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Liuping Wang

Bio: Liuping Wang is an academic researcher from RMIT University. The author has contributed to research in topics: Model predictive control & Control theory. The author has an hindex of 33, co-authored 213 publications receiving 5267 citations. Previous affiliations of Liuping Wang include University of Melbourne & Melbourne Institute of Technology.


Papers
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Book
12 May 2009
TL;DR: In this article, the authors present methods for design and implementation of MPC systems using basis functions that confer the following advantages: continuous-and discrete-time MPC problems solved in similar design frameworks; a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and a more general discrete time MPC design that becomes identical to the traditional approach for an appropriate choice of parameters.
Abstract: Model Predictive Control (MPC) is unusual in receiving on-going interest in both industrial and academic circles. Issues such as plant optimization and constrained control which are critical to industrial engineers are naturally embedded in its designs. Model Predictive Control System Design and Implementation Using MATLAB proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages: continuous- and discrete-time MPC problems solved in similar design frameworks; a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters. After the theoretical presentation, detailed coverage is given to three industrial applications: a food extruder, a motor and a magnetic bearing system. The subject of quadratic programming, often associated with the core optimization algorithms of MPC is also introduced and explained. The technical contents of this book, mainly based on advances in MPC using state-space models and basis functions to which the author is a major contributor, will be of interest to control researchers and practitioners, especially of process control. From a pedagogical standpoint, this volume includes numerous simple analytical examples and every chapter contains problems and MATLAB programs and exercises to assist the student.

1,310 citations

BookDOI
31 Mar 2008
TL;DR: Identification of Continuous-time Models from Sampled Data brings together contributions from well-known experts who present an up-to-date view of this active area of research and describe recent methods and software tools developed in this field.
Abstract: System identification is an established field in the area of system analysis and control. It aims to determine particular models for dynamical systems based on observed inputs and outputs. Although dynamical systems in the physical world are naturally described in the continuous-time domain, most system identification schemes have been based on discrete-time models without concern for the merits of natural continuous-time model descriptions. The continuous-time nature of physical laws, the persistent popularity of predominantly continuous-time proportional-integral-derivative control and the more direct nature of continuous-time fault diagnosis methods make continuous-time modeling of ongoing importance. Identification of Continuous-time Models from Sampled Data brings together contributions from well-known experts who present an up-to-date view of this active area of research and describe recent methods and software tools developed in this field. They offer a fresh look at and new results in areas such as: time and frequency domain optimal statistical approaches to identification; parametric identification for linear, nonlinear and stochastic systems; identification using instrumental variable, subspace and data compression methods; closed-loop and robust identification; and continuous-time modeling from non-uniformly sampled data and for systems with delay. The CONtinuous-Time System IDentification (CONTSID) toolbox described in the book gives an overview of developments and practical examples in which MATLAB can be brought to bear in the cause of direct time-domain identification of continuous-time systems.This survey of methods and results in continuous-time system identification will be a valuable reference for a broad audience drawn from researchers and graduate students in signal processing as well as in systems and control. It also covers comprehensive material suitable for specialised graduate courses in these areas.

467 citations

Journal ArticleDOI
Liuping Wang1
TL;DR: In this paper, a more appropriate expansion, related to Laguerre net-works, is introduced and analyzed by relaxing the constraint on the exponential change rate of the control signal and allowing arbitrary complexity in describing the trajectory.

191 citations

Journal ArticleDOI
TL;DR: In this paper, a frequency sampling filter model is used to represent the dynamic linear element and a simple least-squares algorithm is applied to simultaneously estimate the parameters of the linear subsystem and the inverse static nonlinearity.

189 citations

Journal ArticleDOI
TL;DR: This paper addresses the problem of reducing the impact of periodic disturbances arising from the current sensor offset error on the speed control of a permanent-magnet synchronous motor using a cascade model predictive control scheme with an embedded disturbance model.
Abstract: This paper addresses the problem of reducing the impact of periodic disturbances arising from the current sensor offset error on the speed control of a permanent-magnet synchronous motor. The new results are based on a cascade model predictive control scheme with an embedded disturbance model. Supporting experimental results, where the per-unit model is used to improve numerical conditioning, are also given.

174 citations


Cited by
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Journal ArticleDOI
TL;DR: It is seen that many PID variants have been developed in order to improve transient performance, but standardising and modularising PID control are desired, although challenging, and the inclusion of system identification and "intelligent" techniques in software based PID systems helps automate the entire design and tuning process to a useful degree.
Abstract: Designing and tuning a proportional-integral-derivative (PID) controller appears to be conceptually intuitive, but can be hard in practice, if multiple (and often conflicting) objectives such as short transient and high stability are to be achieved. Usually, initial designs obtained by all means need to be adjusted repeatedly through computer simulations until the closed-loop system performs or compromises as desired. This stimulates the development of "intelligent" tools that can assist engineers to achieve the best overall PID control for the entire operating envelope. This development has further led to the incorporation of some advanced tuning algorithms into PID hardware modules. Corresponding to these developments, this paper presents a modern overview of functionalities and tuning methods in patents, software packages and commercial hardware modules. It is seen that many PID variants have been developed in order to improve transient performance, but standardising and modularising PID control are desired, although challenging. The inclusion of system identification and "intelligent" techniques in software based PID systems helps automate the entire design and tuning process to a useful degree. This should also assist future development of "plug-and-play" PID controllers that are widely applicable and can be set up easily and operate optimally for enhanced productivity, improved quality and reduced maintenance requirements.

2,461 citations

01 Nov 1981
TL;DR: In this paper, the authors studied the effect of local derivatives on the detection of intensity edges in images, where the local difference of intensities is computed for each pixel in the image.
Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

1,829 citations

Journal Article
TL;DR: In this paper, two major figures in adaptive control provide a wealth of material for researchers, practitioners, and students to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs.
Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

1,814 citations

Book
08 Aug 2005
TL;DR: Advanced PID Control builds on the basics learned in PID Controllers but augments it through use of advanced control techniques, including auto-tuning, gain scheduling and adaptation.
Abstract: The authors of the best-selling book PID Controllers: Theory, Design, and Tuning once again combine their extensive knowledge in the PID arena to bring you an in-depth look at the world of PID control. A new book, Advanced PID Control builds on the basics learned in PID Controllers but augments it through use of advanced control techniques. Design of PID controllers are brought into the mainstream of control system design by focusing on requirements that capture effects of load disturbances, measurement noise, robustness to process variations and maintaining set points. In this way it is possible to make a smooth transition from PID control to more advanced model based controllers. It is also possible to get insight into fundamental limitations and to determine the information needed to design good controllers. The book provides a solid foundation for understanding, operating and implementing the more advanced features of PID controllers, including auto-tuning, gain scheduling and adaptation. Particular attention is given to specific challenges such as reset windup, long process dead times, and oscillatory systems. As in their other book, modeling methods, implementation details, and problem-solving techniques are also presented.

1,533 citations