Automotive Active Suspension Research Articles

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>

Performance analysis of fuzzy-LQR and fuzzy-LQG controllers for active vehicle suspension systems

Suspension systems in vehicles are crucial to both ride quality and driving security. The challenge of creating an effective control mechanism for automotive active suspension systems is addressed in this work. In the event of unforeseen road disturbances, the active suspension systems are intended to deliver a more pleasant ride and good handling. Fuzzy-Linear Quadratic Gaussian (FLQG) and Fuzzy-Linear Quadratic Regulator (FLQR) controllers adapting to road disturbances are proposed to enhance vehicle comfort through the reduction of the driver's overall body acceleration. The simulation findings demonstrate that the FLQR and FLQG controllers are efficient in adjusting the automotive suspension configuration under varying road profiles compared to those of the conventional LQR and LQG controllers.

Optimizing a Hybrid Controller for Automotive Active Suspension System by Using Genetic Algorithms With Two High Level Parameters

Optimizing a Hybrid Controller for Automotive Active Suspension System by Using Genetic Algorithms With Two High Level Parameters

Experimental Assessment of a Controlled Slippage Magnetorheological Automotive Active Suspension for Ride Comfort

Experimental Assessment of a Controlled Slippage Magnetorheological Automotive Active Suspension for Ride Comfort

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.

Experimental assessment of a linear actuator driven by magnetorheological clutches for automotive active suspensions.

The main functions of automotive suspensions are to improve passenger comfort as well as vehicle dynamic performance. Simultaneously satisfying these functions is not possible because they require opposing suspension adjustments. This fundamental design trade-off can be solved with an active suspension system providing real-time modifications of the suspension behavior and vehicle attitude corrections. However, current active suspension actuator technologies have yet to reach a wide-spread commercial adoption due to excessive costs and performance limitations. This paper presents a design study assessing the potential of magnetorheological clutch actuators for automotive active suspension applications. An experimentally validated dynamic model is used to derive meaningful design requirements. An actuator design is proposed and built using a motor to feed counter-rotating MR clutches to provide upward and downward forces. Experimental characterization shows that all intended design requirements are met, and that the actuator can output a peak force of ±5300 N, a peak linear speed of ±1.9 m/s and a blocked-output force bandwidth of 92 Hz. When compared to other relevant technologies, the MR approach simultaneously shows both better force density and speeds (bandwidth) while adding minimal costs and weight. Results from this experimental assessment suggest that MR slippage actuation is promising for automotive active suspensions.

Improved fireworks algorithm with information exchange for function optimization

The fireworks algorithm, which is inspired by the explosion of fireworks, is a new swarm-based meta-heuristic algorithm for global optimization. This work proposes an improved fireworks optimization algorithm (IFWA) based on the enhanced fireworks algorithm (EFWA). Three aspects of improvement are presented after an analysis of the drawbacks of EFWA. These improvements are a new explosion scheme, GS-Gaussian explosion operator, and deep information exchange strategy. The proposed IFWA is tested on 23 benchmark function optimization problems and a real engineering problem, namely, optimal controller design for automotive active suspension. Optimization results prove that IFWA has competitive advantage compared with EFWA and other popular meta-heuristic algorithms and demonstrates the potential to solve real problems effectively.

Development of A New Automotive Active Suspension System

The main objective was to develop a smart new vehicle suspension system that minimizes the road irregularities impact on the driver, also to increase performance and stability of the vehicle at high speeds. The central idea is based on modifying the normal passive suspension system into a computer controller hydraulic actuated active suspension system simply by adding a new component such as a hydraulic cylinder on a normal passive system. The new suspension system is economical to be wildly used in consumer’s cars with low prices. The new added components was analytically tested and modeled according to different parameters. A new test rig was implemented to simulate a real quarter suspension system. The new suspension model was controlled by feedback controller according to the road conditions; the controller output controls the cylinder actuator to compensate the road oscillations and increases the vehicle stability for the passenger. Finally, to maximize the aerodynamics coefficients of the vehicle during high speeds by controlling the vehicle clearance level from the ground to achieve full stability, steering and fuel economy.

Active Vibration Control of Automotive Suspension System using Fuzzy Logic Algorithm

This study details an efficient fuzzy logic controller (FLC) to improve the performance of active automotive suspension system. A comparison between passive and FLC active suspensions is performed. A mathematical model of automotive active suspension has six degrees of freedom and two input forces generated by two separate actuators are solved using Matlab Simulink. In order to evaluate the effectiveness of the proposed controller under random road disturbance, several performance criteria are assessed based on the dynamic response of the half automotive suspension system. Simulation results of the active suspension system based on the fuzzy logic clearly have been provided to illustrate the effectiveness of the FLC under different road conditions and confirmed that fuzzy logic is very effective for enhancing ride comfort and stability of the vehicle.

Fault-Tolerant Actuating Pressure Controller Design for an Electrohydrostatic Actuator Experiencing a Leaky Piston Seal

Electrohydrostatic actuators (EHAs), as a class of pump-controlled hydraulic actuators, are known for energy efficiency and easy maintainability. Thus, they can be widely used in the situations where actuating pressure/force control of hydraulic actuators is essential. Examples are automotive active suspension, deep-drawing press, molding machine, and vibration isolation. However, a leaky piston seal in an EHA system can be especially problematic as it is not visually detectable, but causes internal leakage flowing across actuator chambers impairing the performance. This paper employs quantitative feedback theory (QFT) to design a robust fixed-gain linear actuating pressure controller that is tolerant to actuator internal leakage. Since QFT captures uncertainties by templates, representing frequency responses of the plant on Nichols chart, the first step, to design a QFT controller, is to establish plant templates. In doing so, a set of offline parametric system identifications are implemented, and the family of identified models, providing frequency responses, are used to design the QFT fault-tolerant controller. The controller also satisfies the prescribed design tolerances on tracking, stability and sensitivity (disturbance rejection at plant output) under different conditions, including various levels of actuator internal leakage, environmental stiffnesses, and load masses. The ability of the controller to maintain actuating pressure within the acceptable response envelope is demonstrated in experiments. The experimental results show that the system specifications are satisfied despite internal leakage up to 12 L/min.

Co-Design of an Active Suspension Using Simultaneous Dynamic Optimization

Design of physical systems and associated control systems are coupled tasks; design methods that manage this interaction explicitly can produce system-optimal designs, whereas conventional sequential processes may not. Here, we explore a new technique for combined physical and control system design (co-design) based on a simultaneous dynamic optimization approach known as direct transcription, which transforms infinite-dimensional control design problems into finite-dimensional nonlinear programming problems. While direct transcription problem dimension is often large, sparse problem structures and fine-grained parallelism (among other advantageous properties) can be exploited to yield computationally efficient implementations. Extension of direct transcription to co-design gives rise to new problem structures and new challenges. Here, we illustrate direct transcription for co-design using a new automotive active suspension design example developed specifically for testing co-design methods. This example builds on prior active suspension problems by incorporating a more realistic physical design component that includes independent design variables and a broad set of physical design constraints, while maintaining linearity of the associated differential equations. A simultaneous co-design approach was implemented using direct transcription, and numerical results were compared with conventional sequential optimization. The simultaneous optimization approach achieves better performance than sequential design across a range of design studies. The dynamics of the active system were analyzed with varied level of control authority to investigate how dynamic systems should be designed differently when active control is introduced.

Implementation of LQR Controller on Electromagnetic Suspension System for Passenger’s Car

In the past few decades, researchers have been focusing on improving control strategy for automotive active suspension system. There are few configurations used in active suspension mechanism and one of them is Electromagnetic Suspension (EMS) System. In this study, the implementation of a Linear Quadratic Regulator (LQR) controller in an EMS System for vehicle Active Suspension System (ASS) was investigated. This provides an optimal control solution for the active suspension system. Quarter vehicle model of an electromagnetic suspension was derived and employed to study the implementation of the LQR controller. Detailed parametric analysis for the proposed controller and implementation results will be presented in this paper. This is done by investigating the effect of weighting parameters in the performance index of the optimal control strategy in stabilising the vehicle response. The controller’s performance was compared against a passive suspension system based on a commercial passenger’s car. Parametric analyses revealed the importance of weighting parameters in the optimal control formulation. Finally, it was proven that LQR controller strategy provides a satisfactory improvement for the EMS system compared to passive suspension performance

Based on Single Neuron PID Control of Vehicle Active Suspension System

Relative to the passive suspension, automotive active suspension car driving more ride comfort and stability, has a vital role to further improve the performance of the vehicle. For such a typically complex active suspension system research, the key issue is the selection of control strategies. The problems in the currently active suspension control strategy, the principle of a simple, effective, this paper, a single neuron PID control strategy used in the automotive active suspension system. The results show that compared with other control strategies, single neuron PID control strategy is reliable, has more advantages.

An embedded implementation of the Generalized Predictive Control algorithm applied to automotive active suspension systems

The Generalized Predictive Control (GPC) algorithm relies on the solution of an optimization problem at every sampling period. Profiling shows that matrix operations consume the largest portion of the computation requirements of the algorithm. This paper presents an embedded real-time implementation of the GPC algorithm, called GPC-on-Chip, based on the state-of-the-art Customizable Advanced Processor (CAP9™) technology from Atmel®, targeting automotive active suspension systems. Our system utilizes a systolic-array based matrix co-processor in order to accelerate matrix operations. The proposed embedded system is designed to fit within the proposed platform while meeting tight real-time constraints imposed by automotive active suspension systems. In order to check the applicability of the proposed system-on-chip, it is profiled against a wide variety of GPC tuning parameters and compared against the software-only implementation. An average speedup of approximately 10× is achieved.

A hybrid electromagnetic shock absorber for active vehicle suspension systems

The use of electromagnetic dampers (ED) in vehicle active suspension systems has drawn considerable attention in the past few years, attributed to the fact that active suspension systems have shown superior performance in improving ride comfort and road handling of terrain vehicles, compared with their passive and semi-active counterparts. Although demonstrating superb performance, active suspensions still have some shortcomings that must be overcome. They have high energy consumption, weight, and cost and are not fail-safe in case of a power breakdown. The novel hybrid ED, which is proposed in this paper, is a potential solution to the above-mentioned drawbacks of conventional active suspension systems. The proposed hybrid ED is designed to inherit the high-performance characteristics of an active ED with the reliability of a passive damper in a single package. The eddy current damping effect is utilised as a source of the passive damping. First, a prototype ED is designed and fabricated. The prototype ED is then utilised to experimentally establish the design requirements for a real-size active ED. This is accomplished by comparing its vibration isolation performance in a 1-DOF quarter-car test rig with that of a same-class semi-active damper. Then, after a real-size active ED is designed, the concept of hybrid damper is introduced to the damper design to address the drawbacks of the active ED. Finally, the finite-element method is used to accurately model and analyse the designed hybrid damper. It is demonstrated that by introducing the eddy current damping effect to the active part, a passive damping of approximately 1570 Ns/m is achieved. This amount of passive damping guarantees that the damper is fail-safe and reduces the power consumption more than 70%, compared with an active ED in an automotive active suspension system.

Distributed Dynamic Scheduling of Controller Area Network Messages for Networked Embedded Control Systems

AbstractThis paper presents an online, dynamic, and distributed message scheduling of communication packets over the controller area network (CAN) bus. Using online, dynamic, and distributed scheduling of messages, one can obtain better temporal characteristics for networked embedded control systems. The scheduling algorithm is implemented as a hardware unit which is augmented to the communication controller on all of the communicating nodes in the system. This insures minimum change in the current platforms used in CAN-based control applications like those found heavily in automotive and aerospace applications. We provide a number of experiments held over a physical CAN bus for controlling an automotive active suspension system and showing the effect of the proposed scheduling implementation on the quality of the control loop.

Towards Real-Time Networked Embedded Generalized Predictive Control for Automotive Active Suspension Systems

This paper emphasizes the implementation challenge of real-time embedded generalized predictive control (GPC) when applied to a class of automotive active suspension systems. A real-time experimental environment is specially designed in order to verify parameters tuning in closed-loop with the virtual process environment. Real-time experimentation carried on this environment shows the necessity of high prediction and control horizons setting to achieve acceptable comfort performance. A control algorithm is decomposed into parallel real-time computations using a set of systolic array based processors. A systemC model is constructed to verify the proposed implementation. Simulation and profiling results show the feasibility and the efficiency of GPC in future hardware implementation.

MPC-On-Chip: An Embedded GPC Coprocessor for Automotive Active Suspension Systems

Safety critical automotive systems require tight real-time constraints. This letter presents a case study for embedded implementation of a model predictive controller (MPC) used to control an automotive active suspension system. It proposes a special purpose coprocessor for solving the online mathematical optimization needed by the controller, while maintaining real-time constraints imposed by system dynamics. Mathematical formulation for the optimum usage of hardware resources is provided and verified for its applicability on a proposed platform.

2C24 Arrangement of actuators to save energy consumed in automotive active suspensions

2C24 Arrangement of actuators to save energy consumed in automotive active suspensions

Using fuzzy logic controller and evolutionary genetic algorithm for automotive active suspension system

This study designs a fuzzy logic controller (FLC) for an active automobile suspension system in which the membership functions and control rules are optimized using a genetic algorithm (GA). The objective of the FLC is to strike an optimal balance between the ride comfort and the vehicle stability. The values of the crossover and mutation parameters in the GA are adapted dynamically during the convergence procedure using a fuzzy control scheme. The convergence state of the GA is determined by using a support vector machine (SVM) method to identify the variation in each of the genes of the best-fit GA chromosome following each iteration loop. The feasibility of the proposed GA-assisted FLC scheme is verified by performing a series of numerical simulations in which the characteristics of the controlled plant are compared with those observed in a passive suspension system and obtained under an optimal linear feedback controller. The results demonstrate that the GA-assisted FLC results in a lower suspension deflection, a reduced sprung mass acceleration and a lower bouncing distance between the tire and the ground.