Stability Properties of the Adaptive Horizon Multi-Stage MPC

This paper presents an adaptive horizon multi-stage model-predictive control (MPC) algorithm. It establishes appropriate criteria for recursive feasibility and robust stability using the theory of input-to-state practical stability (ISpS). The proposed algorithm employs parametric nonlinear programming (NLP) sensitivity and terminal ingredients to determine the minimum stabilizing prediction horizon for all the scenarios considered in the subsequent iterations of the multi-stage MPC. This technique notably decreases the computational cost in nonlinear model-predictive control systems with uncertainty, as they involve solving large and complex optimization problems. The efficacy of the controller is illustrated using three numerical examples that illustrate a reduction in computational delay in multi-stage MPC.Comment: Accepted for publication in Elsevier's Journal of Process Contro

Application of fuzzy logic to power system stabilizer

Power systems stability is a complex problem which was first recognised in 1920 and has been widely investigated by power system engineers ever since. The first laboratory test on a practical power system was conducted in 1924, followed by the first field test in the following year. The models and method of analysis were relatively simple, with long fault clearing times (0.5 to 2.0 seconds). In 1930, network analysers (which were analogue simulators of the power system) were developed and this led to the improvement of stability analysis. In early 1950’s, they were used to analyse problems which required detailed models of the synchronous machine, excitation system and speed governor. In the mid 1950’s, the first digital computer program for power systems stability was developed. Since the 1960's most of the industry efforts and interests relating to system stability have been concentrated on transient stability. Power systems are designed and operated to criteria concerning transient stability (Kundur, 1994). There have been significant developments in equipment modelling and testing, for synchronous machines, excitation systems and loads. In addition, using high speed fault clearing, fast exciters and special stability aids have been used to improve the transient stability of power systems. The high speed exciters adversely affect the small signal stability associated with local plant mode of oscillations by introducing negative damping of the rotor angle oscillations. Such problems have been solved using power systems stabilisers (PSS). The incorporation of a power systems stabiliser (PSS) into the excitation controller is to improve the system’s performance where the system’s damping is low. At the same time, it can also combat the damping reductions introduced by an AVR (Hughes, 1991). The damping of the rotor angle oscillations can be improved by adding a supplementary signal to the excitation control system to produce a component of the electrical torque on the rotor in phase with speed variations (Larsen and Swann, 1981). Figure 5.1 shows the block diagram of a power system stabiliser added to the excitation control system. The rotor angle oscillations of a generator feeding power to a large inter-connected power system occur in the frequency range of 0.2 to 2 Hz. Different signals have been used as the input to the PSS including: the rotor speed deviation, the bus frequency, the electrical power deviation and the accelerating power (Padiyar, 1996). When a speed signal is employed as an input for the PSS, then a phase lead compensator is required to provide sufficient phase lead (Hughes, 1991). A transient gain or washout is normally used to remove any steady state offset in the speed signal. This filter acts as a high pass filter and is required to ensure that the stabilising signal (PSS output) does not affect the steady state regulation characteristics

Synthesis of Minimal Error Control Software

Software implementations of controllers for physical systems are at the core of many embedded systems. The design of controllers uses the theory of dynamical systems to construct a mathematical control law that ensures that the controlled system has certain properties, such as asymptotic convergence to an equilibrium point, while optimizing some performance criteria. However, owing to quantization errors arising from the use of fixed-point arithmetic, the implementation of this control law can only guarantee practical stability: under the actions of the implementation, the trajectories of the controlled system converge to a bounded set around the equilibrium point, and the size of the bounded set is proportional to the error in the implementation. The problem of verifying whether a controller implementation achieves practical stability for a given bounded set has been studied before. In this paper, we change the emphasis from verification to automatic synthesis. Using synthesis, the need for formal verification can be considerably reduced thereby reducing the design time as well as design cost of embedded control software. We give a methodology and a tool to synthesize embedded control software that is Pareto optimal w.r.t. both performance criteria and practical stability regions. Our technique is a combination of static analysis to estimate quantization errors for specific controller implementations and stochastic local search over the space of possible controllers using particle swarm optimization. The effectiveness of our technique is illustrated using examples of various standard control systems: in most examples, we achieve controllers with close LQR-LQG performance but with implementation errors, hence regions of practical stability, several times as small.Comment: 18 pages, 2 figure

Mathematical problems for complex networks

Copyright @ 2012 Zidong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article is made available through the Brunel Open Access Publishing Fund.Complex networks do exist in our lives. The brain is a neural network. The global economy is a network of national economies. Computer viruses routinely spread through the Internet. Food-webs, ecosystems, and metabolic pathways can be represented by networks. Energy is distributed through transportation networks in living organisms, man-made infrastructures, and other physical systems. Dynamic behaviors of complex networks, such as stability, periodic oscillation, bifurcation, or even chaos, are ubiquitous in the real world and often reconfigurable. Networks have been studied in the context of dynamical systems in a range of disciplines. However, until recently there has been relatively little work that treats dynamics as a function of network structure, where the states of both the nodes and the edges can change, and the topology of the network itself often evolves in time. Some major problems have not been fully investigated, such as the behavior of stability, synchronization and chaos control for complex networks, as well as their applications in, for example, communication and bioinformatics

On Mitigation of Side-Channel Attacks in 3D ICs: Decorrelating Thermal Patterns from Power and Activity

Various side-channel attacks (SCAs) on ICs have been successfully demonstrated and also mitigated to some degree. In the context of 3D ICs, however, prior art has mainly focused on efficient implementations of classical SCA countermeasures. That is, SCAs tailored for up-and-coming 3D ICs have been overlooked so far. In this paper, we conduct such a novel study and focus on one of the most accessible and critical side channels: thermal leakage of activity and power patterns. We address the thermal leakage in 3D ICs early on during floorplanning, along with tailored extensions for power and thermal management. Our key idea is to carefully exploit the specifics of material and structural properties in 3D ICs, thereby decorrelating the thermal behaviour from underlying power and activity patterns. Most importantly, we discuss powerful SCAs and demonstrate how our open-source tool helps to mitigate them.Comment: Published in Proc. Design Automation Conference, 201

CAutoCSD-evolutionary search and optimisation enabled computer automated control system design

This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of 'Computer-Aided Control System Design' (CACSD) to the novel 'Computer-Automated Control System Design' (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency-domains. Such performance-prioritised unification is aimed to relieve practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-committing to the adopted scheme. With the recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible, analytically and practically, and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, meets multiple objectives in designing an LTI controller for a non-minimum phase plant and offers a high-performing LTI controller network for a nonlinear chemical process

Closed-loop Identification of an Industrial Extrusion Process

This paper deals with the challenging problem of closed-loop identification for multivariable chemical processes and particularly the estimation of an open-loop plant model for a lab-scale industrial twin-screw extruder used in a powder coatings manufacturing line. The aim is to produce a low order efficient model in order to assist the scaling-up and the model-based control design of the manufacturing process. To achieve this goal, a two-stage indirect approach has been deployed which relies on the a-priori knowledge of the controller parameters in order to extract good estimates of the open-loop dynamics of the underlying process. As input excitation signals we have used multiple single variable step tests at various operating conditions (current industrial practice) carried out manually in order to generate the data-set which captures the dynamics of the extrusion process. In order to increase the efforts for obtaining a suitable plant model, we have employed various identification techniques, such as Prediction Error Methods (PEM) and Subspace Identification Methods (SIM) in order to generate candidate closed-loop models that fit to the original input-output process data. Then, a comparison of the estimated models was performed by means of the mean square error and data fitting criteria in order to select the model that best describes the dynamic behaviour of the extrusion process. Model validation based on closed-loop step responses also used as verification of the results

Gradient System Modelling of Matrix Converters with Input and Output Filters

Due to its complexity, the dynamics of matrix converters are usually neglected in controller design. However, increasing demands on reduced harmonic generation and higher bandwidths makes it necessary to study large-signal dynamics. A unified methodology that considers matrix converters, including input and output filters, as gradient systems is presented.