David J. Murray-Smith

Nonlinear model structure identification using genetic programming

Genetic Programming is an optimisation procedure which may be applied to the identification of the nonlinear structure of a dynamic model from experimental data. In such applications, the model structure may be described either by differential equations or by a block diagram and the algorithm is configured to minimise the sum of the squares of the error between the recorded experimental response from the real system and the corresponding simulation model output. The technique has been applied successfully to the modelling of a laboratory scale process involving a coupled water tank system and to the identification of a helicopter rotor speed controller and engine from flight test data. The resulting models provide useful physical insight.

Ship steering control system optimisation using genetic algorithms

The optimisation of non-linear control systems by genetic algorithm is studied in this paper. It involves the performance of two systems for regulating the motion of a ship model. These systems allow Course Changing and Track Keeping through the implementation of a sliding mode controller. The genetic algorithm is used to optimise the performance of the complete system under various operating conditions by optimising the parameters of the sliding mode controller. The type of vessel considered is an oil tanker. ( 2000 Elsevier Science ‚td. All rights reserved.

Genetic algorithm automated approach to the design of sliding mode control systems

Although various nonlinear control theories, such as sliding mode control, have proved sound and successful, there is a serious lack of effective or tractable design methodologies due to difficulties encountered in the application of traditional analytical and numerical methods. This paper develops a reusable computing paradigm based on genetic algorithms to transform the ‘unsolvable problem’ of optimal designs into a practically solvable ‘non-deterministic polynomial problem’, which results in computer automated designs directly from nonlinear plants. The design methodology takes into account practical system constraints and extends the solution space, allowing new control terms to be included in the controller structure. In addition, the practical implementations using laboratory-scale systems demonstrate that such ‘off-the-computer’ designs offer a superior performance to manual designs in terms of transient and steady-state responses and of robustness. Various contributions to the genetic algorithm te...

Real-Time Simulation

The design, development and testing of many engineering systems, such as aircraft and their flight-control systems, missiles, ship-propulsion systems, road vehicle suspension systems and certain kinds of chemical plant, can benefit, at certain stages of the process, from the use of computer simulation models which operate in the same timescale as the real system so that the execution time of the model is matched to the real system time. This is generally known as ‘real-time’ simulation. It is particularly important in applications in which the simulation may have to run with a human operator as part of a closed-loop system or in conjunction with real engineering hardware. Simulation may then be used to check interactions between individual items of equipment and to investigate the total system operation.

Optimisation of the weighting functions of an H ∞ controller using genetic algorithms and structured genetic algorithms

In this article, the optimisation of the weighting functions for an H ∞ controller using genetic algorithms and structured genetic algorithms is considered. The choice of the weighting functions is one of the key steps in the design of an H ∞ controller. The performance of the controller depends on these weighting functions since poorly chosen weighting functions will provide a poor controller. One approach that can solve this problem is the use of evolutionary techniques to tune the weighting parameters. The article presents the improved performance of structured genetic algorithms over conventional genetic algorithms and how this technique can assist with the identification of appropriate weighting functions’ orders.

Issues of numerical accuracy and stability in inverse simulation

Abstract Inverse simulation algorithms based on integration have been widely applied to predict the control input time histories required for aircraft to follow ideally defined manoeuvres. Several different inverse simulation algorithms are available but these different methods are all subject to a number of numerical and stability problems, such as high frequency oscillation effects and also lower frequency oscillatory phenomena termed “constraint oscillations”. Difficulties can also arise in applications involving discontinuous manoeuvres, discontinuities within the model or input constraints involving actuator saturation. This paper has shown that the dynamic response properties of the internal system are the cause of the so-called “constraint oscillation” phenomenon. In addition, a new inverse simulation approach based on the constrained derivative-free Nelder–Mead search-based optimisation method has been developed to eliminate problems of discontinuities and saturation. Simulation studies involving nonlinear ship models suggest that this new approach leads to improved properties in terms of convergence and numerical stability.

The optimization of a tanker autopilot control system using genetic algorithms

A genetic algorithm (GA) optimization application is presented in this paper. It involves the performance optimization of a navigation system for a nonlinear tanker model. This navigation system is fully autonomous and regulates the heading of the vessel with reference to a desired course made up of waypoints. The system consists of two components. The first is a line of sight (LOS) autopilot, which determines the desired heading of the tanker from positional information. The second is a heading control system that is derived from sliding mode (SM) control theory. In this investigation the GA is used to optimize the parameters for the SM controller so that the performance of the complete system satisfies specific design criteria. The resulting optimized navigation system is evaluated for different operating conditions for the tanker, e.g., different course, different forward velocities.

Performance based linear control system design by genetic evolution with simulated annealing

This paper develops a genetic algorithm based design automation method for linear control systems. It unifies the design and avoids the need for pre-selection of control schemes. Using this method, best performance is obtained for controllers described by a transfer function. The genetic algorithm encoded in decimal numerals is fine tuned by incorporating a simulated annealing technique for a more accurate search. It is shown that the design can be applied to both linear and nonlinear plants without manual calculations and can include practical constraints imposed upon the performance requirement. This method also allows the step of linearising nonlinear plants to be bypassed.

Sensitivity-analysis method for inverse simulation application

An important criticism of traditional methods of inverse simulation that are based on the Newton–Raphson algorithm is that they suffer from numerical problems. In this paper these problems are discussed and a new method based on sensitivity-analysis theory is developed and evaluated. The Jacobian matrix may be calculated by solving a sensitivity equation and this has advantages over the approximation methods that are usually applied when the derivatives of output variables with respect to inputs cannot be found analytically. The methodology also overcomes problems of input-output redundancy that arise in the traditional approaches to inverse simulation. The sensitivityanalysis approach makes full use of information within the time interval over which key quantities are compared, such as the difference between calculated values and the given ideal maneuver after each integration step. Applications to nonlinear HS125 aircraft and Lynx helicopter models show that, for this sensitivity-analysis method, more stable and accurate results are obtained than from use of the traditional Newton–Raphson approach.

Genetic Programming for the Automatic Design of Controllers for a Surface Ship

In this paper, the implementation of genetic programming (GP) to design a controller structure is assessed. GP is used to evolve control strategies that, given the current and desired state of the propulsion and heading dynamics of a supply ship as inputs, generate the commanded forces required to maneuver the ship. The controllers created using GP are evaluated through computer simulations and real maneuverability tests in a laboratory water basin facility. The robustness of each controller is analyzed through the simulation of environmental disturbances. In addition, GP runs in the presence of disturbances are carried out so that the different controllers obtained can be compared. The particular vessel used in this paper is a scale model of a supply ship called CyberShip II. The results obtained illustrate the benefits of using GP for the automatic design of propulsion and navigation controllers for surface ships.