No description available.

No description available.

No description available.

No description available.

No description available.

This thesis considers the optimisation of vehicle suspension systems via a reinforcement learning technique The aim is to assess the potential of learning automata to learn 'optimum' control of suspension systems, which contain some active element under electronic control, without recourse to system models. Control optimisation tasks on full-active and senu-active suspension systems are used for the feasibility assessment and subsequent development of the learning automata technique. The quarter-vehicle simulation model, with ideal full-active suspension actuation, provides a well-known environment for initial studies applying classical discrete learning automata to learn the controller gains of a linear state-feedback controller. Learning automata are shown to be capable of acquiring near optimal controllers without any explicit knowledge of the suspension environment. However, the methodology has to be developed to allow safe on-line application. A moderator is introduced to prevent excessive suspension deviations as a result of possible unstable control actions applied during learning. A hardware trial is successfully implemented on a test vehicle fitted with semi-active suspension, excited by a hydraulic road simulation rig. During these initial studies some inherent weaknesses of the discrete automata are noted A discrete action set provides insufficient coverage of a continuous controller space so optima may be overlooked. Subsequent methods to increase the resolution of search lead to a forced convergence and hence an increased likelihood of local optima location. Th1s motivates the development of a new formulation of learning automaton, the CARLA, which exhibits a continuous action space and a reinforcement generalisation. The new method is compared w1th discrete automata on vanous stochastic function optimisatwn case stui1es, demonstrating that the new functionality of CARLA overcomes many of the identified shortcomings of discrete automata. Furthermore, CARLA shows a potential capability to learn in non-stationary environments. Repeatmg the earlier suspension tasks with CARLA applied, including an on-line hardware study, further demonstrates a performance gain over discrete automata Finally, a complex multi-goal learning task is considered A dynamic roll-control strategy is formulated based on the senu-active suspension hardware of the test vehicle. The CARLA is applied to the free parameters of this strategy and is seen to successfully synthesise improved roll-control over passive suspension.

The work presented in this thesis is an investigation into vehicle bodyshell structural joints, with the aim of improving their stiffness. The bodyshell joint is defined to be an area or sub-structure containing the intersection of beam-type members, the behaviour of which may be defined by a matrix determined experimentally or analytically by using the finite element method. An actual bodyshell was tested on a suitably designed rig and the primary displacement modes affecting the steady state and vibration response of a bodyshell were identified and the relevant stiffness measured by using a special transducer. The joint rotational displacements were measured in these modes and the relative importance of the joints obtained. The joints were then modified by the addition of stiffening plates and the effect on the various stiffnesses noted. To assist the analyst, a similar study was performed on the effectiveness of the panels, such as roof, floor and rear quarter. A finite element beam model was established for the bodyshell and modified until a good approximation was achieved with respect to the experiments. Some practical modification of three selected joints cut from the bodyshell was done in order to improve their stiffness. A theoretical study of the influence of spot welding size and spacing on the stiffness of two plates was made. A finite element model of an actual body joint was established and the effect on stiffness of various modifications was observed.

No description available.

No description available.

A navigation and positioning system for an electric automatic guided vehicle has been designed and implemented on an industrial pallet truck. The system includes an optical sensor mounted on the vehicle, capable of recognizing special markers at a distance of 0.3m. Software implemented in a z-80 microprocessor controls the sensor, performs all data processing and contains the decision making processes necessary for the vehicle to navigate its way to its task location. A second microprocessor is used to control the vehicle's drive motors under instruction from the navigation unit, to accurately position the vehicle at its destination. The sensor reliably recognises markers at vehicle speeds up to 1ms- 1, and the system has been integrated into a multiprocessor controlled wire-guidance system and applied to a prototype vehicle.