Active Vehicle Suspension Control Research Articles

Unknown System Dynamics Estimator for Active Vehicle Suspension Control Systems With Time-Varying Delay.

This article proposes a novel control method for vehicle active suspension systems in the presence of time-varying input delay and unknown nonlinearities. An unknown system dynamics estimator (USDE), which employs first-order low-pass filter operations and has only one tuning parameter, is constructed to deal with unknown nonlinearities. With this USDE, the widely used function approximators (e.g., neural networks and fuzzy-logic systems) are not needed, and the intermediate variables and observer used in the traditional estimators are not required. This estimator has a reduced computational burden, trivial parameter tuning and guaranteed convergence. Moreover, a predictor-based compensation strategy is developed to handle the time-varying input delay. Finally, we combine the suggested USDE and predictor to design a feedback controller to attenuate the vibrations of vehicle body and retain the required suspension performances. Theoretical analysis is carried out via the Lyapunov-Krasovkii functional to prove the stability of the closed-loop system. Simulation results based on professional vehicle simulation software Carsim are provided to show the efficiency of the proposed control scheme.

Active vehicle suspension control using road preview model predictive control and radial basis function networks

Active suspension systems in road vehicles are applied in order to mitigate the road-induced chassis vertical accelerations more effectively than standard passive suspensions, thus increasing comfort and handling. Such systems are greatly assisted by road preview schemes, consisting of special sensors usually based on laser scanners (e.g. LiDAR sensors), which detect road irregularities ahead of the vehicle and feed this information to a control system, designed to manipulate the active suspension accordingly. In this paper, a model predictive controller (MPC) with road preview incorporating radial basis function (RBF) models, is presented as a control scheme for a full car active suspension system. The employed RBF models can efficiently approximate the nonlinear behavior of the suspension system, thus improving performance over linear MPC methods. Special care is taken to alleviate the increased computational complexity entailed in the RBF models, in order to ensure that online implementation of the controller is feasible. The proposed scheme is evaluated on a detailed simulated full car model under various road excitation types, while making use of a realistic approach for incorporating LiDAR road scanner noise. Comparisons to a passive suspension system, as well as a standard MPC controller with a fully linear plant model, demonstrate the performance potential of using RBF prediction models in a road preview MPC context.

An Active Vehicle Suspension Control Approach with Electromagnetic and Hydraulic Actuators

An active vibration control approach from an online estimation perspective of unavailable feedback signals for a quarter-vehicle suspension system is introduced. The application of a new signal differentiation technique for the online estimation of disturbance trajectories due to irregular road surfaces and velocity state variables is described. It is assumed that position measurements are only available for active disturbance suppression control implementation. Real-time signal differentiation is independent of detailed mathematical models of specific dynamic systems and control force generation mechanisms. Active control forces can be supplied by electromagnetic or hydraulic actuators. Analytical and simulation results prove the effective and fast dynamic performance of the online signal estimation as well as a satisfactory active disturbance attenuation on a quarter-vehicle active suspension system.

Output feedback H ∞/GH 2 preview control of active vehicle suspensions: a comparison study of LQG preview

This study concerns with multi-objective H ∞/GH 2 preview control of active vehicle suspensions. This control scheme has two main aspects: first, it allows constrained outputs of the system to vary freely as long as they remain within their given bounds, in order that the best possible performance could be delivered. Secondly, the optimisation as well as constraint fulfilment is done for the worst-case road disturbances to cover all road types. To design a system to perform satisfactorily for a wide range of road irregularities, H ∞-norm is used wherever minimisation is required, and generalised H 2-norm is used to care for the constraints on suspension working space. Moreover, to ensure desired stability margins for the system, pole location constraints are considered in the design. The proposed approach is evaluated on a quarter-car model and compared with the state-of-the-art preview control algorithm in the literature, namely, Linear quadratic Gaussian preview. Simulation results demonstrate the effectiveness of the proposed approach.

State of the Art in Vehicle Active Suspension Adaptive Control Systems Based on Intelligent Methodologies

This paper reviews computational-intelligence-involved approaches in active vehicle suspension control systems with a focus on the problems raised in practical implementations by their nonlinear and uncertain properties. After a brief introduction on active suspension models, the paper explores the state of the art in fuzzy inference systems, neural networks, genetic algorithms, and their combination for suspension control issues. Discussions and comments are provided based on the reviewed simulation and experimental results. The paper is concluded with remarks and future directions.

Multi-objective preview control of active vehicle suspensions

This paper develops the LMI-based solution for the design of a discrete-time multi-objective preview controller, and considers it for an active vehicle suspension system. A quarter car model, which captures many features of real structures, is used in this study and hence the look-ahead preview control is considered. To provide ride comfort for a wide range of road irregularities, H∞ norm is used as a comfort measure, while generalized H2 is used to care for the constraints on suspension working space, tire deflection and actuator saturation. Moreover, to ensure desired stability margins for the feedback part of the system, pole location constraints are considered in the design. The effects of inclusion of preview information in control law on ride comfort, ride safety, working space and power requirements, for various road profiles, are examined. The results demonstrate the effectiveness of the preview-included multi-objective design to pure feedback scheme.

Formula omitted] control of active vehicle suspensions with actuator time delay

The paper deals with the H ∞ control problem for active vehicle suspension systems with actuator time delay. The time delay for the actuator is assumed as uncertain time-invariant but has a known constant bound. By suitably formulating the sprung mass acceleration, suspension deflection and tyre deflection as the optimization object and considering the actuator time delay, a delay-dependent memoryless state feedback H ∞ controller is designed in terms of the feasibility of certain delay-dependent matrix inequalities. A quarter-car model with active suspension system is considered in this paper and a numerical example is employed to illustrate the effectiveness of the proposed approach. It is confirmed by the simulations that the designed controller not only can achieve the optimal performance for active suspensions but also preserves the closed-loop stability in spite of the existence of the actuator time delay within allowable bound.

Predictive Time-Delay Control of Vehicle Suspensions

A predictive control methodology is introduced for active vehicle suspension control, taking into account the effect of time delay as an inherent characteristic of active systems in the absence of preview information. In this method, at each time instant, the control signal is calculated by minimizing a perfor mance index, based on the predicted response in a finite prediction horizon. The discrete predictive method ology, compared with the widely used linear quadratic regulator method, is easier to formulate and results in a discrete-time control algorithm suitable for digital control, and time delays can be more easily introduced in the formulation. In this paper, predictive control simulation results are compared with passive control re sults. It is shown that this method is capable of improving ride performance of the single-degree-of-freedom quarter-car model for a wide range of input frequencies in the presence of time delay, while in the meantime it maintains the maximum suspension travel below the maximum of passively controlled suspensions.

Intelligent Feedback Linearization for Active Vehicle Suspension Control

Effective control of ride quality and handling performance are challenges for active vehicle suspension systems, particularly for off-road applications. Off-road vehicles experience large suspension displacements, where the nonlinear kinematics and damping characteristics of suspension elements are significant. These nonlinearities tend to degrade the performance of active suspension systems, introducing harshness to the ride quality and reducing off-road mobility. Typical control strategies rely on linear, time-invariant models of the suspension dynamics. While these models are convenient, nominally accurate, and tractable due to the abundance of linear control techniques, they neglect the nonlinearities and time-varying dynamics present in real suspension systems. One approach to improving the effectiveness of active vehicle suspension systems, while preserving the benefits of linear control techniques, is to identify and cancel these nonlinearities using Feedback Linearization. In this paper the authors demonstrate an intelligent parameter estimation approach using structured artificial neural networks that continually “learns” the nonlinear parameter variations of a quarter-car suspension model. This estimation algorithm becomes the foundation for an Intelligent Feedback Linearization (IFL) controller for active vehicle suspensions. Results are presented for computer simulations, real-time experimental tests, and field evaluations using an off-road vehicle (a military HMMWV). Experimental results for a quarter-car test rig demonstrate 60% improvements in ride quality relative to baseline (non-adapting) control algorithms. Field trial results reveal 95% reductions in absorbed power and 65% reductions in peak sprung mass acceleration using this IFL approach versus conventional passive suspensions.

Observer-based wheelbase preview control of active vehicle suspensions

Optimal observer-based wheelbase preview regulator problem is investigated for active vehicle suspension systems. It is shown that the problem reduces to the classical linear quadratic Gaussian problem, whose solution is well defined, by augmenting dynamics of system and road inputs. The resulting optimal controller is in the form of augmented state feedback controller and this augmented state is estimated by Kalman-Bucy filter using dynamics of the augmented system. Numerical examples of a half car model are given to verify the performance improvement achievable with the proposed controller.

Actuator and sensor selection for an active vehicle suspension aimed at robust performance

A recently presented method for actuator and sensor selection for linear control systems is applied and evaluated for an active vehicle suspension control problem. The aim is to eliminate the actuator/sensor combinations for which no controller exists that achieves a specified level of robust performance. Complete controller synthesis is avoided by using necessary conditions for robust performance. Due to this, it cannot be guaranteed that a stabilizing and robustly performing controller exists for the combinations satisfying the conditions. This is a major shortcoming of the method. Depending on the application and implementation, the computation time consumed by the selection procedure can be large.

Decentralized Control of Active Vehicle Suspensions With Preview

In this paper, decentralized control of active vehicle suspensions with preview of road irregularities is considered using a two-degree-of-freedom vehicle model. It is shown that by taking advantage of the separation between the eigenvalues of the slow subsystem representing the body mode, and the fast subsystem corresponding to the wheel mode, the design of the preview controller can be decoupled. Since decentralized preview controllers are synthesized independently for two single-degree-of-freedom systems, analytical solutions are obtained. The results of the analysis and simulations demonstrate that the performance of the system with the proposed controller is comparable to that of the optimal preview controller based on a fully interconnected system.

Application of Learning Automata to Controller Design in Slow-Active Automobile Suspensions

This study considers a new design methodology in the context of active vehicle suspension control. The approach combines concepts from Stochastic Optimal Control with those of Learning Automata. A learning automaton effectively learns optimal control on-line in the vehicle, in an appropriate stochastic 'test-track' environment. For practical application, the overwhelming advantage of this approach is that no explicit modelling is required, and considerable time savings may be expected in system development. This simulation study considers the on-line learning of optimal control in a low-bandwidth active suspension system, where control feedback is confined to a body-mounted accelerometer at each corner of the vehicle. It is shown that learning can successfully take place under a range of conditions, including the case when there is substantial transducer noise. The performance of the resulting control system is shown to depend heavily on the nature of the learning environment.