On the synthesis of driver inputs for the simulation of closed-loop handling manoeuvres

This paper concerns a new ‘Dual Model’ methodology for the synthesis of steering, throttle and braking inputs for the closed-loop simulation of linear or non-linear vehicle handling dynamics. The method provides near-optimal driver control inputs that are both insensitive to driver model assumptions, and feasible for use with complex non-linear vehicle handling models. The paper describes the Dual Model technique, and evaluates its effectiveness, in the context of a low-order non-linear handling model, via comparison with independently derived optimal control inputs. A test case of an obstacle avoidance manoeuvre is considered. The methodology is particularly applicable to the design and development of future chassis control systems

Simultaneous optimisation of vehicle parameter and control action to examine the validity of handling control assumptions

In this paper a general method is presented for optimising system parameters and inputs. The Generalised Optimal Control technique involves iterative resimulation of system states, but is applicable to any (smoothly) nonlinear system, and can be operated using non-quadratic cost functions. Here it is applied to find optimal steer and torque inputs for a 2DOF vehicle handling model with a (combined slip) nonlinear tyre model. System parameters for centre of gravity and yaw inertia are simultaneously optimised, and hence the validity of some handling control assumptions – particularly the benefits of zero sideslip – is examined. The results are satisfactory, and they are mainly in keeping with expectation. The method is proven to be effective, though computationally rather expensive

Comparison of optimal driving policies for limit handling manoeuvres

This paper concerns the synthesis of optimal control inputs for automotive handling dynamics, a typical application being in the evaluation of active safety systems operating near the limits of friction. The paper considers an example of an emergency limit handling manoeuvre – combined acceleration and steering to achieve obstacle avoidance whilst also maximising speed and maintaining stability. Two independent methods are applied to the problem. The first is a general numerical optimiser for nonlinear control systems (Generalised Optimal Control, or GOC). The second is an indirect Dual Model (DM) method, which has the advantage that no differential analysis of the vehicle model is required, and it can therefore be applied directly to a wide range of complex multibody dynamic models. A relatively low-order handling model is actually used within this study, since this allows comparison between the two methods and an evaluation of the general usefulness of the DM approach in the future

A randomized integral error criterion for parametric identification of dynamic models of mechanical systems

This paper proposes a new approach to the identification of reduced order models for complex mechanical vibration systems. Parametric identification is commonly conducted by the regression of time-series data, but when this includes significant unmodelled modes, the error process has a high variance and autocorrelation. In such cases, optimization using least-squares methods can lead to excessive parameter bias. The proposed method takes advantage of the inherent boundedness of mechanical vibrations to design a new regression set with dramatically reduced error variance. The principle is first demonstrated using a simple two-mass simulation model, and from this a practicable approach is derived. Extensive investigation of the new randomized integral error criterion method is then conducted using the example of identification of a quarter-car suspension system. Simulation results are contrasted with those from comparable direct least-squares identifications. Several forms of the identification equations and error sources are used, and in all cases the new method has clear advantages, both in accuracy and consistency of the resulting identification model

Combined state and parameter estimation of vehicle handling dynamics

This paper considers an extended form of the wellknown Kalman filter observer, to reconstruct dynamic states from a small sensor set, but also to rapidly adapt selected parameters in the nonlinear dynamic model which lies at the heart of the observer. A generic procedure is described for constructing the extended Kalman filter in such a way that any combination of model parameters can be identified. The study is carried out in simulation, using two different vehicle dynamic models, one to act as the test vehicle, the other forming the nucleus of the observer. The assumption is that while in-vehicle testing is most desirable for proving many controller algorithms, here we need ‘true’ reference state information, to examine Kalman filter accuracy. A number of experiments are carried out to prove the system’s identification properties and also to compare its performance with a more conventional Kalman filter, based on a linear handling model. The results demonstrate high levels of performance and significant robustness to design parameters such as parameter adaptation speed and anticipated sensor noise. Most significantly, the observer also operates well and is capable of parameter adaptation when model and sensor covariance information is not available – usually a restricting factor in practical Kalman filter estimator design. The only significant caveat is that we are ‘buying’ excellent dynamic tracking from a small sensor set, at some computational expense

An application of multimedia in teaching mechanical vibrations

An application of multimedia in teaching mechanical vibration

Development of a Master of Science programme in Automotive Systems Engineering

Development of a Master of Science programme in Automotive Systems Engineerin

Influence of anti-dive and anti-squat geometry in combined vehicle bounce and pitch dynamics

The paper presents a six-degree-of-freedom (6-DOF) multi-body vehicle model, including realistic representation of suspension kinematics. The suspension system comprises anti-squat and anti-dive element. The vehicle model is employed to study the effect of these features upon combined bounce and pitch plane dynamics of the vehicle, when subjected to bump riding events. The investigations are concerned with a real vehicle and the numerical predictions show reasonable agreement with measurements obtained on an instrumented vehicle under the same manoeurves

A comparison of braking and differential control of road vehicle yaw-sideslip dynamics

Two actuation mechanisms are considered for the comparison of performance capabilities in improving the yaw–sideslip handling characteristics of a road vehicle. Yaw moments are generated either by the use of single-wheel braking or via driveline torque distribution using an overdriven active rear differential. For consistency, a fixed reference vehicle system is used, and the two controllers are synthesized via a single design methodology. Performance measures relate to both open-loop and closed-loop driving demands, and include both on-centre and limit handling manoeuvres

An agent-based traffic simulation framework to model intelligent virtual driver behaviour

This paper presents an agent-based traffic simulation framework that supports intelligent virtual driver behaviour. The framework exploits concepts used in Artificial Life (ALife), Artificial Intelligence (AI) and Agent technology to model the inherent unpredictability and autonomous behaviour of drivers within traffic simulation models. Each driver agent in our system contains knowledge and a decision-making mechanism, both of which are based on heuristics. This approach replaces some of the prescriptive nature of driving simulation models by allowing behaviours to emerge as a result of individual driver agent interactions. The framework also contributes to accident analysis by improving current limitations in which accident investigation methods concentrate on the events themselves, rather than pre-crash influences. Within this context, the framework provides an opportunity to increase the understanding of accident causation factors, to examine alternative traffic scenarios (what if analyses) and methodology to obtain quantitative estimates of accident risk. Current implementation results show that driver agents within the integrated simulation are able to perceive other drivers’ speeds and distances, avoid collisions, perform realistic vehicle following, and demonstrate emergent traffic flow. A major application area for this framework includes the evaluation of vehicle, highway and road user factors that precede a collision, or near misses