Full Vehicle Active Suspension Research Articles

Optimizing full vehicle active suspension model with advanced reinforcement learning controller

Optimizing full vehicle active suspension model with advanced reinforcement learning controller

A low-complexity fixed-time prescribed performance control for uncertain full-vehicle active suspensions with hydraulic actuators

This paper addresses the low-complexity fixed-time prescribed performance control problem for uncertain full-vehicle active suspension systems. Different from existing results that ignore the dynamics of the actuator, this paper considers a hydraulic actuator in the controller design. To address the nonlinearities of hydraulic active suspension systems, a new low-complexity fixed-time prescribed performance control method is proposed. In this method, the function approximators (e.g. neural networks (NNs) and fuzzy systems (FSs)) are not needed, which means that the heavy computational costs are avoided. Furthermore, by developing a fixed-time performance function, the proposed method can ensure that the suspension motions converge to the prescribed range within a finite time. Based on the Lyapunov theorem and Extreme Value Theorem, the stability of the closed-loop suspension system is strictly proved. Finally, the comparative simulation results show that the proposed method effectively improves the attitude stability and ride comfort of the suspension system.

Neural Network-Interacted Robust Coordinated Control of Full-Vehicle Active Air Suspension With Uncertainties and Time Delays

Neural Network-Interacted Robust Coordinated Control of Full-Vehicle Active Air Suspension With Uncertainties and Time Delays

A Joint Fault-Tolerant and Fault Diagnosis Strategy for Multiple Actuator Faults of Full-Vehicle Active Suspension Systems

A Joint Fault-Tolerant and Fault Diagnosis Strategy for Multiple Actuator Faults of Full-Vehicle Active Suspension Systems

Multi-Objective Finite-Frequency H ∞/GH 2 Static Output-Feedback Control Synthesis for Full-Vehicle Active Suspension Systems: A Metaheuristic Optimization Approach

In this paper, the problem of multi-objective control for active suspension systems with polytopic uncertainty is addressed via <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GH</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> static output feedback with a limited-frequency characteristic. For the overall analysis of the performance demanding both vehicle-ride comfort related to vertical- and transversal-directional dynamics and the time-domain constraints related to the driving maneuverability, a seven-degree-of-freedom full-vehicle model with an active suspension system is investigated. The robust static output-feedback control strategy is adopted because some state variables may not be directly measured in a realistic implementation. In designing this control, the finite-frequency H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance using the generalized Kalman—Yakubovich—Popov lemma is optimized to improve the passenger’s ride comfort, while the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GH</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> performance is optimized to guarantee the constraints concerning the suspension deflection limitation, road-holding ability, and actuator saturation problem. This control synthesis problem is formulated as nonconvex bilinear matrix inequalities and requires simultaneous consideration of different finite-frequency domain ranges for vertical and transversal motions for evaluating the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance. These design difficulties are overcome by the proposed multi-objective quantum-behaved particle swarm optimizer, which efficiently explores the relevant trade-offs between the considered multiple performance objectives and eventually provides the desired Pareto-optimal control set. Further, the numerical simulation cases of a full-vehicle active suspension system are presented to illustrate the effectiveness of the proposed control synthesis methodology in frequency and time domains.

A reinforcement learning backstepping‐based control design for a full vehicle active Macpherson suspension system

This paper presents a reinforcement learning backstepping-based control scheme for the design of a full vehicle active suspension system such that both the superior ride comfort and stabilization are achieved. It is well known that the traditional backstepping control scheme is suitable to solve higher order nonlinear system based on the strict-feedback form; however, the disadvantage of the procedure requires computing the derivatives of virtual control signals, which is a very complex task. Therefore, a reinforcement learning scheme using the deep deterministic policy gradient (DDPG) control strategy is developed to replace the process of finding virtual control force in backstepping method. It not only avoids the complexity of analytically calculating the derivatives of the virtual control signals, but also retains the systems robustness when there exist random disturbances from road irregularities. To verify the performance of the proposed active suspension control scheme, the random road unevenness profiles according to ISO 8608 is considered as vertical and lateral disturbance input excitation of a full-vehicle suspension system. Compared with conventional passive suspension system and backstepping control scheme, the proposed approach can be demonstrated to not only effectively improve ride comfort under random road excitation, but also improve the transient response and robustness.

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.

An ABC Optimized Adaptive Fuzzy Sliding Mode Control Strategy for Full Vehicle Active Suspension System

This work presents a Fuzzy based adaptive Sliding Mode Control scheme to deal with the control problem of full vehicle active suspension system and take into consideration the nonlinearities of the spring and damper, unmodeled dynamics as well as external disturbances. The control law of fuzzy-based Adaptive Sliding Mode Control scheme will update the parameters of fuzzy sliding mode control by using the stability analysis of Lyapunov criteria such that the convergence infinite time and the stability of the closed-loop is ensured. The proposed control scheme consists of four similar subsystems used for the four sides of the vehicle. The sub-control scheme contains two loops, the outer loop is built using a sliding mode controller with a fuzzy estimator to approximate and estimate the unknown parameters in the system. In the inner loop, a controller of type Fractional Order PID (FOPID) is utilized to create the required actuator force. All parameters in the four sub-control schemes are optimized utilizing Artificial Bee Colony (ABC) algorithm in order to improve the performance. The results indicate the effectiveness and good achievement of the proposed controller in providing the best ability to limit the vibration with good robustness properties in comparison with passive suspension system and using sliding mode control method. The controlled suspension system shows excellent results when it was tested with and without typical breaking and bending torques.

Neuro-adaptive optimized control for full active suspension systems with full state constraints

In this paper, an adaptive neural network (NN) optimized control strategy is presented to improve the inherent tradeoff between ride comfort of passengers and the suspension travel for full vehicle active suspension system with state constraints. To the best of our knowledge, the automotive suspension system control using hydraulic actuators is a highly complex nonlinear control task, involving external disturbances and uncertainties. To address it, this paper develops the virtual and actual optimal controllers based on backstepping technique and the identifier-actor-critic structure. Meanwhile, the Barrier Lyapunov functions are developed to prevent the systems from the state constraints and the systems states are limited in the preselected compact sets. It is particularly worth mentioning that the proposed optimal control strategy ensures that all the closed-loop signals remain bounded, while the power of the control input as well as the amplitude of the vertical displacement has been minimized. The simulation results show how the full-car system can be controlled optimally and satisfactorily, and confirm the superiority of proposed method.

Regeneration Energy for Nonlinear Active Suspension System Using Electromagnetic Actuator

The main purpose of using the suspension system in vehicles is to prevent the road disturbance from being transmitted to the passengers. Therefore, a precise controller should be designed to improve the performances of suspension system. This paper presents a modeling and control of the nonlinear full vehicle active suspension system with passenger seat utilizing Fuzzy Model Reference Learning Control (FMRLC) technique. The components of the suspension system are: damper, spring and actuator, all of those components have nonlinear behavior, so that, nonlinear forces that are generated by those components should be taken into account when designed the control system. The designed controller consumes high power so that when the control system is used, the vehicle will consume high amount of fuel. It notes that, when vehicle is driven on a rough road; there will be a shock between the sprung mass and the unsprung mass. This mechanical power dissipates and converts into heat power by a damper. In this paper, the wasted power has reclaimed in a proper way by using electromagnetic actuator. The electromagnetic actuator converts the mechanical power into electrical power which can be used to drive the control system. Therefore, overall power consumption demand for the vehicle can be reduced. When the electromagnetic actuator is used three main advantages can be obtained: firstly, fuel consumption by the vehicle is decreased, secondly, the harmful emission is decreases, therefore, our environment is protected, and thirdly, the performance of the suspension system is improved as shown in the obtained results.

Ride Comfort-Road Holding Trade-off Improvement of Full Vehicle Active Suspension System by Interval Type-2 Fuzzy Control

This paper presents a hybrid fuzzy controller structure to improve ride comfort-road holding trade-off characteristics of a full vehicle active suspension system. The controller includes two interval type-2 fuzzy logic controllers, optimized for ride comfort and road holding, for each tire. Type C random road profiles according to ISO 8608 are applied to each tire in the model and ride index, road holding, crest factor, and RMS acceleration values are evaluated. Stability of the overall closed loop system is presented by phase plane analysis. The simulation results reveal that simultaneous improvement of ride index and road holding can be made possible with the proposed controller.

Design of Fuzzy Super Twisting Sliding Mode Control Scheme for Unknown Full Vehicle Active Suspension Systems Using an Artificial Bee Colony Optimization Algorithm

AbstractThis article proposes a new intelligent control scheme that uses the Fuzzy Super Twisting Sliding Mode Concept (FSTSMC) and PID controller tuned with the Artificial Bee Colony (ABC) algorithm to control a full vehicle active suspension system with new convergence proof. Suspension systems are utilized to provide vehicle safety and improve comfortable driving. The effects of road roughness transmitted by tires to the vehicle body can be reduced by using suspension systems. In this work super twisting sliding mode is combined with a fuzzy system to design a robust control method. The super twisting sliding mode concept is utilized to limit and minimize the chattering problem and the fuzzy system is used for estimating the unknown parameters and uncertainty in the suspension system components (spring, damper, and actuator). The advantage of such combination is that it can handle the uncertainties and nonlinearities efficiently. The PID controller is used to create the required force to be produced by the actuator. The proposed control scheme consists of four similar sub control schemes, one for each of the four corners of the vehicle. All parameters in the sub control schemes are optimized using the ABC algorithm. The designed control system is applied for a full vehicle model with 8 degrees of freedom. Simulation results show large reduction in the vibration of the vehicle body when passing on disturbance and also show good robustness properties when tested for different road conditions. Passive and active suspension systems using sliding modecontrol (SMC) and the proposed FSTSMC are compared to test the efficiency and the ability of the proposed control scheme to achieve safe and comfortable driving for a random road profile.

Active Suspension System Control With Decentralized Event-Triggered Scheme

This article concerns the problem of improving ride comfort of a full-vehicle active suspension system through an observer-based decentralized event-triggered H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controller. The observer-based controller is adopted to cope with the situation that states information of vehicles are not available. To reduce the observer-based controller computing frequency, a decentralized event-triggered data transmission scheme is provided, and lower bounds on the minimum interevent time are guaranteed. Based on the proposed controller, the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance of an active full-vehicle suspension system can be obtained. Finally, simulations in the bump and random road conditions are shown to demonstrate the effectiveness of the derived algorithm.

Simultaneous dynamic system estimation and optimal control of vehicle active suspension

ABSTRACTThis paper proposes a combined observer/controller method that estimates the states and simultaneously improves the ride comfort and stability of a full vehicle active suspension system using a single inertial measurement unit (IMU) in the presence of noise and centre of gravity uncertainties. The derived model is based on a channel-by-channel estimation technique using filtered white noise excitation signals on all the wheels, as well as active suspension actuators. The transfer functions of the vehicle system are estimated by analysing the IMU signals in the frequency domain. The derived result is then used in a linear quadratic regulator to calculate the actuators' forces to improve the vehicle's ride comfort and road holding stability within the limits of the rattle space. The simulation results show the efficacy of the proposed approach. Specifically, the observer estimates the actual behaviour of the vehicle with accuracy with up to uncertainty. Finally, a parametric study has been investigated to ascertain the applicability of the proposed approach in estimating the vehicle dynamics with simultaneously improving the ride comfort and road holding stability.

Terminal sliding mode control for full vehicle active suspension systems

In current study, a terminal sliding mode control approach different from the conventional sliding mode control is proposed for active suspension system, which has an ability to reach the sliding surface in a finite time to achieve a high control accuracy. A full vehicle active suspension model is adopted with consideration of system uncertainties. The terminal sliding mode controller (TSMC) is systematically designed to force motion trajectories of vehicle body to accurately track the ideal reference model, and the controller parameters are tuned by a novel kidney-inspired algorithm (KA) for better control performance. The thought of designing an adaptive scheme for the reference model is one of the main contribution of this work. Simulation results clearly show the strength of adaptive scheme. The effectiveness and the strong robustness in stabilizing the attitude of the vehicle and improving the ride comfort are the main positive features of the proposed TSMC.

A neurofuzzy controller for full vehicle active suspension systems

The main objectives of designing the controller for vehicle suspension systems are to reduce the discomfort sensed by passengers that arises from road roughness and to increase the road handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as many control objectives as possible. This paper deals with an artificial intelligent neurofuzzy (NF) technique to design a robust controller. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibration on each corner of the vehicle by supplying control forces to the suspension system when travelling on a rough road. The other purpose of using the NF controller for the vehicle model is to reduce the body inclinations that are made during intensive maneuvers, including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparison with an optimal proportional-integral-derivative (PID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.

Control and Sensor Fault Tolerance of Vehicle Active Suspension

In this paper, a full vehicle active suspension system is considered with the dynamics of the four force actuators. Being a large-scale and complex nonlinear system, it is physically decomposed into five interconnected subsystems. For each subsystem, a local controller is designed. Each control module is decomposed into functional submodules. In a higher level, a global module is designed to supervise all the submodules. In parallel to this hierarchical structure of control, a fault diagnosis structure is constructed. A local diagnosis module is designed for each subsystem to detect and isolate sensor faults. The local modules are monitored by a global module. Simulation is done to illustrate the system control, diagnosis, and fault tolerance.

Nonlinear Control of Full-Vehicle Active Suspensions with Backstepping Design Scheme

This paper proposes a nonlinear backstepping control strategy to improve the inherent tradeoff between ride comfort of passengers and suspension travel. Passenger comfort in ground vehicles usually depends on a combination of vertical motion (heave) and angular motion (pitch and roll). Suspension travel means the space variation between vehicle body and tires. Our active suspension design has the ability to cope with these two conflicting objectives for improving the tradeoff between them. The novelty is in use of the nonlinear filter whose effective bandwidth depends on the magnitudes of suspension travel. Hence, the nonlinear design allows the closed-loop system to behave differently in various operating regions. As a result, the excellent performance of full-vehicle active suspension is demonstrated in simulations compared to a standard passive suspension system.

LPV Model Based Gain-scheduling Controller for a Full Vehicle Active Suspension System

This article addresses the design of a gain-scheduling type nonlinear controller for a full-vehicle active suspension system. The proposed method is based on a Linear Parameter Varying (LPV) model of the system. In this model, the variations in suspension deflection and mass are chosen as the scheduling parameters. During the simulations, the full-vehicle system that is controlled by the proposed method is tested with different road profiles, having high and low bumps, hollows and combinations of the two. The simulation results demonstrate that the proposed method successfully maximizes the ride comfort when suspension deflection is far away from the structural limits and minimizes the suspension deflection by changing its behavior when the suspension limits are reached.

FULL VEHICLE ACTIVE SUSPENSION: SENSOR FAULT DIAGNOSIS AND FAULT TOLERANCE

In this paper, sensor fault detection, identification and fault tolerance model-based approach is designed for a linear full vehicle's active suspension system. The approach uses a bank of reduced order sliding mode observers to generate residuals. Residuals are defined in such a way to isolate the faulty sensor after detecting the occurrence of the fault. Once detected and isolated, the sensor fault is accommodated by replacing the faulty measurement by its estimation. In this study, sensor drift, bias and complete breakdown are considered. Simulation is made to illustrate the proposed strategy.