Speculative Thread Framework for Transient Management and Bumpless Transfer in Reconfigurable Digital Filters

There are many methods developed to mitigate transients induced when abruptly changing dynamic algorithms such as those found in digital filters or controllers. These "bumpless transfer" methods have a computational burden to them and take time to implement, causing a delay in the desired switching time. This paper develops a method that automatically reconfigures the computational resources in order to implement a transient management method without any delay in switching times. The method spawns a speculative thread when it predicts if a switch in algorithms is imminent so that the calculations are done prior to the switch being made. The software framework is described and experimental results are shown for a switching between filters in a filter bank.Comment: 6 pages, 7 figures, to be presented at American Controls Conference 201

Bumpless Topology Transition

The topology transition problem of transmission networks is becoming increasingly crucial with topological flexibility more widely leveraged to promote high renewable penetration. This paper proposes a novel methodology to address this problem. Aiming at achieving a bumpless topology transition regarding both static and dynamic performance, this methodology utilizes various eligible control resources in transmission networks to cooperate with the optimization of line-switching sequence. Mathematically, a composite formulation is developed to efficiently yield bumpless transition schemes with AC feasibility and stability both ensured. With linearization of all non-convexities involved and tractable bumpiness metrics, a convex mixed-integer program firstly optimizes the line-switching sequence and partial control resources. Then, two nonlinear programs recover AC feasibility, and optimize the remaining control resources by minimizing the $\mathcal{H}_2$-norm of associated linearized systems, respectively. The final transition scheme is selected by accurate evaluation including stability verification using time-domain simulations. Finally, numerical studies demonstrate the effectiveness and superiority of the proposed methodology to achieve bumpless topology transition.Comment: Accepted by TPWR

Investigation of a hybrid switching control system

Bibliography: pages 84-86.A servo motor is to be used to position the cutting arm in a hypothetical pattern generation application. The motor is controlled in closed-loop in order to track, with zero asymptotic error, a reference signal represented by either a sinusoidal, triangular, or square wave. In addition, the schedule of reference signal type changes is not known a priori and the controlled system must achieve asymptotic tracking without operator intervention. As no simple single controller can satisfy these requirements for all setpoint types, a Hybrid Switching Control System is proposed which combines intuitive logic with standard control techniques. Under the guidance of a simple supervisor, the controller corresponding to each type of setpoint is switched in and out of the active feedback loop as required. A simple Multi-layer Perceptron neural network was selected to identify the type of signal being tracked and hence initiate controller switching. This network performed very well even in the presence of measurement noise, and the hybrid system automatically tracked each of the three types of reference signal over a wide range of signal amplitude and frequency. However, the reconfiguration interval was quite long (although still acceptable in terms of the proposed application), and the size of the neural net structure had to be limited for the system to work in real-time

Robust control examples applied to a wind turbine simulated model

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modeling and control become challenging problems. On the one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behavior. Therefore, the development of modeling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of the development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchmark model and the Monte Carlo tool represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modeling and control become challenging problems. On the one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behavior. Therefore, the development of modeling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of the development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchmark model and the Monte Carlo tool represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics

Superheat control for air conditioning and refrigeration systems: Simulation and experiments

Ever since the invention of air conditioning and refrigeration in the late nineteenth century, there has been tremendous interest in increasing system efficiency to reduce the impact these systems have on global energy consumption. Efficiency improvements have been accomplished through component design, refrigerant design, and most recently control system design. The emergence of the electronic expansion valve and variable speed drives has made immense impacts on the ability to regulate system parameters, resulting in important strides towards efficiency improvement. This research presents tools and methodologies for model development and controller design for air conditioning and refrigeration systems. In this thesis, control-oriented nonlinear dynamic models are developed and validated with test data collected from a fully instrumented experimental system. These models enable the design of advanced control configurations which supplement the performance of the commonly used proportional-integral-derivative (PID) controller. Evaporator superheat is a key parameter considered in this research since precise control optimizes evaporator efficiency while protecting the system from component damage. The controllers developed in this thesis ultimately provide better transient and steady state performance which increases system efficiency through low superheat set point design. The developed controllers also address the classical performance versus robustness tradeoff through design which preserves transients while prolonging the lifetime of the electronic expansion valve. Another notable contribution of this thesis is the development of hardware-in-the-loop load emulation which provides a method to test component and software control loop performance. This method alleviates the costs associated with the current method of testing using environmental test chambers

Fault tolerant longitudinal aircraft control using non-linear integral sliding mode

Copyright © 2014 Institution of Engineering and Technology (IET)This study proposes a novel non-linear fault tolerant scheme for longitudinal control of an aircraft system, comprising an integral sliding mode control allocation scheme and a backstepping structure. In fault free conditions, the closed loop system is governed by the backstepping controller and the integral sliding mode control allocation scheme only influences the performance if faults/failures occur in the primary control surfaces. In this situation, the allocation scheme redistributes the control signals to the secondary control surfaces and the scheme is able to tolerate total failures in the primary actuator. A backstepping scheme taken from the existing literature is designed for flight path angle tracking (based on the non-linear equations of motion) and this is used as the underlying baseline controller in nominal conditions. The efficacy of the scheme is demonstrated using a high-fidelity aircraft benchmark model. Excellent results are obtained in the presence of plant/model uncertainty in both fault free and faulty conditions

Advances in Youla-Kucera parametrization: A Review

International audienceYoula-Kucera (YK) parametrization was formulated decades ago for obtaining the set of controllers stabilizing a linear plant. This fundamental result of control theory has been used to develop theoretical tools solving many control problems ranging from stable controller switching, closed-loop identification, robust control, disturbance rejection, adaptive control to fault tolerant control.This paper collects the recent work and classifies the maccording to the use of YK parametrization, Dual YK parametrization or both, providing the latest advances with main applications indifferent control fields. A final discussion gives some insights on the future trends in the field

Heterogeneous and hybrid control with application in automotive systems

Control systems for automotive systems have acquired a new level of complexity. To fulfill the requirements of the controller specifications new technologies are needed. In many cases high performance and robust control cannot be provided by a simple conventional controller anymore. In this case hybrid combinations of local controllers, gain scheduled controllers and global stabilisation concepts are necessary. A considerable number of state-of-the-art automotive controllers (anti-lock brake system (ABS), electronic stabilising program (ESP)) already incorporate heterogeneous and hybrid control concepts as ad-hoc solutions. In this work a heterogeneous/hybrid control system is developed for a test vehicle in order to solve a clearly specified and relevant automotive control problem. The control system will be evaluated against a state-of-the-art conventional controller to clearly show the benefits and advantages arising from the novel approach. A multiple model-based observer/estimator for the estimation of parameters is developed to reset the parameter estimate in a conventional Lyapunov based nonlinear adaptive controller. The advantage of combining both approaches is that the performance of the controller with respect to disturbances can be improved considerably because a reduced controller gain will increase the robustness of the approach with respect to noise and unmodelled dynamics. Several alternative resetting criteria are developed based on a control Lyapunov function, such that resetting guarantees a decrease in the Lyapunov function. Since ABS systems have to operate on different possibly fast changing road surfaces the application of hybrid methodologies is apparent. Four different model based wheel slip controllers will be presented: two nonlinear approaches combined with parameter resetting, a simple linear controller that has been designed using the technique of simultaneously stabilising a set of linear plants as well as a sub-optimal linear quadratic (LQ)-controller. All wheel slip controllers operate as low level controllers in a modular structure that has been developed for the ABS problem. The controllers will be applied to a real Mercedes E-class passenger car. The vehicle is equipped with a brake-by-wire system and electromechanical brake actuators. Extensive real life tests show the benefits of the hybrid approaches in a fast changing environment

Towards a novel biologically-inspired cloud elasticity framework

With the widespread use of the Internet, the popularity of web applications has significantly increased. Such applications are subject to unpredictable workload conditions that vary from time to time. For example, an e-commerce website may face higher workloads than normal during festivals or promotional schemes. Such applications are critical and performance related issues, or service disruption can result in financial losses. Cloud computing with its attractive feature of dynamic resource provisioning (elasticity) is a perfect match to host such applications. The rapid growth in the usage of cloud computing model, as well as the rise in complexity of the web applications poses new challenges regarding the effective monitoring and management of the underlying cloud computational resources. This thesis investigates the state-of-the-art elastic methods including the models and techniques for the dynamic management and provisioning of cloud resources from a service provider perspective. An elastic controller is responsible to determine the optimal number of cloud resources, required at a particular time to achieve the desired performance demands. Researchers and practitioners have proposed many elastic controllers using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. There exist many issues that have not received much attention from a holistic point of view. Some of these issues include: 1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; 2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and 3) the lack of considering uncertainty aspects while designing auto-scaling solutions. This thesis seeks solutions to address these issues altogether using an integrated approach. Moreover, this thesis aims at the provision of qualitative elasticity rules. This thesis proposes a novel biologically-inspired switched feedback control methodology to address the horizontal elasticity problem. The switched methodology utilises multiple controllers simultaneously, whereas the selection of a suitable controller is realised using an intelligent switching mechanism. Each controller itself depicts a different elasticity policy that can be designed using the principles of fixed gain feedback controller approach. The switching mechanism is implemented using a fuzzy system that determines a suitable controller/- policy at runtime based on the current behaviour of the system. Furthermore, to improve the possibility of bumpless transitions and to avoid the oscillatory behaviour, which is a problem commonly associated with switching based control methodologies, this thesis proposes an alternative soft switching approach. This soft switching approach incorporates a biologically-inspired Basal Ganglia based computational model of action selection. In addition, this thesis formulates the problem of designing the membership functions of the switching mechanism as a multi-objective optimisation problem. The key purpose behind this formulation is to obtain the near optimal (or to fine tune) parameter settings for the membership functions of the fuzzy control system in the absence of domain experts’ knowledge. This problem is addressed by using two different techniques including the commonly used Genetic Algorithm and an alternative less known economic approach called the Taguchi method. Lastly, we identify seven different kinds of real workload patterns, each of which reflects a different set of applications. Six real and one synthetic HTTP traces, one for each pattern, are further identified and utilised to evaluate the performance of the proposed methods against the state-of-the-art approaches