There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

A survey of iterative learning control

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

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.

Adaptive Control

Economic dispatch problem (EDP) is an important class of optimization problems in the smart grid, which aims at minimizing the total cost when generating certain amount of power. In this work, a novel consensus based algorithm is proposed to solve EDP in a distributed fashion. The quadratic convex cost functions are assumed in the problem formulation, and the strongly connected communication topology is sufficient for the information exchange. Unlike centralized approaches, the proposed algorithm enables generators to collectively learn the mismatch between demand and total amount of power generation. The estimated mismatch is then used as a feedback mechanism to adjust current power generation by each generator. With a tactical initial setup, eventually, all generators can automatically minimize the total cost in a collective sense.

Consensus Based Approach for Economic Dispatch Problem in a Smart Grid

This note presents a new nonlinear integral-type sliding surface which incorporates a virtual nonlinear nominal control to achieve prescribed specifications. First, the plant with matched uncertainties is considered. The resultant closed-loop system during ideal sliding mode behaves exactly like the nominal plant under the nonlinear nominal control, which completely nullifies the matched uncertainties and consequently satisfies the prescribed specifications. Second, the stability analysis of the proposed sliding mode for the systems with unmatched uncertainties is performed to exploit the stability conditions. Numerical results demonstrate the validity of the proposed concept.

Nonlinear integral-type sliding surface for both matched and unmatched uncertain systems

In this article we review the recent advances in iterative learning control (ILC) for nonlinear dynamic systems. In the research field of ILC, two categories of system nonlinearities are considered, namely, the global Lipschitz continuous (GLC) functions and local Lipschitz continuous (LLC) functions. ILC for GLC systems is widely studied and analysed using contraction mapping approach, and the focus of recent exploration moves to application problems, though a number of theoretical issues remain open. ILC for LLC systems is currently a hot area and the recent research focuses on ILC design and analysis by means of Lyapunov approach. The objectives of this article are to introduce recent development and advances in nonlinear ILC schemes, highlight their effectiveness and limitations, as well as discuss the directions for further exploration of nonlinear ILC.

A survey on iterative learning control for nonlinear systems

Parasitic torque pulsations exist in permanent magnet synchronous motors (PMSMs) due to nonsinusoidal flux density distribution around the air-gap, errors in current measurements, and variable magnetic reluctance of the air-gap due to stator slots. These torque pulsations vary periodically with rotor position and are reflected as speed ripple, which degrades the PMSM drive performance, particularly at low speeds. Because of the periodic nature of torque ripple, iterative learning control (ILC) is intuitively an excellent choice for torque ripple minimization. In this paper, first we propose an ILC scheme implemented in time domain to reduce periodic torque pulsations. A forgetting factor is introduced in this scheme to increase the robustness of the algorithm against disturbance. However, this limits the extent to which torque pulsations can be suppressed. In order to eliminate this limitation, a modified ILC scheme implemented in frequency domain by means of Fourier series expansion is presented. Experimental evaluations of both proposed schemes are carried out on a DSP-controlled PMSM drive platform. Test results obtained demonstrate the effectiveness of the proposed control schemes in reducing torque ripple by a factor of approximately three under various operating conditions.

Torque ripple minimization in PM synchronous motors using iterative learning control

A new discrete-time integral sliding-mode control (DISMC) scheme is proposed for sampled-data systems. The new control scheme is characterized by a discrete-time integral sliding manifold which inherits the desired properties of the continuous-time integral sliding manifold, such as full order sliding manifold with pole assignment, and elimination of the reaching phase. In particular, comparing with existing discrete-time sliding-mode control, the new scheme is able to achieve more precise tracking performance. It will be shown in this work that, the new control scheme achieves O(T2) steady-state error for state regulation with the widely adopted delay-based disturbance estimation. Another desirable feature is, the proposed DISMC prevents the generation of overlarge control actions due to deadbeat response, which is usually inevitable due to the existence of poles at the origin for a reduced order sliding manifold designed for sampled-data systems. Both the theoretical analysis and illustrative example demonstrate the validity of the proposed scheme

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Jian-Xin Xu

Papers

Consensus Based Approach for Economic Dispatch Problem in a Smart Grid

Nonlinear integral-type sliding surface for both matched and unmatched uncertain systems

A survey on iterative learning control for nonlinear systems

Torque ripple minimization in PM synchronous motors using iterative learning control

On the Discrete-Time Integral Sliding-Mode Control