Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.
Model Predictive Control (MPC) represents a major paradigm shift in the field of automatic control. This radically affects synthesis techniques (illustrated by control of an unstable system) and underlying concepts (illustrated by control of a multivariable system), as well as lifting the Control engineer's focus from prescriptions to specifications ("what" not "how", illustrated by emulation of a conventional autopilot). Part of the objective of this paper is to emphasise the significance of this paradigm shift. Another part is to consider the fact that this shift was missed for many years by the academic community, and what this tells us about teaching and research in the field.
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Systems with redundant control actuators are sometimes arranged so that a new actuator comes into play if the one normally used becomes saturated. We calI this 'daisy-chaining.' Such an arrangement also provides a degree of faulttolerance against actuator failures. This paper points out that a similar property is implicit in constrained predictive control, as it is usually formulated. Predictive control therefore has a degree of implicit fault-tolerance. In order to obtain this property the predictive control must have explicit constraints on the input levels (actuator positions) and the usual disturbance model, which results in integraI action arising in the controller. The general considerations dealt with in the paper are illustrated by a simplified example, based on the liquefaction of natural gas.
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