Active Suspension Control Research Articles

Extended State Observer-Based Adaptive Neural Networks Backstepping Control for Pneumatic Active Suspension with Prescribed Performance Constraint

Pneumatic actuator is one of the key technologies in the field of active suspension due to its low cost, cleanliness, and high power-to-weight ratio characteristics. However, the dynamic models and control strategies of the pneumatic suspension have not been well demonstrated because they are nonlinear systems. Besides, the vertical displacement stability of sprung mass is very important for ensuring ride comfort, but accurate control is still a challenging problem in the presence of parametric uncertainties. In this study, an adaptive neural networks backstepping scheme is designed for the stability control of the pneumatic suspension. Firstly, a mathematical model of the pneumatic system is studied to investigate the dynamic system behavior and to obtain the control algorithm. Secondly, an extended state observer is applied to estimate uncertain parameters, unmodeled dynamics, and external disturbances. Thirdly, unknown masses of various load passengers are approximated by using radial basis function neural networks (RBFNNs). To enhance the system stability, a proposed control with a prescribed performance function (PPF) is designed to guarantee the vertical displacement of the chassis. Adaptive backstepping control with PPF is developed to stabilize the perturbed system and guarantee tracking performance. Finally, the simulation examples for the pneumatic suspension are employed to investigate the effectiveness of the proposed method.

Design and implementation of passivity-based controller for active suspension system using port-Hamiltonian observer

The objective of this study is to design and implement an observer for quarter-car active suspension system in Port-Hamiltonian form. A novel state observer is designed for active suspension system modelled in port-Hamiltonian form to estimate the states in presence of road disturbances. The observer is designed considering suspension deflection alone as the output, which is an easily measurable output. Performance of the proposed observer is evaluated experimentally with road disturbance input mimicking a sudden bump and a continuously varying road input, and proven to be effective in minimising the error dynamics in presence of bounded unmodelled disturbances. To prove the effectiveness of the state-estimator, an Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) designed using the desired physical properties of the closed-loop system is implemented using the observer states. Experimental results of the controller implemented using the designed state observer show good improvement in the ride comfort, ride stability and suspension stroke of the active suspension system, which proves the effectiveness of the proposed port-Hamiltonian observer in terms of minimising the error dynamics.

Disturbance Observer and Tube-based Model Reference Adaptive Control for Active Suspension Systems with Non-ideal Actuators

Active suspension systems undertake a vital role in ensuring passengers’ ride comfort and vehicle vibration attenuation. This work proposes a tube-based model reference adaptive control (TMRAC) that identifies a correctional control component at each time instant for performance amplification of active suspension systems with non-ideal actuators. The TMRAC is augmented by a disturbance observer (DO) to compensate for the effects of dead-zone and hysteresis. Closed-loop stability analysis of the developed method is also investigated in the sense of Lyapunov theory. The developed control scheme's feasibility with regard to suspension safety, road handling, and ride comfort is assessed through simulations by using a class C road profile. Comparison results with DO-based conventional MRAC are also conducted. The obtained results demonstrate that the proposed DO-based control strategy provides a superior ride comfort quality and better road disturbance mitigation with less control effort than the conventional method.

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.

Integral-based event triggering actuator fault-tolerant control for an active suspension system under a networked communication scheme

This paper presents a research on the problem of enhancing ride safety and comfort during driving of a vehicle using an active suspension control system under a networked communication. An integral event-triggered condition is defined to reduce the network usage over time, a Dynamic Output Feedback Controller is designed under the <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> criteria and Lyapunov-Krasovskii functionals to guarantee the system stability, actuator faults are considered for the controller design. The control algorithm is solved in terms of Linear Matrix Inequalities. In order to prove a practical feasibility, control performance characteristics for vibration suppression are evaluated under various road conditions.

Simulation of an Active Suspension Control for Frequency Response from Road Vibrations Using H-Infinity Synthesis and Validation for Robust Operation Using Μ-Synthesis

In a general suspension system ride comfort and suspension performance are inversely related.Whenever the vibrations are damped in a periodic and slow manner, the ride comfort improves but the suspension performance degrades. This project presents a frequency-based controller for active suspension using the H-infinity algorithm. The implementation includes performing the analysis using a general quarter car model. This is done to reduce the complexity of the model and at the same time helps to carry out the analysis without any compromise. In the implementation, the quarter car model is converted into a state-space model to make further analysis easier. The next part includes the development of a robust controller so that the system response is acceptable even in the case of uncertain input. In the model developed, the output states to be monitored are taken as suspension deflection and body acceleration. The input state to the controller is taken as the actuator force input. Intent of the model development is to mitigate the general trade-off between ride comfort and suspension performance.

Nonlinear vehicle active suspension system control method based on the extended high gain observer

Nonlinear vehicle active suspension system control method based on the extended high gain observer

Nonlinear vehicle active suspension system control method based on the extended high gain observer

Nonlinear vehicle active suspension system control method based on the extended high gain observer

Vehicle Stability Analysis under Extreme Operating Conditions Based on LQR Control.

Under extreme working conditions such as high-speed driving on roads with a large road surface unevenness coefficient, turning on a road with a low road surface adhesion coefficient, and emergency acceleration and braking, a vehicle's stability deteriorates sharply and reduces ride comfort. There is extensive existing research on vehicle active suspension control, trajectory tracking, and control methods. However, most of these studies focus on conventional operating conditions, while vehicle stability analysis under extreme operating conditions is much less studied. In order to improve the stability of the whole vehicle under extreme operating conditions, this paper investigates the stability of a vehicle under extreme operating conditions based on linear quadratic regulator (LQR) control. First, a seven degrees of freedom (7-DOF) dynamics model of the whole vehicle is established based on the use of electromagnetic active suspension, and then an LQR controller of the electromagnetic active suspension is designed. A joint simulation platform incorporating MATLAB and CarSim was built, and the CarSim model is verified by real vehicle tests. Finally, the stability of the vehicle under four different ultimate operating conditions was analyzed. The simulation results show that the root mean square (RMS) values of body droop acceleration and pitch angle acceleration are improved by 57.48% and 28.81%, respectively, under high-speed driving conditions on Class C roads. Under the double-shift condition with a low adhesion coefficient, the RMS values of body droop acceleration, pitch acceleration, and roll angle acceleration are improved by 58.25%, 55.41%, and 31.39%, respectively. These results indicate that electromagnetic active suspension can significantly improve vehicle stability and reduce driving risk under extreme working conditions when combined with an LQR controller.

Force analysis and measurement of permanent magnet biased AMB and PMB in hybrid magnetically suspended flywheel

For the hybrid magnetically suspended flywheel (MSFW), the magnetic forces generated by the permanent magnet (PM) biased AMB could make the flywheel rotor suspends at the radial equilibrium position, and the magnetic forces of the permanent magnetic bearing (PMB) could suspend the flywheel rotor at the axial equilibrium position. Firstly, the force model of the PM-biased AMB is developed, the weak coupling of magnetic forces among the x axis and y axis is quantitatively analyzed, and then the active suspension control of the flywheel rotor is realized by the PM-biased AMB. Furthermore, the force and torque models of the PMB are established, and the force characteristic of PMB with a negative stiffness could push the flywheel rotor back to the balanced status. Finally, the measurement experiments are conducted to verify the force characteristics of the PM-biased AMB and the PMB, the active control performance and weak coupling of the hybrid MSFW are verified by measuring displacement deflections.

Active Disturbance Rejection Control of Voice Coil Motor Active Suspension Based on Displacement Feedback

Electromagnetic active suspension is an important technique used in vehicles to improve ride comfort and handling stability. To achieve satisfactory suspension performance, the active suspension control requires strong robustness against the vehicle uncertainties and external disturbances. The voice coil motor (VCM) is applied to the active suspension. A suspension model considering the actuator is established. An active suspension controller based on the active disturbance rejection control (ADRC) method is proposed. Through the test platform built, it is demonstrated that the ADRC of VCM active suspension has better performance than the PID. The results indicate that the overshoot and adjustment time of ADRC controller is reduced by 80% and 50%, respectively. The body vertical displacement of the ADRC is 50% smaller than PID.

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.

Coordinated Control of Active Suspension and DYC for Four-Wheel Independent Drive Electric Vehicles Based on Stability

Active suspension control and direct yaw-moment control (DYC) are widely used in the vehicle control field. To solve the coupling between those two controllers, a coordinated control of active suspension and DYC is proposed to further improve the vehicle roll and yaw stability. To enhance the adaptive ability of the active suspension, a proportional integral control optimized by the genetic fuzzy algorithm is introduced. DYC is proposed based on the sliding mode control. To restrain the chattering, the parameters of the sliding mode control is optimized by a genetic algorithm. Finally, a coordinated controller is presented based on the adaptive distribution of the anti-roll torque in the front and rear suspension. The simulation results show that the proposed active suspension and DYC can greatly improve the roll and yaw stability, respectively. The expected vehicle status can be well tracked. In addition, the coordinated control is compared by simply using two independent controllers under a different tire–road friction coefficient and different steering maneuver. The results show that the coordinated control has an even better performance under each working condition.

A Response-Type Road Anomaly Detection and Evaluation Method for Steady Driving of Automated Vehicles

Serious Road anomalies caused by bridge approach settlement, pavement rutting, etc., not only seriously affect traffic safety and user experience but also aggravate the damage of road structure. It is more relevant to automated vehicles (AVs) as they are currently not designed to measure the pavement roughness directly. Without prior-collected road anomalies information, AVs’ active suspension control system can only passively reduce the negative impact of road anomalies to a certain extent. This paper proposed a response-type road anomaly detection and evaluation method by collecting the vibration data from AVs. A mechanical estimation model for the height of anomaly (HoA) is constructed to evaluate the degree of road anomaly. Passenger’s comfort is evaluated by three featured indicators: maximal acceleration, weighted root-mean-square acceleration, and jerk. A full-car simulation model is programmed based on the Simulink platform to reveal the relationship among road anomalies, comfort, and speed, which helps design a steady driving velocity profile for AVs. The results show that the root-mean-square error of road anomalies estimation is about 0.63cm. AVs’ comfort can be improved significantly by employing the proposed steady driving strategies.

Event-Triggered Adaptive Finite-Time Control for Active Suspension Systems With Prescribed Performance

In this article, we address the event-triggered based adaptive finite-time control problem for active suspension systems (ASSs) over the resource-constrained controller area network (CAN), which is the most extensively employed in-vehicle communication network in automotive systems. The control aim is to develop an event-triggered algorithm to reduce the communication burden from the CAN and meanwhile improve the suspension performances. Specifically, a novel finite-time performance function is presented to guarantee that the tracking error is retained in a small region at any prespecified time. It is shown that under the proposed control framework, ride comfort, suspension space limitation, and handling stability are all ensured. Then, the established event-triggered mechanism based on the relative threshold method can avoid Zeno behavior. Finally, all the signals are bounded for the closed-loop ASSs. The usefulness of the presented adaptive control strategy is demonstrated through the simulation results.

Energy-Saving Robust Saturated Control for Active Suspension Systems via Employing Beneficial Nonlinearity and Disturbance

This article proposes a novel control framework for active suspension systems by purposely employing beneficial nonlinearity and a useful disturbance effect for control performance enhancement. To this aim, a novel amplitude-limited PD-SMC control scheme is established to ensure a stable performance-oriented tracking control of the overall closed-loop system. Importantly, different from most existing control methods, the designed tracking controller purposely employs beneficial nonlinear stiffness and damping of a novel bioinspired reference model and deliberately utilizes useful disturbance response on the active suspension system, so as to improve the convergence speed and reduce control energy cost simultaneously. The asymptotic stability is theoretically proved by a rigorous Lyapunov-based analysis. To the best of our knowledge, this is a unique control scheme for active suspension systems which can technically take several critical control practice issues into account with guaranteed excellent performance simultaneously, including energy savings, actuator saturation, unexpected disturbances, etc. The superior performance is well validated with a series of experiments, and carefully compared to several existing control methods. The results of this study would definitely present a unique insight and an alternative approach to active controller designs via exploiting beneficial nonlinear and disturbance effects for better control performance and lower energy cost simultaneously.

Design of passive suspension system to mimic fuzzy logic control active suspension system

BackgroundThis paper proposes a method for designing a passive suspension system that determines the optimal suspension settings while offering feasible performance near an active suspension system. A mathematical model of a nonlinear quarter car is developed and simulated for control and optimization in MATLAB/Simulink® environment. The input road condition is a Class C Road, and the vehicle moves at 80 kmph. Fuzzy logic control (FLC) action is used to accomplish active suspension system control. An approach for investigating optimal suspension settings based on the FLC control force is described here. The optimized passive suspension system is supposed to have the same suspension travel and velocity as an active suspension system. The least square technique is implemented to optimize the suspension parameters of the passive suspension system.ResultsThe initial passive suspension system, FLC active system, and optimized suspension system are simulated in MATLAB/Simulink® environment. It is observed that RMS acceleration for the FLC system is 0.5057 m/s2, which is reduced by 46% (passive suspension system has RMS acceleration of 0.9322 m/s2, which is uncomfortable). For optimized system, RMS acceleration is 0.6990 m/s2. It is observed that the optimized passive suspension system almost mimics the initial FLC active suspension system. For the optimized system, sprung mass acceleration and VDV are improved by 30% and 27%, respectively, compared to the initial passive system.ConclusionIt is observed that the optimized passive suspension system mimics the initial FLC system. Also, an optimized FLC system has improved health criterion-based results compared to other suspension systems.

Communication-Constrained Active Suspension Control for Networked In-Wheel Motor-Driven Electric Vehicles With Dynamic Dampers

This paper is concerned with the networked active suspension control problem for in-wheel motor-driven electric vehicles subject to communication constraints, including intermittent data transmissions, quantized output measurements and network-induced transmission delays. The central goal is to develop a resource-efficient communication and active suspension control co-design approach such that the limited in-vehicle network resources are occupied in an efficient manner, while simultaneously achieving the desired suspension system performance. For this purpose, a networked data transmission framework, incorporating a dynamic event-triggered communication mechanism and a logarithmic quantizer, is developed to accommodate the intermittent and digitalized packet-based transmissions over the in-vehicle controller area network channels. Specifically, the event triggering mechanism sporadically selects the sampled sensor data packets to be transmitted, which are then quantized before sending over the network channels. Then, formal stability and suspension performance analysis is carried out, and a co-design criterion on the existence of the desired triggering mechanism and event-triggered output feedback controller is further derived. Finally, the effectiveness and merits of the derived results are substantiated via several numerical case studies.

Road Preview Control for Active Suspension System

Road Preview Control for Active Suspension System

A new active suspension control scheme for vehicles considering steering stability

A new active suspension control scheme for vehicles considering steering stability