Generalised Riccati solution and pinning control of complex stochastic networks

This paper considers the global synchronisation of a stochastic version of coupled map lattices networks through an innovative stochastic adaptive linear quadratic pinning control methodology. In a stochastic network, each state receives only noisy measurement of its neighbours' states. For such networks we derive a generalised Riccati solution that quantifies and incorporates uncertainty of the forward dynamics and inverse controller in the derivation of the stochastic optimal control law. The generalised Riccati solution is derived using the Lyapunov approach. A probabilistic approximation type algorithm is employed to estimate the conditional distributions of the state and inverse controller from historical data and quantifying model uncertainties. The theoretical derivation is complemented by its validation on a set of representative examples

DOBC based Fully Probability Design for Stochastic System with the Multiplicative Noise

This paper proposes a Fully Probabilistic control framework for stochastic systems with multiplicative noise and external disturbance. The proposed framework consists of two main components, the disturbance observer based compensator to reject the modelled disturbance, and the Fully Probability Design (FPD) controller to achieve the regulation objective. The disturbance observer is developed based on Bayes' theory following a probabilistic framework. Compared with the conventional FPD, the new framework in this paper is extended to deal with multiplicative noise, and at the same time improve the performance of the control system by rejecting external disturbances. The convergence analysis of the estimation and control processes is also provided. Finally, a numerical example is given to illustrate the effectiveness of the proposed control method

Consensus, Control and Message Passing in Complex Control Systems

Real-world merging systems are characterised by several challenges and high level of complexities, such as stochasticity, nonlinearities, high dimensionality, and systems with coupling. The aim of this thesis is to address these inconveniences in order to develop robust control algorithms for such real engineered emergent systems. The study in this thesis considered the development of the fully probabilistic (FP) framework that addresses the main challenge of controlling real-world stochastic and uncertain systems.The probabilistic framework characterises the dynamics of the system to be controlled in terms of probability distributions which is a desirable approach to handle the stochasticity of dynamical systems. Non-linearity of real-world systems on the other hand, hinders the derivation of analytic control solutions, yielding expensive numerical computations. To address this problem, a transformation method has been introduced to the developed FP control framework which facilitated the derivation of an analytic solution despite the nonlinearity of the system dynamics. This method transformed the nonlinear state function to another variant where the nonlinearities are preserved but have now been transformed to a nonlinear affine state function. The inclusion of this novelty allows for the control of more realistic systems which tend to be nonlinear. Further advancement includes the extension of the developed nonlinear FP control method to control large-scale complex nonlinear systems. This is achieved by decomposing the complex system into small subsystems and then decentrally controlling each individual subsystem by a local controller. Probabilistic message passing is thereafter used to coordinate between the subsystems constituting the complex system, thus achieving the overall objective of the controlled complex system. This decentralised control framework has further been advanced to consider several control objectives, including regulation, tracking and formation control where the subsystems that constitute the overall network rely on the probabilistic message passing approach to interact with each other

Fully probabilistic control for uncertain nonlinear stochastic systems

This paper develops a novel probabilistic framework for stochastic nonlinear and uncertain control problems. The proposed framework exploits the Kullback–Leibler divergence to measure the divergence between the distribution of the closed-loop behavior of a dynamical system and a predefined ideal distribution. To facilitate the derivation of the analytic solution of the randomized controllers for nonlinear systems, transformation methods are applied such that the dynamics of the controlled system becomes affine in the state and control input. Additionally, knowledge of uncertainty is taken into consideration in the derivation of the randomized controller. The derived analytic solution of the randomized controller is shown to be obtained from a generalized state-dependent Riccati solution that takes into consideration the state-and control-dependent functional uncertainty of the controlled system. The pro-posed framework is demonstrated on an inverted pendulum on a cart problem, and the results are obtaine

Micro-combs: a novel generation of optical sources

The quest towards the integration of ultra-fast, high-precision optical clocks is reflected in the large number of high-impact papers on the topic published in the last few years. This interest has been catalysed by the impact that high-precision optical frequency combs (OFCs) have had on metrology and spectroscopy in the last decade [1–5]. OFCs are often referred to as optical rulers: their spectra consist of a precise sequence of discrete and equally-spaced spectral lines that represent precise marks in frequency. Their importance was recognised worldwide with the 2005 Nobel Prize being awarded to T.W. Hänsch and J. Hall for their breakthrough in OFC science [5]. They demonstrated that a coherent OFC source with a large spectrum – covering at least one octave – can be stabilised with a self-referenced approach, where the frequency and the phase do not vary and are completely determined by the source physical parameters. These fully stabilised OFCs solved the challenge of directly measuring optical frequencies and are now exploited as the most accurate time references available, ready to replace the current standard for time. Very recent advancements in the fabrication technology of optical micro-cavities [6] are contributing to the development of OFC sources. These efforts may open up the way to realise ultra-fast and stable optical clocks and pulsed sources with extremely high repetition-rates, in the form of compact and integrated devices. Indeed, the fabrication of high-quality factor (high-Q) micro-resonators, capable of dramatically amplifying the optical field, can be considered a photonics breakthrough that has boosted not only the scientific investigation of OFC sources [7–13] but also of optical sensors and compact light modulators [6,14]

Assessment and control of transition to turbulence in plane Couette flow

Transition to turbulence in shear flows is a puzzling problem regarding the motion of fluids flowing, for example, through the pipe (pipe flow), as in oil pipelines or blood vessels, or confined between two counter-moving walls (plane Couette flow). In this kind of flows, the initially laminar (ordered and layered) state of fluid motion is linearly stable, but turbulent (disordered and swirling) flows can also be observed if a suitable perturbation is imposed. This thesis concerns the assessment of transitional properties of such flows in the uncontrolled and controlled environments allowing for the quantitative comparisons of control strategies aimed at suppressing or trigerring transition to turbulence. Efficient finite-amplitude perturbations typically take the form of small patches of turbulence embedded in the laminar flow and called turbulent spots. Using direct numerical simulations, the nonlinear dynamics of turbulent spots, modelled as exact solutions, is investigated in the transitional regime of plane Couette flow and a detailed map of dynamics encompassing the main features found in transitional shear flows (self-sustained cycles, front propagation and spot splitting) is built. The map represents a quantitative assessment of transient dynamics of turbulent spots as a dependence of the relaminarisation time, i.e. the time it takes for a finite-amplitude perturbation, added to the laminar flow, to decay, on the Reynolds number and the width of a localised perturbation. By applying a simple passive control strategy, sinusoidal wall oscillations, the change in the spot dynamics with respect to the amplitude and frequency of the wall oscillations is assessed by the re-evaluation of the relaminarisation time for few selected localised initial conditions. Finally, a probabilistic protocol for the assessment of transition to turbulence and its control is suggested. The protocol is based on the calculation of the laminarisation probability, i.e. the probability that a random perturbation decays as a function of its energy. It is used to assess the robustness of the laminar flow to finite-amplitude perturbations in transitional plane Couette flow in a small computational domain in the absence of control and under the action of sinusoidal wall oscillations. The protocol is expected to be useful for a wide range of nonlinear systems exhibiting finite-amplitude instability

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal