Dominic Buchstaller

Robust Stability for Multiple Model Adaptive Control: Part I—The Framework

An axiomatic framework providing robust stability and performance bounds for a wide class of Estimation based Multiple Model Switched Adaptive Control (EMMSAC) algorithms is developed. The approach decouples development of both the atomic control designs and the estimation processes thus permitting the usage of standard controller design and optimization approaches for these components. The framework is shown to give tractable algorithms for MIMO LTI plants, and also for some classes of nonlinear systems (for example, an integrator with input saturation). The gain bounds obtained have the key feature that they are functions of the complexity of the underlying uncertainty as described by metric entropy measures. For certain important geometries, such as a compact parametric uncertainties, the gain bounds are independent of the number of plant models (above a certain threshold) which are utilized in the implementation. Design processes are described for achieving a suitable sampling of the plant uncertainty set to create a finite candidate plant model set (whose size is also determined by a metric entropy measure) which achieves a guaranteed robustness/performance.

Robust stability and performance analysis for multiple model adaptive controllers

For an Estimation Based Multiple Model Switched Adaptive Control (EMMSAC) algorithm controlling a MIMO minimal LTI plant, lp, 1 ≤ p ≤ ∞ bounds on the gain from the input and output disturbances to the internal signals are obtained which are invariant to the number of models in the plant model set. For a compact uncertainty set it is shown that a realisable EMMSAC algorithm achieves robust stability for any plant within the uncertainty set.

Scaling of gain bounds for switched adaptive control with large uncertainties

A wide class of MMAC with a finite lp (1 les p les infin) closed loop gain are shown to have an unboundedly increasing lp closed loop gain for a simple set of plants under increasing parametric uncertainty. A modification is proposed which achieves a quadratic closed loop gain function which is independent of the size of the uncertainty set.

Robust Stability for Multiple Model Adaptive Control: Part II—Gain Bounds

The axiomatic development of a wide class of Estimation based Multiple Model Switched Adaptive Control (EMMSAC) algorithms considered in the first part of this two part contribution forms the basis for the proof of the gain bounds given in this paper. The bounds are determined in terms of a cover of the uncertainty set, and in particular, in many instances, are independent of the number of candidate plant models under consideration. The full interpretation, implications and usage of these bounds within design synthesis are discussed in part I. Here in part II, key features of the bounds are also discussed and a simulation example is considered. It is shown that a dynamic EMMSAC design can be universal and hence non-conservative and hence outperforms static EMMSAC and other conservative designs. A wide range of possible dynamic algorithms are outlined, motivated by both performance and implementation considerations.

Gain bounds for multiple model switched adaptive control of general MIMO LTI systems

For the class of MIMO minimal LTI systems controlled by an estimation based multiple model switched adaptive controller (EMMSAC), bounds are obtained for the closed loop lp gain, 1 ¿ p ¿ ¿, from the input and output disturbances to the internal signals.

Multiple model based fault localization for HVDC transmission systems: Robustness and real-world performance

This paper discusses the robustness properties of a novel, model-based HVDC fault localization algorithm. The approach utilizes a bank of Kalman filters to rank the fit of a discrete set of line fault models to measured data. The best fitting model indicates the fault position and fault type. The robustness of the algorithm in the presence of disturbances and unmodeled dynamics is demonstrated using synthetic data from high fidelity simulations as well as field data from a real HVDC transmission system.

A multiple model approach to fault detection and localization in multi-terminal HVDC systems

This paper discusses a novel protection technique for DC line faults in multi-terminal HVDC (MTDC) systems. Hereby a bank of Kalman filters ranks the fit of a discrete set of line fault models to measured data. The best fitting model indicates the fault location and fault type. The suitability of the technique is discussed for fast on-line fault localization and exact off-line fault localization and fault identification. Experimental results are shown to validate the approach based on data from a high fidelity simulation of a 3-terminal HVDC system.

Sampling and Controlling Faster than the Computational Delay

Abstract For a sampled-data control system, the sampling period is chosen smaller than the computational delay, an approach we call intra-delay sampling. Utilising parallel computing architectures, it is shown that intra-delay sampling schemes are feasible and that they yield better performance than their slower sampling counterparts.