Hierarchical optimisation of the design parameters of a vehicle suspension system

This paper presents a design methodology for the mechanical systems entitled First Design. It is based on a hierarchical organisation of the design, taking into account the notion of robustness at an early phase of the project. The aim is to improve the quality of the system in order to make it robust, less sensitive to the variability of the external parameters and design parameters. We distinguish two main stages of the design cycle: one concerning functional parameters and another concerning physical parameters. The methodology is based on simplified models, on sensitivity analysis and on robust multi-objective optimisation. As an example, the methodology will be applied to the optimisation of vehicle suspension system design parameters. For each stage of the hierarchical design, adapted simplified models, sensitivity analyses and optimisation processes will be studied and applied to our vehicle suspension system.     

Hierarchical optimisation of the design parameters of a vehicle suspension system

This paper presents a design methodology for the mechanical systems entitled First Design. It is based on a hierarchical organisation of the design, taking into account the notion of robustness at an early phase of the project. The aim is to improve the quality of the system in order to make it robust, less sensitive to the variability of the external parameters and design parameters. We distinguish two main stages of the design cycle: one concerning functional parameters and another concerning physical parameters. The methodology is based on simplified models, on sensitivity analysis and on robust multi-objective optimisation. As an example, the methodology will be applied to the optimisation of vehicle suspension system design parameters. For each stage of the hierarchical design, adapted simplified models, sensitivity analyses and optimisation processes will be studied and applied to our vehicle suspension system.     

Wear/comfort Pareto optimisation of bogie suspension

Pareto optimisation of bogie suspension components is considered for a 50 degrees of freedom railway vehicle model to reduce wheel/rail contact wear and improve passenger ride comfort. Several operational scenarios including tracks with different curve radii ranging from very small radii up to straight tracks are considered for the analysis. In each case, the maximum admissible speed is applied to the vehicle. Design parameters are categorised into two levels and the wear/comfort Pareto optimisation is accordingly accomplished in a multistep manner to improve the computational efficiency. The genetic algorithm (GA) is employed to perform the multi-objective optimisation. Two suspension system configurations are considered, a symmetric and an asymmetric in which the primary or secondary suspension elements on the right- and left-hand sides of the vehicle are not the same. It is shown that the vehicle performance on curves can be significantly improved using the asymmetric suspension configuration. The Pareto-optimised values of the design parameters achieved here guarantee wear reduction and comfort improvement for railway vehicles and can also be utilised in developing the reference vehicle models for design of bogie active suspension systems.     

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.     

Pareto optimisation of railway bogie suspension damping to enhance safety and comfort

This paper presents the optimisation of damping characteristics in bogie suspensions using a multi-objective optimisation methodology. The damping is investigated and optimised in terms of the resulting performances of a railway vehicle with respect to safety, comfort and wear considerations. A complete multi-body system model describing the railway vehicle dynamics is implemented in commercial software Gensys and used in the optimisation. In complementary optimisation analyses, a reduced and linearised model describing the bogie system dynamics is also utilised. Pareto fronts with respect to safety, comfort and wear objectives are obtained, showing the trade-off behaviour between the objectives. Such trade-off curves are of importance, especially in the design of damping functional components. The results demonstrate that the developed methodology can successfully be used for multi-objective investigations of a railway vehicle within models of different levels of complexity. By introducing optimised passive damping elements in the bogie suspensions, both safety and comfort are improved. In particular, it is noted that the use of optimised passive damping elements can allow for higher train speeds. Finally, adaptive strategies for switching damping parameters with respect to different ride conditions are outlined and discussed.     

Robustness analysis of bogie suspension components Pareto optimised values

Bogie suspension system of high speed trains can significantly affect vehicle performance. Multiobjective optimisation problems are often formulated and solved to find the Pareto optimised values of the suspension components and improve cost efficiency in railway operations from different perspectives. Uncertainties in the design parameters of suspension system can negatively influence the dynamics behaviour of railway vehicles. In this regard, robustness analysis of a bogie dynamics response with respect to uncertainties in the suspension design parameters is considered. A one-car railway vehicle model with 50 degrees of freedom and wear/comfort Pareto optimised values of bogie suspension components is chosen for the analysis. Longitudinal and lateral primary stiffnesses, longitudinal and vertical secondary stiffnesses, as well as yaw damping are considered as five design parameters. The effects of parameter uncertainties on wear, ride comfort, track shift force, stability, and risk of derailment are studied by varying the design parameters around their respective Pareto optimised values according to a lognormal distribution with different coefficient of variations (COVs). The robustness analysis is carried out based on the maximum entropy concept. The multiplicative dimensional reduction method is utilised to simplify the calculation of fractional moments and improve the computational efficiency. The results showed that the dynamics response of the vehicle with wear/comfort Pareto optimised values of bogie suspension is robust against uncertainties in the design parameters and the probability of failure is small for parameter uncertainties with COV up to 0.1.     

Parameters optimisation of a vehicle suspension system using a particle swarm optimisation algorithm

The purpose of this paper is to determine the lumped suspension parameters that minimise a multi-objective function in a vehicle model under different standard PSD road profiles. This optimisation tries to meet the rms vertical acceleration weighted limits for human sensitivity curves from ISO 2631 [ISO-2631: guide for evaluation of human exposure to whole-body vibration. Europe; 1997] at the driver's seat, the road holding capability and the suspension working space. The vehicle is modelled in the frequency domain using eight degrees of freedom under a random road profile. The particle swarm optimisation and sequential quadratic programming algorithms are used to obtain the suspension optimal parameters in different road profile and vehicle velocity conditions. A sensitivity analysis is performed using the obtained results and, in Class G road profile, the seat damping has the major influence on the minimisation of the multi-objective function. The influence of vehicle parameters in vibration attenuation is analysed and it is concluded that the front suspension stiffness should be less stiff than the rear ones when the driver's seat relative position is located forward the centre of gravity of the car body. Graphs and tables for the behaviour of suspension parameters related to road classes, used algorithms and velocities are presented to illustrate the results. In Class A road profile it was possible to find optimal parameters within the boundaries of the design variables that resulted in acceptable values for the comfort, road holding and suspension working space.     

Optimisation of lateral car dynamics taking into account parameter uncertainties

Simulation studies on an active all-wheel-steering car show that disturbance of vehicle parameters have high influence on lateral car dynamics. This motivates the need of robust design against such parameter uncertainties. A specific parametrisation is established combining deterministic, velocity-dependent steering control parameters with partly uncertain, velocity-independent vehicle parameters for simultaneous use in a numerical optimisation process. Model-based objectives are formulated and summarised in a multi-objective optimisation problem where especially the lateral steady-state behaviour is improved by an adaption strategy based on measurable uncertainties. The normally distributed uncertainties are generated by optimal Latin hypercube sampling and a response surface based strategy helps to cut down time consuming model evaluations which offers the possibility to use a genetic optimisation algorithm. Optimisation results are discussed in different criterion spaces and the achieved improvements confirm the validity of the proposed procedure.     

Multi-objective control of a full-car model using linear-matrix-inequalities and fixed-order optimisation

This paper studies multi-objective control of a full-vehicle suspension excited by random road disturbances. The control problem is first formulated as a mixed ?₂/?_∞ synthesis problem and an output-feedback solution is obtained by using linear-matrix-inequalities. Next, the multi-objective control problem is re-formulated as a non-convex and non-smooth optimisation problem with controller order restricted to be less than the vehicle model order. For a range of orders, controllers are synthesised by using the HIFOO toolbox. The efficacy of the presented procedures are demonstrated by several design examples.     

Design and validation of a robust active trailer steering system for multi-trailer articulated heavy vehicles

ABSTRACT

Multi-trailer articulated heavy vehicles (MTAHVs) are increasingly used around the world due to their economic and environmental benefits. However, MTAHVs exhibit poor maneuverability and low lateral stability, which may lead to fatal traffic accidents. Given the safety risks, it is necessary to solve the steering and stability problems of MTAHVs before they are safely mass deployed on our roads. To this end, active trailer steering (ATS) based on the linear quadratic regulator (LQR) technique has been explored. The LQR-based ATS demonstrates improved maneuverability and enhanced lateral stability. In the ATS design, the vehicle and operating parameters are assumed constant. Thus, it is natural to question the robustness of the ATS in presence of vehicle and operating parameter uncertainties. To address the problem, this paper proposes a robust ATS system. The robust ATS controller is designed using a linear matrix inequality (LMI) based LQR method. In the design, both vehicle and steering actuator parameter uncertainties are considered; to enhance the robustness of the ATS, the weighting matrices of the proposed controller are optimized. The robust controller is applied to an A-Train Double, one type of MTAHV. The effectiveness of the robust ATS is demonstrated using numerical and hardware-in-the-loop real-time simulations.     

A robust optimization for the frequency and decoupling ratio of a powertrain mounting system based on interval analysis

This paper presents a robust optimization method to decrease the variations in the performance of the designed system caused by the unavoidable manufacturing, installation or measurement errors of the design variables. Generally, it is difficult and costly to determine statistical information with sufficient precision for uncertain design variables; in this study, interval numbers are used to describe the uncertain design variables, and only the bounds of these variables are required. An improved interval truncation method is presented for estimating the variation ranges of the system performances. The robustness estimations of the system performances are incorporated into the optimization formulation to obtain the nominal design variables, which could make the system performances relatively robust; therefore, the design robustness is estimated and improved in the optimization iteration process. The robust optimization method is applied to a general powertrain mounting system (PMS) to improve the design robustness of the PMS decoupling layout and frequency allocation. The optimization results show that the robust optimization method could effectively increase the decoupling ratios in the interested vertical and pitch directions, and the frequency allocation is more robust than that obtained using the traditional deterministic optimization.     

Optimal preview game theory approach to vehicle stability controller design

Dynamic game theory brings together different features that are keys to many situations in control design: optimisation behaviour, the presence of multiple agents/players, enduring consequences of decisions and robustness with respect to variability in the environment, etc. In the presented methodology, vehicle stability is represented by a cooperative dynamic/difference game such that its two agents (players), namely the driver and the direct yaw controller (DYC), are working together to provide more stability to the vehicle system. While the driver provides the steering wheel control, the DYC control algorithm is obtained by the Nash game theory to ensure optimal performance as well as robustness to disturbances. The common two-degrees-of-freedom vehicle-handling performance model is put into discrete form to develop the game equations of motion. To evaluate the developed control algorithm, CarSim with its built-in nonlinear vehicle model along with the Pacejka tire model is used. The control algorithm is evaluated for a lane change manoeuvre, and the optimal set of steering angle and corrective yaw moment is calculated and fed to the test vehicle. Simulation results show that the optimal preview control algorithm can significantly reduce lateral velocity, yaw rate, and roll angle, which all contribute to enhancing vehicle stability.     

Optimisation of active suspension control inputs for improved performance of active safety systems

A collocation-type control variable optimisation method is used to investigate the extent to which the fully active suspension (FAS) can be applied to improve the vehicle electronic stability control (ESC) performance and reduce the braking distance. First, the optimisation approach is applied to the scenario of vehicle stabilisation during the sine-with-dwell manoeuvre. The results are used to provide insights into different FAS control mechanisms for vehicle performance improvements related to responsiveness and yaw rate error reduction indices. The FAS control performance is compared to performances of the standard ESC system, optimal active brake system and combined FAS and ESC configuration. Second, the optimisation approach is employed to the task of FAS-based braking distance reduction for straight-line vehicle motion. Here, the scenarios of uniform and longitudinally or laterally non-uniform tyre–road friction coefficient are considered. The influences of limited anti-lock braking system (ABS) actuator bandwidth and limit-cycle ABS behaviour are also analysed. The optimisation results indicate that the FAS can provide competitive stabilisation performance and improved agility when compared to the ESC system, and that it can reduce the braking distance by up to 5% for distinctively non-uniform friction conditions.     

Robust control for 4WS vehicles considering a varying tire-road friction coefficient

A µ-synthesis for four-wheel steering (4WS) problems is proposed. Applying this method, model uncertainties can be taken into consideration, and a µ-synthesis robust controller is designed with optimized weighting functions to attenuate the external disturbances. In addition, an optimal controller is designed using the well-known optimal control theory. Two different versions of control laws are considered here. In evaluations of vehicle performance with the robust controller, the proposed controller performs adequately with different maneuvers (i.e., J-turn and Fishhook) and on different road conditions (i.e., icy, wet, and dry). The numerical simulation shows that the designed µ-synthesis robust controller can improve the performance of a closed-loop 4WS vehicle, and this controller has good maneuverability, sufficiently robust stability, and good performance robustness against serious disturbances.     

Robust control and actuator dynamics compensation for railway vehicles

A robust controller is designed for active steering of a high speed train bogie with solid axle wheel sets to reduce track irregularity effects on the vehicle’s dynamics and improve stability and curving performance. A half-car railway vehicle model with seven degrees of freedom equipped with practical accelerometers and angular velocity sensors is considered for the H_∞ control design. The controller is robust against the wheel/rail contact parameter variations. Field measurement data are used as the track irregularities in simulations. The control force is applied to the vehicle model via ball-screw electromechanical actuators. To compensate the actuator dynamics, the time delay is identified online and is used in a second-order polynomial extrapolation carried out to predict and modify the control command to the actuator. The performance of the proposed controller and actuator dynamics compensation technique are examined on a one-car railway vehicle model with realistic structural parameters and nonlinear wheel and rail profiles. The results showed that for the case of nonlinear wheel and rail profiles significant improvements in the active control performance can be achieved using the proposed compensation technique.     

Application of Analytic Hierarchy Process to Stochastic Robustness Synthesis of Integrated Vehicle Controllers

This paper deals with the robust design procedure of integrated vehicle dynamics controller based on Stochastic Robustness Synthesis with use of a rational decision making process of the controller parameters. The basic control structure that integrates four-wheel steering and four-wheel torque control is determined using a nonlinear predictive control theory. The Analytic Hierarchy Process, a basic approach to decision making, is applied to determine the weight coefficients of robustness evaluation function of the controller. The desired vehicle dynamic performance is described as four-layer hierarchy structure and the design priority is determined with respect to several design criteria. The proposed design process produced a control system with excellent stability and performance robustness to vehicle parameter variations.     

Application of Analytic Hierarchy Process to Stochastic Robustness Synthesis of Integrated Vehicle Controllers

This paper deals with the robust design procedure of integrated vehicle dynamics controller based on Stochastic Robustness Synthesis with use of a rational decision making process of the controller parameters. The basic control structure that integrates four-wheel steering and four-wheel torque control is determined using a nonlinear predictive control theory. The Analytic Hierarchy Process, a basic approach to decision making, is applied to determine the weight coefficients of robustness evaluation function of the controller. The desired vehicle dynamic performance is described as four-layer hierarchy structure and the design priority is determined with respect to several design criteria. The proposed design process produced a control system with excellent stability and performance robustness to vehicle parameter variations.     

Optimisation of active suspension control inputs for improved vehicle handling performance

Active suspension is commonly considered under the framework of vertical vehicle dynamics control aimed at improvements in ride comfort. This paper uses a collocation-type control variable optimisation tool to investigate to which extent the fully active suspension (FAS) application can be broaden to the task of vehicle handling/cornering control. The optimisation approach is firstly applied to solely FAS actuator configurations and three types of double lane-change manoeuvres. The obtained optimisation results are used to gain insights into different control mechanisms that are used by FAS to improve the handling performance in terms of path following error reduction. For the same manoeuvres the FAS performance is compared with the performance of different active steering and active differential actuators. The optimisation study is finally extended to combined FAS and active front- and/or rear-steering configurations to investigate if they can use their complementary control authorities (over the vertical and lateral vehicle dynamics, respectively) to further improve the handling performance.     

Two degrees of freedom PID multi-controllers to design a mathematical driver model: experimental validation and robustness tests

This paper proposes a mathematical driver model based on PID multi-controllers having two degrees of freedom. Each PID controller making up this model is synthesised by the Ziegler–Nichols oscillation method, using the linear time invariant models which are obtained around their nominal operating points. Different PID controllers are combined using nonlinear optimisation and the H _∞ constraint. To demonstrate its robustness, it was tested on two models: a linear parameter variant model and a nonlinear four-wheel model. It was also tested in situations of high dynamic demand. The driver model showed good performance, stability and trajectory tracking. The performance tests were carried out using experimental data acquired by a Laboratory Peugeot 307 developed by INRETS-MA. This driver model was developed for an application known as ‘Itinerary Rupture DIagnosis’ (DIARI), which aims to evaluate the physical limits of a vehicle negotiating a bend. DIARI requires a tool to determine the steering commands to be applied to a vehicle model, making extrapolations with respect to speed.     

Multiobjective optimisation of bogie suspension to boost speed on curves

To improve safety and maximum admissible speed on different operational scenarios, multiobjective optimisation of bogie suspension components of a one-car railway vehicle model is considered. The vehicle model has 50 degrees of freedom and is developed in multibody dynamics software SIMPACK. Track shift force, running stability, and risk of derailment are selected as safety objective functions. The improved maximum admissible speeds of the vehicle on curves are determined based on the track plane accelerations up to 1.5?m/s². To attenuate the number of design parameters for optimisation and improve the computational efficiency, a global sensitivity analysis is accomplished using the multiplicative dimensional reduction method (M-DRM). A multistep optimisation routine based on genetic algorithm (GA) and MATLAB/SIMPACK co-simulation is executed at three levels. The bogie conventional secondary and primary suspension components are chosen as the design parameters in the first two steps, respectively. In the last step semi-active suspension is in focus. The input electrical current to magnetorheological yaw dampers is optimised to guarantee an appropriate safety level. Semi-active controllers are also applied and the respective effects on bogie dynamics are explored. The safety Pareto optimised results are compared with those associated with in-service values. The global sensitivity analysis and multistep approach significantly reduced the number of design parameters and improved the computational efficiency of the optimisation. Furthermore, using the optimised values of design parameters give the possibility to run the vehicle up to 13% faster on curves while a satisfactory safety level is guaranteed. The results obtained can be used in Pareto optimisation and active bogie suspension design problems.