Fuzzy and adaptive fuzzy control for the automobiles’ active suspension system

Two typical criteria for good vehicle suspension performance are their ability to provide good road handling and increased passenger comfort. The main disturbance affecting these two criteria is terrain irregularities. Active suspension control systems reduce these undesirable effects by isolating car body motion from vibrations at the wheels. This paper describes fuzzy and adaptive fuzzy control (AFC) schemes for the automobile active suspension system (ASS). The design objective is to provide smooth vertical motion so as to achieve the road holding and riding comfort over a wide range of road profiles. The efficacy of the proposed control schemes is demonstrated via simulations. With respect to the optimal linear quadratic regulator (LQR), it is shown that superior results have been achieved by the AFC.     

Optimal roll control of an articulated vehicle: theory and model validation

This paper investigates optimal roll control of an experimental articulated vehicle. The test vehicle and the mathematical model used to design the control strategies are presented. The vehicle model is validated against experimental data from the test vehicle in passive configuration. The initial controller design, performed by Sampson (Sampson, D.J.M. and Cebon, D., 2003a, Achievable roll stability of heavy road vehicles. Proc. Instn. Mech. Engrs, Part D, J. Automobile Engineering, 217(4), 269–287), is reviewed and adapted for the experimental vehicle. The effect of not controlling all the axles on the vehicle is investigated and a variable vehicle speed controller is designed by interpolating between constant speed controllers. Substantial reduction in normalized load transfer is achieved for a range of manoeuvres, both in steady-state and transient conditions.     

Fault tolerant nonlinear control with applications to an automated transit bus

This paper presents an integrated structure for a passive and active fault tolerant control (FTC) design approach in the framework of a robust nonlinear control technique called Dynamic Surface Control (DSC). As motivated by the automated vehicle application, we consider two categories of possible faults: pre-specified (a priori) and non-specified faults. It is first shown that DSC can be considered as a passive FTC approach in the sense that it gives simultaneous robust stability to a set of nonlinear systems even in the presence of model uncertainties and the pre-specified faults. Then, the non-specified fault is classified depending on the fault’s impact on the closed-loop system and isolatability from a fault detection and diagnosis (FDD) system. If a fault is both intolerable and isolatable, an active FTC approach is taken which includes FDD and controller reconfiguration. More specifically, trajectory reconfiguration is considered to accommodate the actuator fault, i.e., to compensate for the performance loss due to the fault within the framework of a switched hierarchical structure. Finally, the integrated structure for the longitudinal control of an automated transit bus is designed through the proposed method. Simulation results of the fault tolerant controller are shown for both single and multiple multiplicative faults. This controller was implemented on the California PATH transit buses in a demonstration of automated public transportation technology in San Diego, California in August of 2003.     

Heavy duty vehicle rollover detection and active roll control

This paper presents the results of a comprehensive study on heavy-duty vehicle (HDV) roll stability improvement technology. The proposed rollover threat warning system uses the real-time dynamic model-based time-to-rollover (TTR) metric as a basis for online rollover detections. Its feasibility for implementation in a HDV rollover threat detection system is demonstrated through vehicle dynamic simulation studies. The research on the development of a rollover threat detection system is further enhanced in combination with an active roll control system using active suspension mechanism to improve heavy-duty trucks’ roll stability both in the static cornering and in emergency maneuvers. It has been demonstrated that the roll stability of typical heavy-duty trucks has been largely improved by the proposed active safety monitoring and control system.     

A model-based traction control strategy non-reliant on wheel slip information

A traction control system (TCS) for two-wheel-drive vehicles can conveniently be realised by means of slip control. Such a TCS is modified in this paper in order to be applicable to four-wheel-drive vehicles and anti-lock braking systems, where slip information is not readily available. A reference vehicle model is used to estimate the vehicle velocity. The reference model is excited by a saw-tooth signal in order to adapt the slip for maximum tyre traction performance. The model-based TCS is made robust to vehicle modelling errors by extending it with (i) a superimposed loop of tyre static curve gradient control or (ii) a robust switching controller based on a bi-directional saw-tooth excitation signal. The proposed traction control strategies are verified by experiments and computer simulations.     

The IMMa optimisation algorithm without control input parameters

The IMMa optimisation algorithm (IOA) consists of a heuristic method based on a differential evolution algorithm for choosing the Magic Formula (MF) tyre model parameters. In a previous paper, we demonstrated that the IOA improved the searching procedure of optimum MF parameters with respect to the starting value optimisation (SVO) methods. But we had to introduce some control input parameters that were fixed during the running process. Now, the new version does not require control input variables to be chosen by the user. That is, we use an algorithm with self-adapting control parameters and it continues being easy to use, robust and fast. Hence, users do not need any kind of knowledge to use the IOA.     

Application of time-variant predictive control to modelling driver steering skill

The paper addresses the need for improved mathematical models of human steering control. A multiple-model structure for a driver's internal model of a nonlinear vehicle is proposed. The multiple-model structure potentially offers a straightforward way to represent a range of driver expertise. The internal model is combined with a model predictive steering controller. The controller generates a steering command through the minimisation of a cost function involving vehicle path error. A study of the controller performance during an aggressive, nonlinear steering manoeuvre is provided. Analysis of the controller performance reveals a reduction in the closed-loop controller bandwidth with increasing tyre saturation and fixed controller gains. A parameter study demonstrates that increasing the multiple-model density, increasing the weights on the path error, and increasing the controller knowledge range all improved the path following accuracy of the controller.     

A nonlinear virtual rider for motorcycles

This work presents a virtual rider for the guidance of a nonlinear motorcycle model. The target motion is defined in terms of roll angle and speed. The virtual rider inputs are the steering torque, the rear-wheel driving/braking torque and front-wheel braking torque. The virtual rider capability is assessed by guiding the nonlinear motorcycle model in demanding manoeuvres with roll angles of 50° and longitudinal accelerations up to 0.8 g. Considerations on the effective preview distance used by the virtual rider are included.     

Observer-based clutch disengagement control during gear shift process of automated manual transmission

A clutch disengagement strategy is proposed for the shift control of automated manual transmissions. The control strategy is based on a drive shaft torque observer. With the estimated drive shaft torque, the clutch can be disengaged as fast as possible without large driveline oscillations, which contributes to the reduction of total shift time and shift shock. The proposed control strategy is tested on a complete powertrain simulation model. It is verified that the system is robust to the variations of driving conditions, such as vehicle mass and road grade. It is also demonstrated that the revised system with switched gain can provide satisfactory performance even under large estimation error of the engine torque.     

Fault-tolerant control of heavy-haul trains

The fault-tolerant control (FTC) of heavy-haul trains is discussed on the basis of the speed regulation proposed in previous works. The fault modes of trains are assumed and the corresponding fault detection and isolation (FDI) are studied. The FDI of sensor faults is based on a geometric approach for residual generators. The FDI of a braking system is based on the observation of the steady-state speed. From the difference of the steady-state speeds between the fault system and the faultless system, one can get fault information. Simulation tests were conducted on the suitability of the FDIs and the redesigned speed regulators. It is shown that the proposed FTC does not explicitly worsen the performance of the speed regulator in the case of a faultless system, while it obviously improves the performance of the speed regulator in the case of a faulty system.