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This thesis addresses the problem of real-time optimal control of aircraft systems using parallel processing techniques. It is shown that transputer hardware can be used in designing a suitable optimal controller for general nonlinear time-varying aircraft. In the first part of the thesis, nonlinearties and time varying aspects of the aircraft system, together with the current available solutions are investigated and suitable designs presented. Here the linear regulator approach for linear time-varying aircraft is investigated first but it is shown that real-time performance is difficult to achieve. The problem is then approached differently in that the aircraft is considered as a linear time-invariant system for short time intervals and it is then found possible to implement an optimal control solution in real-time, and suitable multi-transputer architectures are presented. The receding/moving horizon approach is applied to the aircraft system and is shown to be adequate for achieving satisfactory results. The problem of selection of the weights in the performance index of the optimal control problem is then studied and a design procedure is presented. The modeling of the aircraft as decoupled longitudinal and lateral dynamics is investigated and approached in such a way as to reduce the cross-coupling effects. Another important aspect of this research involves the consideration of failure detection and diagnosis in the aircraft hardware. Problems including actuator failure are studied and some remedial methods for handling the failures by enabling system reconfiguration after the occurrence of the failure are presented. The multi-processor based control system design is shown to offer a viable solution to solving complicated optimisation problems without the need for the simplification of the system dynamical equations and thereby loosing accuracy. Such simplification is usually a prerequisite for enabling practical designs. However with the use of parallel processing techniques such designs can be achieved for the more complicated (and more computationally demanding) cases as well.