Fractional Order Systems Fractional Order PID Controller Chaotic Fractional Order Systems Field Programmable Gate Array, Microcontroller and Field Programmable Analog Array Implementation Switched Capacitor and Integrated Circuit Design Modeling of Ionic Polymeric Metal Composite

Fractional Order Systems: Modeling and Control Applications

A review of performance monitoring and assessment techniques for univariate and multivariate control systems

In this chapter we give an overview of the field of iterative learning control (ILC). We begin with a detailed description of the ILC technique, followed by two illustrative examples that give a flavor of the nature of ILC algorithms and their performance. This is followed by a topical classification of some of the literature of ILC and a discussion of the connection between ILC and other common control paradigms, including conventional feedback control, optimal control, adaptive control, and intelligent control. Next, we give a summary of the major algorithms, results, and applications of ILC given in the literature. This discussion also considers some emerging research topics in ILC. As an example of some of the new directions in ILC theory, we present some of our recent results that show how ILC can be used to force a desired periodic motion in an initially non-repetitive process: a gas-metal arc welding system. The chapter concludes with summary comments on the past, present, and future of ILC.

Iterative Learning Control: An Expository Overview

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

The main objective of this paper is to solve the position tracking control problem for the permanent magnet linear motor by using the discrete-time fast terminal sliding mode control (SMC) method. Specifically, based on Euler's discretization technique, the approximate discrete-time model is first obtained and analyzed. Then, by introducing a new type of discrete-time fast terminal sliding surface, an improved discrete-time fast SMC method is developed and an equivalent-control-based fast terminal SMC law is subsequently designed. Rigorous analysis is provided to demonstrate that the fast terminal SMC law can offer a higher accuracy than the traditional linear SMC law. Numerical simulations and experimental results are finally performed to demonstrate the effectiveness of the proposed approach and show the advantages of the present discrete-time fast terminal SMC approach over some existing approaches, such as discrete-time linear sliding mode control approach and the PID control method.

Discrete-Time Fast Terminal Sliding Mode Control for Permanent Magnet Linear Motor

This paper focuses on the state-periodic adaptive compensation of cogging and Coulomb friction for permanent-magnet linear motors (PMLMs) executing a task repeatedly. The cogging force is considered as a position-dependent disturbance and the Coulomb friction is non-Lipschitz at zero velocity. The key idea of our disturbance compensation method is to use past information for one trajectory period along the state axis to update the current adaptation law. The new method consists of three different steps: 1) in the first repetitive trajectory, an adaptive compensator is designed to guarantee the l/sub 2/-stability of the overall system; 2) from the second repetitive trajectory and onward, a trajectory-periodic adaptive compensator stabilizes the system; and 3) to make use of the stored past state-dependent cogging information, a search process is utilized for adapting the current cogging coefficient. We illustrate the validity of our state-periodic adaptive cogging and friction compensator by actual PMLM-model-based simulation.

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State-periodic adaptive compensation of cogging and Coulomb friction in permanent-magnet linear motors

This paper focuses on the state-periodic adaptive compensation of cogging and Coulomb friction for permanent magnet linear motors (PMLM) executing a task repeatedly. The cogging force is considered as a position dependent disturbance and the considered Coulomb friction is non-Lipschitz at zero velocity. The key idea of our disturbance compensation method is to use one trajectory-period past information along the state axis to update the current adaptation law. The new method consists of three different steps: firstly, in the first repetitive trajectory, an adaptive compensator is designed to guarantee the l/sub 2/-stability of the overall system. Secondly, from the second repetitive trajectory and onwards, a trajectory-periodic adaptive compensator is designed to stabilize the system. Finally, to make use of the stored past state-dependent cogging information, a search process is utilized for adapting the current cogging coefficient. The validity of our adaptive cogging and friction compensator is illustrated through a simulation example.

State-periodic adaptive compensation of cogging and Coulomb friction in permanent magnet linear motors

A robust tuning method for fractional order PI controllers

A simple microprocessor controlled mini-electrochemical (EC) instrument integrating with square wave voltammetry (SWV) was designed and fabricated. Direct digital frequency synthesizer (DDFS) was exploited in the circuit design to generate a linear and very low frequency scanning waveform for SWV. An ultra-low bias current, a high performance operational amplifier, a lock-in amplifier, and several noise reduction methods, such as cable selection, PCB cleaning, and guarding, were utilized to achieve the capability of ultra-low current measurement. The initial performance test of this SWV-based EC device was also conducted in different ferricyanide solutions. Compared with commercial electrochemical analyzers, the miniaturized EC instrument presented here has the characteristics of low-cost, small size, ease of use and reduced energy consumption. Due to its flexible design, this SWV-based instrument can easily be extended to meet the requirements of the portable and reliable EC chip device integrating other commonly used EC methods.

Design and fabrication of a miniaturized electrochemical instrument and its preliminary evaluation

The EMG data acquired during voluntary movement of the active muscles of the disabled may provide useful control commands and information in functional electrical stimulation or in artificial prosthesis provided that the raw EMG data are property processed and identified. This technique may be used by the patients to transfer commands to their paralyzed extremities or artificial limbs. Combination of autoregressive and neural network technique to identify various functional hand movements is proposed. Functional hand movements such as palmar flexion and dorsiflexion, wrist pronation and supination, wrist flexion and extension, are identified. A fourth order parametric model is employed to evaluate the set of coefficients. The coefficients are then used as input for the neural network to identify the functional movement. Experiment was done on three healthy individuals and the rate of identification is shown to be adequate to be used in the development of either neural prostheses or artificial limbs.

Huifang Dou

Papers

State-periodic adaptive compensation of cogging and Coulomb friction in permanent-magnet linear motors

State-periodic adaptive compensation of cogging and Coulomb friction in permanent magnet linear motors

A robust tuning method for fractional order PI controllers

Design and fabrication of a miniaturized electrochemical instrument and its preliminary evaluation

A combination of AR and neural network technique for EMG pattern identification