Implementation of a knowledge-based PID auto-tuner
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Discrete-time control systems (2nd ed.)
Discrete-time control systems (2nd ed.)
Computer-controlled systems (3rd ed.)
Computer-controlled systems (3rd ed.)
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Journal of Intelligent and Robotic Systems
Adaptive switching control of LTI MIMO systems using a family of controllers approach
Automatica (Journal of IFAC)
Brief Inference of candidate loop performance and data filtering for switching supervisory control
Automatica (Journal of IFAC)
Neural-network hybrid control for antilock braking systems
IEEE Transactions on Neural Networks
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A pole-placement based adaptive controller synthesised from a multiestimation scheme is designed for linear plants. A higher level switching structure between the various estimation schemes is used to supervise the reparameterisation of the adaptive controller in real time. The basic usefulness of the proposed scheme is to improve the transient response so that the closed-loop stability is guaranteed. The switching process is subject to a minimum dwelling or residence time within which the supervisor is not allowed to switch between the multiple estimation schemes. The high level supervision is based on the multiestimation identification scheme. The residence time condition guarantees the closed-loop stability. The above higher level switching structure is on-line supervised by a closed-loop tracking error based algorithm. This second supervision on-line tunes the free design parameters which appear as time varying weights in the loss function of the above switching structure. Thus, the closed-loop behaviour, compared to the constant parameter case one, is improved when the design parameter is not tightly initialised. Both supervisors are hierarchically organised in the sense that they act on the system at different rates. Furthermore, a projection algorithm has been considered in the estimation scheme in order to include a possible a priori knowledge of the estimates parameter vector value in the estimation algorithm.