abstract In this article, the nonlinear pneumatic magnetorheological (MR) suspension system is designed to improve vehicle characteristics in both ride comfort and dynamic stability. The four-degree-of-freedom (4-DOF) half-vehicle suspension system that is described based on bounce and pitch motions is derived. Both interval type-1 and interval type-2 of fuzzy models are applied as alternative controllers for the pneumatic MR suspension system. Both a controlled force of air spring and tracking ability of desired damping force are generated for each wheel of alternative controllers. In order to apply voltages for both the front and rear MR dampers, the tracks of desired damping forces are incorporated with the front MR damper controller and rear MR damper controller, respectively. The conventional damping case of the passive suspension system is used as a baseline for comparisons.

Abstract This article presents a semi-active vibration control suspension system using a preview Model Predictive Control (MPC) linked with a magnetorheological (MR) damper to improve vehicle stability during handling dynamics, consequently confidently achieving both maneuverability and lateral dynamic motion. The mathematical model (4DOF) described by bounce and pitch motions for sprung mass and two bounce motions for the un-sprung masses, which consists of a preview half-vehicle suspension system and MR dampers at the front and rear axles, is derived. A nonpreview case of the linear quadratic regulator (LQR), a preview case of the LQR, and a preview case of the MPC as alternative methods are applied to design the system controller in combination with a signum function method as a damper controller for both the front and rear MR dampers. The vehicle handling model based on the look-ahead distance of the road, which includes yaw and lateral motions, is linked with the driver model.

Abstract Aiming at improving safety (anti-roll performance) with consideration of ride comfort of vehicles during cornering and over road irregularities, magnetorheological (MR) fluid-based semi-active anti-roll bar is investigated in this article. The vehicle roll model with both roll stiffness and roll damping of the vehicle body influenced by the MR anti-roll bar is established to analyze the impact of the torsional stiffness and torsional damping. Combining with the Pareto front of the lateral load transfer ratio (LTR) of the front axle, the optimal roll stiffness and roll damping of a vehicle are determined, and correspondingly the torsional stiffness and torsional damping of the anti-roll bar are determined. And then the mathematical model and multibody dynamic model of the anti-roll bar are established, and the simulation of the MR semi-active anti-roll bar model is carried out via MATLAB/Simscape Multibody.

Abstract As known to all, it is a challenging task to solve the delay of a controllable suspension system under the transient road. Thus, how to effectively and low-costly acquire road information and choose the reasonable control algorithm remains a hot topic in both academia and industry. With the rapid development and extensive application of the advanced intelligent driving system, a large number of sensors, such as cameras, have been installed on the vehicle, and deep learning technology has also been widely used to identify lane recognition, traffic direction signal, and pedestrian detection, but rarely used in semi-active suspension control. To address the above issues, a novel skyhook preview control (SPC) approach, which combines supervised deep learning, is proposed in the article. Firstly, a full vehicle dynamics model for semi-active suspension is established.

In this research, a semi-active suspension system using a quarter car model has been investigated, and a novel model reference sliding mode control scheme in controlling the semi-active suspension system has been proposed. The proposed scheme uses an approximate ideal skyhook system as a reference model. The controller can ensure robustness for a wide range of operating conditions, and is easy to be carried out and eliminates the necessity of a road signal as well as measuring damper force in real-time. The control law is determined so that an asymptotically stable sliding mode will occur in the error dynamics between the plant and the reference model states. A simulation study is performed to prove the effectiveness and robustness of the control approach.