A Unified Framework for the Study of Anti-Windup Designs

We present a unified framework for the study of linear time-invariant (LTI) systems subject to control input nonlinearities. The framework is based on the following two-step design paradigm: "Design the linear controller ignoring control input nonlinearities and then add anti-windup bumpless transfer (AWBT) compensation to minimize the adverse eflects of any control input nonlinearities on closed loop performance". The resulting AWBT compensation is applicable to multivariable controllers of arbitrary structure and order. All known LTI anti-windup and/or bumpless transfer compensation schemes are shown to be special cases of this framework. It is shown how this framework can handle standard issues such as the analysis of stability and performance with or without uncertainties in the plant model. The actual analysis of stability and performance, and robustness issues are problems in their own right and hence not detailed here. The main result is the unification of existing schemes for AWBT compensation under a general framework

A decentralized scalable approach to voltage control of DC islanded microgrids

We propose a new decentralized control scheme for DC Islanded microGrids (ImGs) composed by several Distributed Generation Units (DGUs) with a general interconnection topology. Each local controller regulates to a reference value the voltage of the Point of Common Coupling (PCC) of the corresponding DGU. Notably, off-line control design is conducted in a Plug-and-Play (PnP) fashion meaning that (i) the possibility of adding/removing a DGU without spoiling stability of the overall ImG is checked through an optimization problem; (ii) when a DGU is plugged in or out at most neighbouring DGUs have to update their controllers and (iii) the synthesis of a local controller uses only information on the corresponding DGU and lines connected to it. This guarantee total scalability of control synthesis as the ImG size grows or DGU gets replaced. Yes, under mild approximations of line dynamics, we formally guarantee stability of the overall closed-loop ImG. The performance of the proposed controllers is analyzed simulating different scenarios in PSCAD.Comment: arXiv admin note: text overlap with arXiv:1405.242

SYSTEM IDENTIFICATION AND MODEL PREDICTIVE CONTROL FOR INTERACTING SERIES PROCESS WITH NONLINEAR DYNAMICS

This thesis discusses the empirical modeling using system identification technique and the implementation of a linear model predictive control with focus on interacting series processes. In general, a structure involving a series of systems occurs often in process plants that include processing sequences such as feed heat exchanger, chemical reactor, product cooling, and product separation. The study is carried out by experimental works using the gaseous pilot plant as the process. The gaseous pilot plant exhibits the typical dynamic of an interacting series process, where the strong interaction between upstream and downstream properties occurs in both ways. The subspace system identification method is used to estimate the linear model parameters. The developed model is designed to be robust against plant nonlinearities. The plant dynamics is first derived from mass and momentum balances of an ideal gas. To provide good estimations, two kinds of input signals are considered, and three methods are taken into account to determine the model order. Two model structures are examined. The model validation is conducted in open-loop and in closed-loop control system. Real-time implementation of a linear model predictive control is also studied. Rapid prototyping of such controller is developed using the available equipments and software tools. The study includes the tuning of the controller in a heuristic way and the strategy to combine two kinds of control algorithm in the control system. A simple set of guidelines for tuning the model predictive controller is proposed. Several important issues in the identification process and real-time implementation of model predictive control algorithm are also discussed. The proposed method has been successfully demonstrated on a pilot plant and a number of key results obtained in the development process are presented

Modeling and H-Infinity Loop Shaping Control of a Vertical Takeoff and Landing Drone

abstract: VTOL drones were designed and built at the beginning of the 20th century for military applications due to easy take-off and landing operations. Many companies like Lockheed, Convair, NASA and Bell Labs built their own aircrafts but only a few from them came in to the market. Usually, flight automation starts from first principles modeling which helps in the controller design and dynamic analysis of the system. In this project, a VTOL drone with a shape similar to a Convair XFY-1 is studied and the primary focus is stabilizing and controlling the flight path of the drone in its hover and horizontal flying modes. The model of the plane is obtained using first principles modeling and controllers are designed to stabilize the yaw, pitch and roll rotational motions. The plane is modeled for its yaw, pitch and roll rotational motions. Subsequently, the rotational dynamics of the system are linearized about the hover flying mode, hover to horizontal flying mode, horizontal flying mode, horizontal to hover flying mode for ease of implementation of linear control design techniques. The controllers are designed based on an H∞ loop shaping procedure and the results are verified on the actual nonlinear model for the stability of the closed loop system about hover flying, hover to horizontal transition flying, horizontal flying, horizontal to hover transition flying. An experiment is conducted to study the dynamics of the motor by recording the PWM input to the electronic speed controller as input and the rotational speed of the motor as output. A theoretical study is also done to study the thrust generated by the propellers for lift, slipstream velocity analysis, torques acting on the system for various thrust profiles.Dissertation/ThesisMasters Thesis Electrical Engineering 201

A State Observer Based Methodology for Improving Control Schemes Employing Multiple Exogenous Feedforward Signals

Feedback control provides the basis of many different control schemes. However, even high gain feedback may be insufficient for processes requiring high precision or non-causal behavior such as micro additive manufacturing or metrology. Exogenous feedforward inputs can be sometimes be used to provide a solution in these circumstances. These signals are carefully trained such that they produce the desired response in their target system. However, the efficacy of these signals can be greatly diminished when the systems they are applied to have different initial conditions from the ones for which the signals were designed. This problem is magnified when multiple feedforward inputs are applied sequentially. The subtype of Iterative Learning Control, Basis Task Iterative Learning Control (BTILC) involves creation of multiple exogenous feedforward signals which correspond to various learned behaviors. These signals are then applied sequentially in order to produce more complex system outputs without explicitly applying the learning algorithm to those outputs. This makes it a prime example of a control scheme which suffers from the decreased signal efficacy discussed previously. This manuscript first generates a novel algorithmic solution to these issues leveraging state information observed in the feedforward signal training process; called an Informed State Correction (ISC). Then, it presents experimental results which demonstrate a performance increase of approximately 70% in BTILC control schemes implementing an ISC. These results represent a significant increase in the efficacy of BTILC and its applicability to real-world control scenarios. Furthermore, the ISC has been posed such that it can be applied to any control scheme employing multiple exogenous feedforward signals, where it may provide similar performance benefits.Dr. David HoelzleThe College of EngineeringNo embargoAcademic Major: Mechanical Engineerin

Advanced Anti-Windup Techniques for the Limitation of the Effects of the Actuator Saturation

In this thesis an industrial issue is analysed. The issue consist on the undesirable effect of actuator sturation. Two approaches are given to solve the issue: an accurate inertia identification algorithm based on the DFT coefficient; and advanced anti-windup compensators. The principle of the modern anti-windup (DLAW and MRAW, LMI-based design approach), and a systematic design design procedure for the observer-based anti-windup are given. Simulation and test results are also given.ope

SYSTEM IDENTIFICATION AND MODEL PREDICTIVE CONTROL FOR INTERACTING SERIES PROCESS WITH NONLINEAR DYNAMICS

This thesis discusses the empirical modeling using system identification technique and the implementation of a linear model predictive control with focus on interacting series processes. In general, a structure involving a series of systems occurs often in process plants that include processing sequences such as feed heat exchanger, chemical reactor, product cooling, and product separation. The study is carried out by experimental works using the gaseous pilot plant as the process. The gaseous pilot plant exhibits the typical dynamic of an interacting series process, where the strong interaction between upstream and downstream properties occurs in both ways. The subspace system identification method is used to estimate the linear model parameters. The developed model is designed to be robust against plant nonlinearities. The plant dynamics is first derived from mass and momentum balances of an ideal gas. To provide good estimations, two kinds of input signals are considered, and three methods are taken into account to determine the model order. Two model structures are examined. The model validation is conducted in open-loop and in closed-loop control system. Real-time implementation of a linear model predictive control is also studied. Rapid prototyping of such controller is developed using the available equipments and software tools. The study includes the tuning of the controller in a heuristic way and the strategy to combine two kinds of control algorithm in the control system. A simple set of guidelines for tuning the model predictive controller is proposed. Several important issues in the identification process and real-time implementation of model predictive control algorithm are also discussed. The proposed method has been successfully demonstrated on a pilot plant and a number of key results obtained in the development process are presented