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This paper looks at the implementation of a Sliding Mode Observer (SMO) based Reconfiguration algorithm to deal with sensor faults within the context of navigation controllers for Autonomous Underwater Vehicle (AUV). In this paper the reconfigurability aspects are considered for the heading controller. Simulation responses are used to illustrate that the Sliding Mode Observer is able to give state information to the controller when there is a fault in the AUV’s sensor package. Comparisons are made between the Sliding Mode Controller with and without reconfigurability for a number of different sensor failures, e.g. bias errors in or the complete loss of the heading data, and the robustness of the Sliding Mode Observer is investigated through the introduction of disturbances into the system.

Artificial neural networks and genetic algorithms are often quoted in discussions about the contribution of biological systems thinking to engineering design. This paper reviews work on the neuromuscular system, a field in which biological systems thinking could make specific contributions to the development and design of automatic control systems for mechatronics and robotics applications. The paper suggests some specific areas in which a better understanding of this biological control system could be expected to contribute to control engineering design methods in the future. Particular emphasis is given to the nonlinear nature of elements within the neuromuscular system and to processes of neural signal processing, sensing and system adaptivity. Aspects of the biological system that are of particular significance for engineering control systems include sensor fusion, sensor redundancy and parallelism, together with advanced forms of signal processing for adaptive and learning control.

The optimisation of the PID controllers' gains for separate propulsion and heading control systems of CyberShip I, a scale model of an oil platform supply ship, using Genetic Algorithms is considered. During the initial design process both PID controllers have been manually tuned to improve their performance. However this tuning approach is a tedious and time consuming process. A solution to this problem is the use of optimisation techniques based on Genetic Algorithms to optimise the controllers' gain values. This investigation has been carried out through computer-generated simulations based on a non-linear hydrodynamic model of CyberShip I.

Questions of the quality of computer-based models and the formal processes of model testing, involving internal verification and external validation, are usually given only passing attention in engineering reports and in technical publications. However, such models frequently provide a basis for analysis methods, design calculations or real-time decision-making in complex engineering systems. This paper reviews techniques used for external validation of computer-based models and contrasts the somewhat casual approach which is usually adopted in this field with the more formal approaches to software testing and documentation recommended for large software projects. Both activities require intimate knowledge of the intended application, a systematic approach and considerable expertise and ingenuity in the design of tests. It is concluded that engineering degree courses dealing with modelling techniques and computer simulation should put more emphasis on model limitations, testing and validation.

The optimization of nonlinear controller parameters by Genetic Algorithm (GA) is explored in this paper. The type of nonlinear controller that is considered is derived from sliding mode control theory, which is known for its robust properties. The GA technique for optimization has developed from its foundation in Darwinian evolution into a powerful optimization algorithm that can be used for parametric design. Both sliding mode control theory and GA theory are presented. The use of an elite GA for sliding mode controller parameter optimization is illustrated by means of an example which involves the design of a heading controller for a scale model of a supply ship. A GA optimized controller solution is presented. The performance of this controller is illustrated through simulation studies and trials in a model ship water tank facility. These evaluations illustrate that both the sliding mode control law and GA technique are effective design methods for automated design for nonlinear controllers.

Model validation is an important but often neglected aspect of the engineering design process. In the control systems field the accuracy of mathematical models which represent the dynamics of the system being controlled can be of critical importance for design applications in which the overall performance specifications are demanding in terms both of accuracy and speed of response over a wide range of operating conditions. In the past many published reports of engineering design projects which have depended in some way upon complex mathematical or computer models have failed to address questions of model accuracy or to include discussion of the processes used to investigate model limitations. Important exceptions can be found in certain safety-critical areas such as aircraft flight control system design. This paper reviews some of the issues that can arise in the validation of models used for engineering design and makes a number of recommendations both about the processes which should be used in the development and application of mathematical models for engineering design and about the education and training of future engineers.