Active Suspension Model Research Articles

Optimizing full vehicle active suspension model with advanced reinforcement learning controller

Optimizing full vehicle active suspension model with advanced reinforcement learning controller

Robust non-fragile hybrid control for delayed uncertain singular impulsive jump systems based on improved impulse instant-dependent auxiliary functions method

This article researches the issue of robust non-fragile hybrid control for delayed uncertain singular impulsive jump systems (USIMJSs). The key aim is to design non-fragile hybrid state feedback controllers (including a non-fragile normal state feedback controller and a non-fragile impulsive state feedback controller), which are insensitive to the uncertainties of gains of controllers and can provide sufficient tuning margins. The non-fragile normal state feedback controller can eliminate the internal impulses and overcome the external disturbances; the non-fragile impulsive state feedback controller can suppress the interference of external unstable impulses and restrain the instantaneous jumps caused by Markovian modes switching. By introducing impulse instant-dependent auxiliary functions, the improved impulse-time-dependent Lyapunov–Krasovskii functional is constructed, which can capture the information of the impulse instants and Markovian jump modes. Novel criteria of robust admissibilization for delayed USIMJSs are acquired under linear matrix inequalities framework. Lastly, the effectiveness of the derived algorithm and designed method is confirmed by simulation examples including a direct current motor-controlled inverted pendulum device and a Quarter-Car active suspension model.

Design of Automotive Active Suspension System and Simulation for Intelligent Control Strategy

<p>This article focuses on the main suspension systems of current automobiles and the problems existing in active suspension systems. Firstly, the working principle of electromagnetic active suspension systems is explained, and the system design of active suspension is completed. In order to facilitate analysis, a mathematical model of active suspension is built. Then, under the same road parameters, the model is simulated and verified to verify the improvement of structural performance. Secondly, for the control strategy of vehicle stability, a PID controller is adopted, with vehicle acceleration, suspension travel, and tire dynamic load as the input objectives of the controller. The input parameters are automatically tuned and optimized using a bee colony optimization algorithm, and a mathematical expression for the optimization objective is designed. Finally, the optimal solution of the optimization objective is obtained through simulation. Through the method presented in this article, a better design and control scheme for active suspension systems can be obtained, and the practical results have strong guiding significance.</p> <p> </p>

Development of a Novel Suspension System for Electric Vehicles Based on Mechanical Principles

Abstract The electric vehicle industry has witnessed significant growth in recent years, prompting heightened research interest in its various components. The suspension system, serving as a critical power-transmitting device between the frame and the axle, has garnered particular focus. This system is pivotal in meeting the dual performance objectives of ensuring driving smoothness and enhancing maneuvering stability. Accordingly, this study investigates the structural types of suspension systems and advances the design of a suspension guiding mechanism specifically tailored for electric vehicles. At the same time, the dynamic model of active suspension is constructed, and the algorithm for optimizing and establishing the active suspension system for electric vehicles is used. The simulation experiments on the suspension system, the control optimization method of the system in this paper’s car body spring loaded mass acceleration response, dynamic deflection ring, and the control input are stable between -0.4~0.5m/s², -0.005~0.01m and the suspension system’s between -0.7~0.6N, respectively. Moreover, the car body displacement and speed are gradually stabilized at about ±0 compared to the motion speed. In this paper, the suspension system can effectively solve the problem of automobile damping control and reduce the impact suffered by the body so as to improve the smoothness of the body in the process of driving and the operation of the stability of the new for the development and improvement of the suspension system in the electric vehicle industry to provide a reliable method.

Self-Learning Takagi–Sugeno Fuzzy Control With Application to Semicar Active Suspension Model

Self-Learning Takagi–Sugeno Fuzzy Control With Application to Semicar Active Suspension Model

Dynamic Responses of 8-DoF Vehicle with Active Suspension: Fuzzy-PID Control

The driving smoothness of vehicles is heavily influenced by their suspension system, and implementing active suspension control can effectively minimize the vibration movement of the vehicle and ensure a comfortable driving experience. An 8-DoF active suspension model of the full vehicle is established, and a fuzzy-PID controller is designed to autonomously regulate the parameters of the PID controller. Using the MATLAB/Simulink environment, a simulation model for suspension is created, and the vibration characteristics of passive, PID control, and fuzzy-PID control suspensions are compared with the help of the continuous crossing road hump model and C-level road model as road inputs. The results show that the utilization of fuzzy-PID control considerably diminishes the vertical, pitch, and roll oscillations of the suspension body and modifies the suspension dynamic deflection and tire dynamic load in contrast to the other two scenarios, thus enhancing ride comfort. Fuzzy-PID control led to a decrease of approximately 40% in acceleration, 25% in suspension workspace, and 30% in tire deflection compared to passive suspension. In addition, the reduction in acceleration is about 20%, the reduction in suspension workspace is approximately 10%, and the reduction in tire deflection is about 15% compared to the PID control suspension system.

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.

Two-Degree of Freedom-Based Control Model for Active Suspension System to Mitigate the Nonlinear Disturbance

Comfort is an important consideration in passenger vehicles provided by a suspension system. The suspension system must be associated with the vehicle body to improve comfort for the passenger. The active suspension system (ASS) has been introduced to perform this task. Moreover, the sliding mode controller (SMC) is well known for its continuous control signals. This paper proposes the Bayesian-based proportional resonant sliding mode controller (B-PRSMC) and two degrees of the freedom-proportional resonant controller (2d-f-PR) to stabilize passenger and ride comfort. The B-PRSMC is used for examining system states under varying road disturbances based on the Bayesian theorem. After examining the system state, the system performance is controlled by the 2d-f-PR controller. The proposed method is performed on Matlab, and the results are taken regarding body acceleration, body travel and suspension deflection. The mastery of proposed method is estimated under different road profiles. The comparative analysis demonstrates that the suggested controller enhances ride and passenger comfort in varied road profiles due to the combination of B-PRSMC and 2d-f-PR controllers.

A Differential Flatness-Based Model Predictive Control Strategy for a Nonlinear Quarter-Car Active Suspension System

Controlling an automotive suspension system using an actuator is a complex nonlinear problem that requires both fast and precise solutions in order to achieve optimal performance. In this work, the nonlinear model of a quarter-car active suspension is expressed in terms of a flat output and its derivatives in order to embed the nonlinearities of the system in the flat output. Afterward, a Model Predictive Controller based on the differential flatness derivation (MPC-DF) of the quarter-car is proposed in order to achieve optimal control performance in both passenger comfort and road holding without diminishing the lifespan of the wheel. This formulation results in a linear optimization problem while maintaining the nonlinear behavior of the active suspension system. Afterward, the optimization problem is solved by means of Quadratic Programming (QP), enabling real-time implementation. Simulation results are presented using a realistic road disturbance to show the effectiveness of the proposed control strategy.

Improving Vibration Performance of Electric Vehicles Based on In-Wheel Motor-Active Suspension System via Robust Finite Frequency Control

This paper presents a robust finite frequency <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H} _{\infty }$ </tex-math></inline-formula> control strategy for improving vibration performance and ride comfort of electric vehicles through in-wheel motor-active suspension system(IWM-ASS). Since the human body is much sensitive to the vertical vibration of 4 -8 Hz, the main objective is dedicated to deal with the vibration challenge that matches the characteristics of the human body by applying the finite-frequency technique. Firstly, the uncertain quarter-vehicle active suspension model with dynamic damping in-wheel motor driven system is established, in which in-wheel motor is suspended as dynamic vibration absorber(DVA) to isolate the force transmitted to motor bearing in IWM-ASS. Based on the framework of generalized Kalman–Yakubovich–Popov lemma and stability theory, then the performance index of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H} _{\infty }$ </tex-math></inline-formula> norm from external disturbance to controlled output for IWM-ASS is attenuated within the concerned frequency range while other system requirements such as parameter uncertainty, suspension deflection constraint and actuator saturation are also guaranteed in controller design. The resulting robust finite frequency state feedback <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H} _{\infty }$ </tex-math></inline-formula> controller is finally designed utilizing two new theorems, and solved via a set of linear matrix inequalities. Simulations for frequency-domain and time-domain responses are implemented and compared with the entire frequency control method to evaluate the effectiveness of the proposed strategy. It can be concluded from the results that the developed control strategy can effectively attenuate the negative vibration and enhance ride comfort and road-holding ability for electric vehicles of IWM-ASS.

Analysis of Active Suspension Control Based on Improved Fuzzy Neural Network PID

To improve the comfort and smoothness of vehicle driving and reduce the vehicle vibration caused by uneven road surface. In this paper, a new active suspension control strategy is pro-posed by combining a fuzzy neural network and a proportional-integral-derivative (PID) controller, taking body acceleration as the main optimization target and adjusting the parameters of the PID controller in real time. Meanwhile, a fuzzy neural network parameter optimization algorithm combining a particle swarm optimization algorithm and gradient descent method is proposed to realize offline optimization and online fine-tuning of fuzzy neural network parameters. Finally, the active suspension model of a 2-degree-of-freedom 1/4 vehicle is established using MATLAB/Simulink, and the proposed control scheme is verified through simulation studies. The results show that the active suspension system with a particle swarm-optimized fuzzy neural network control method improves the spring mass acceleration, dynamic deflection of suspension, and dynamic tire deformation by 30.4%, 17.8%, and 15.5%, respectively, compared with the passive suspension. In addition, there are also 14.6%, 12.1%, and 11.2% performance improvements, respectively, compared to the PID-controlled active suspension system. These results indicate that the control strategy proposed in this paper can improve the vehicle driving performance and can support the design and development of active suspension systems.

Vertical load distribution strategy of amphibious wheel-track vehicle using neural network and particle swarm optimization

Amphibious wheel-track vehicle (AWTV) can change the vertical load of the wheels and the tracks through the active hydro-pneumatic suspension system, which shows significant advantages in terms of maneuverability and road passing ability. However, AWTV is a strong nonlinear system. The irregularity of the road, and coupling characteristics between the vertical load of the wheel-tracks and the terrain will greatly affect the tractive efficiency of the whole vehicle. Therefore, how to effectively distribute the vertical load between the wheel and the track to improve the tractive efficiency of the whole vehicle is still a huge challenge. To address the above problems, based on the neural network (NN) and particle swarm optimization (PSO) algorithm, vertical load distribution strategy of the AWTV is proposed to improve its traction efficiency under different driving road in this paper. Firstly, the coupling dynamics model of AWTV with road and hydraulic system dynamics model of active suspension is established; Secondly, the wheel-track terrain model is built in EDEM-Recurdyn to collect data of vertical load and traction efficiency under the different soils and speeds, and the coupling function is obtained through NN; Then, the optimal vertical load of each axle is optimized through PSO; Finally, the feasibility and effectiveness of the strategy are verified by the simulation analysis under different road conditions and distribution strategies. The simulation and test results demonstrate that the proposed vertical load distribution strategy based on NN-PSO can effectively improve the traction performance of the AWTV in complex terrain environment, and have relatively superior control characteristics.

Comparative analysis of full car model with driver using PID and LQR controllers

In this study, an active suspension model is proposed to increase the driving safety and passenger comfort of the car, which is adversely affected by car-road interaction. In this context, the full car model with a driver is considered as eight degrees of free-dom. All the motion equations of the car were determined by the Lagrangian method and converted into state-space forms. Then, these equations are solved precisely by us-ing Euler's method in Matlab software. PID and LQR controllers are designed to reduce the vertical displacement of the passenger and the rotational movements of the car. In order to evaluate the performance of the designed controllers, two different road inputs and two different car speeds are taken into account. In the simulation results, the verti-cal displacement and acceleration values of the driver and the rotational movements of the car were examined. While the PID controller stands out in damping the dis-placement values at low speeds, the LQR controller is much more successful in other cases.

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.

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.

Robust Vibration Control for Active Suspension System of In-Wheel-Motor-Driven Electric Vehicle Via μ-Synthesis Methodology

Abstract This paper proposes a robust vibration controller design for active suspension system of in-wheel-motor-driven electric vehicles (IWMD-EVs) based on unified μ-synthesis framework. First, multiple parameter uncertainties and unmodeled high-order dynamics of the suspension are analyzed and discussed. By applying the mixed uncertainties and linear fraction transformation, model perturbations are separated from the suspension system and their perturbation bounds can also be limited. Then, the uncertain quarter-vehicle active suspension model with dynamic damping in-wheel-motor-driven system is established, in which in-wheel motor is suspended as a dynamic vibration absorber. The resulting robust μ-synthesis feedback controller of generalized closed-loop active suspension system is designed with the concept of structured singular value (SSV) μ and μ-synthesis theoretics, and solved via comprehensive solution of the D–G–K iteration. The μ analysis results show that the μ-controller possesses less conservative stability and performance margins as compared to the H∞ method against system uncertainties. Furthermore, simulations of nominal and perturbed suspension systems are implemented and the corresponding frequency and time-domain responses are compared, and then simulation results confirm that the developed μ-controller is capable of attenuating the negative vibration of the active suspension system compared with H∞ controller and passive suspension.

Development of Robust Guaranteed Cost Mixed Control System for Active Suspension of In-Wheel-Drive Electric Vehicles

This paper presents a mixed H2/H∞-based robust guaranteed cost control system design of an active suspension system for in-wheel-independent-drive electric vehicles considering suspension performance requirements and parameter variation. In the active suspension system model, parameter uncertainties of active suspension are described by the bounded method, and the perturbation bounds can be also limited; then, the uncertain quarter-vehicle active suspension model where in-wheel motor is suspended as a dynamic vibration absorber is established. The robust guaranteed cost mixed H2/H∞ feedback controller of the closed-loop active suspension system is designed using Lyapunov stability theory, in which the suspension working space, dynamic tire displacement, and the active control force are taken as H∞ performance indices, the H2 norm of body acceleration is selected as the output performance index to be minimized, and then a comprehensive solution is transformed into a convex optimization problem with linear matrix inequality constraints. Simulations on random and bump road excitations are implemented to verify and evaluate the performance of the designed controller. The results show that the active suspension with developed robust mixed H2/H∞ controller can effectively achieve better ride comfort and road-holding ability compared with passive suspension and alone H∞ controller.

An MPC-LQR-LPV Controller with Quadratic Stability Conditions for a Nonlinear Half-Car Active Suspension System with Electro-Hydraulic Actuators

The active suspension system of a vehicle manipulated using electro-hydraulic actuators is a challenging nonlinear control problem. In this research work, a novel Linear Parameter Varying (LPV) State-Space (SS) model with a fictional input is proposed to represent a nonlinear half-car active suspension system. Four different scheduling parameters are used to embed the nonlinearities of both the suspension and the electro hydraulic actuators to represent its nonlinear behavior. A recursive least squares (RLS) algorithm is used to predict the future behavior of the scheduling parameters along the prediction horizon. A Model Predictive Control-Linear Quadratic Regulator (MPC-LQR) is implemented as the control strategy and, to ensure stability, Quadratic Stability conditions are imposed as Linear Matrix Inequalities (LMI) constraints. Furthermore, the inclusion of attraction sets to overcome the conservative performance imposed by the Quadratic Stability conditions is included, as well as a terminal set were the switching between the MPC and the LQR controller is made. Simulations results for the half-car active suspension model over a typical road disturbance are tested to show the effectiveness of the proposed MPC-LQR-LPV controller with quadratic stability conditions in terms of comfort and road-holding.

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.

Design and Performance Assessment of Adaptive Harmonic Control for a Half‐Car Active Suspension System

The vehicle suspension system is represented by a complex group of components which connect the wheels to the frame or body. Its primary function is to reduce or absorb various vehicle vibrations generated by road disturbances, providing comfort and safety for passengers. Most modern vehicles have independent, active, or semiactive front and rear suspensions which allow the use of electronic actuation. For this reason, automotive engineers conduct research on the active suspension model to determine the most suitable control algorithm. Three active suspension models are intensely used within simulations: the quarter‐car, the half‐car, and the full‐car models. This paper proposes an adaptive harmonic control for a half‐car active suspension system. The mathematical model of the suspension system is implemented in the MATLAB/Simulink environment. The control approach is tested via simulation, and several comparisons with the classical proportional‐integral controller are provided. The simulation results show that the controller behaves quite similarly on the half‐car model as it did on a quarter‐car model. Additionally, an improvement to the harmonic control algorithm has been accomplished.