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 technique involving the construction of fitness functions, coding, initial population formation and reproduction are also presented.
