Literature Review

Published on July 2016 | Categories: Documents | Downloads: 72 | Comments: 0 | Views: 808
of 12
Download PDF   Embed   Report

Comments

Content

A Neural-Network Based Technique for Modeling and Implementing LPV Control of An ArmDriven Inverted Pendulum (Lachhab, N.; Abbas, H.; Werner, H.; Dec 2008), Decision and Control, 2008. CDC 2008. 47th IEEE Conference

In this paper, recurrent neural-networks (RNNs) approach was presented together with stability and identifiability proofs based on the contraction mapping theorem and the concept of signpermutation equivalence, respectively. A slight simplification of the generalized RNN approach is also proposed to facilitate the practical application. To use the RNN for linear parametervarying (LPV) controller synthesis, a method is presented of transforming it into a discrete-time quasi LPV model in polytopic and linear fractional transformation (LFT) representations. A novel indirect technique for closed-loop identification with RNNs is proposed here to identify a black box model for an arm-driven inverted pendulum (ADIP). The identified RNN model is then transformed into a quasi-LPV model. Based on such LPV models, two discrete-time LPV controllers are synthesized to control the ADIP. The first one is a full-order standard polytopic LPV controller and the second one is a fixed-structure LPV controller in LFT form based on the quadratic separator concept. Experimental results illustrate the practicality of the proposed methods. (31 mac 2011)

State-Space Solutions to Standard H2 and H ’ Control Problems (Doyle, J.C.; Glover, K.; Khargonekar, P.P.; Francis, B.A., Aug 1989) Automatic Control, IEEE Transactions Simple state-space formulas are derived for all controllers solving the following standard H’ problem: For a given number >0, find all controllers such that the H ’ norm of the closed-loop transfer function is (strictly) less than . It is known that a controller exists if and only if the unique stabilizing solutions to two algebraic Riccati equations are positive definite and the spectral radius of their product is less than
2

. Under these conditions, a parameterization of all

controllers solving the problem is given as a linear fractional transformation (LFT) on a contractive, stable, free parameter. The state dimension of the coefficient matrix for the LFT, constructed using the two Riccati solutions, equals that of the plant and has a separation structure reminiscent of classical LQG (i.e. H 2) theory. This paper is intended to be of tutorial value, so a standard H2 solution is developed in parallel (31 Mac 2011).

Global Asymptotic Stability Criteria for Multilayer Recurrent Neural Networks with Applications to Modelling and Control (Suykens, J.A.K.; Vandewalle, J., Dec 1995) Neural Networks, 1995. Proceedings., IEEE International Conference

Sufficient conditions for global asymptotic stability of discrete time multilayer recurrent neural networks are derived in this paper. Both the autonomous and nonautonomous case are treated. Multilayer recurrent neural networks are interpreted as so-called NLq systems, which are nonlinear systems consisting of an alternating sequence of linear and static nonlinear operators that satisfy a sector condition (q `layers'). It turns out that many problems arising in recurrent neural networks and system and control theory can be interpreted as NLq systems, such as multilayer Hopfield nets, locally recurrent globally feedforward networks, generalized cellular neural networks, neural state space control systems, the Lur'e problem, linear fractional transformations with real diagonal uncertainty block, digital filters with overflow characteristic etc. In this paper we discuss applications of the theorems for designing neural state space control systems (emulator approach). Narendra's dynamic backpropagation procedure is modified in order to assess closed loop stability. The new theory also enables to consider reference inputs belonging to the class of functions l2 instead of specific reference inputs

Linear Parameter Estimation for Induction Machines Considering the Operating Conditions (De Souza Ribeiro, L.A.; Jacobina, C.B.; Lima, A.M.N., Jan 1999) Power Electronics, IEEE Transactions The use of linear parameter estimation techniques to determine the stator resistance, selfinductance of the stator winding, transient inductance, rotor time constant, as well as the angular shaft speed of a three-phase induction machine is investigated in this paper. In order to obtain results with maximum accuracy, some specific procedures to reduce the effect of the operating conditions on the quality of the estimates are investigated. Both computer and experimental results are used to anchor the main conclusions issued from this study

Nonlinear Modelling and Support Vector Machines (Suykens, J.A.K.;May 2001) Instrumentation and Measurement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE Neural networks such as multilayer perceptrons and radial basis function networks have been very successful in a wide range of problems. In this paper we give a short introduction to some new developments related to support vector machines (SVM), a new class of kernel based techniques introduced within statistical learning theory and structural risk minimization. This new approach lends to solving convex optimization problems and also the model complexity follows from this solution. We especially focus on a least squares support vector machine formulation (LS-SVM) which enables to solve highly nonlinear and noisy black-box modelling problems, even in very high dimensional input spaces. While standard SVMs have been basically only applied to static problems like classification and function estimation, LS-SVM models have been extended to recurrent models and use in optimal control problems. Moreover, using weighted least squares and special pruning techniques, LS-SVMs can be employed for robust nonlinear estimation and sparse approximation. Applications of (LS)-SVMs to a large variety of artificial and real-life data sets indicate the huge potential of these methods

Discrete-time LPV Current Control of An Induction Motor (Bendtsen, J.D.; Trangbaek, K.; Dec 2003) Decision and Control, 2003. Proceedings. 42nd IEEE Conference on In this paper we apply a new method for gain-scheduled output feedback control of nonlinear systems to current control of an induction motor. The method relies on the developed controller synthesis results for linear parameter-varying (LPV) systems, where the controller synthesis is formulated as a set of linear matrix inequalities with full-block multipliers. A standard nonlinear model of the motor is constructed and written on LPV form. We then show that, although originally developed in continuous time, the controller synthesis results can be applied to a discrete-time model as well without further complications. The synthesis method is applied to the model, yielding an LPV discrete-time controller. Finally, the efficiency of the control scheme is validated via simulations as well as experimentally.

Flux Estimation of Induction Machines with the Linear Parameter-Varying System Identification Method (Pan, J. Westwick, D. Nowicki, E. , May 2004) Electrical and Computer Engineering, 2004. Canadian Conference In indirect field orientation control (FOC) methods, the magnitude and direction of the rotor flux are estimated from measurements of stator voltages, stator currents and the angular velocity of the shaft using a parameter model of the induction machine. However the performance of indirect FOC methods is dependent on the accuracy of the machine model and is therefore sensitive to variations in motor parameters such as the rotor resistance and the magnetizing inductance. Motor parameters vary greatly with temperature, frequency and current amplitude. This paper presents a novel method for estimating the rotor flux in an induction motor. Subspace identification methods are used to construct a linear parameter-varying (LPV), discrete time model of an induction motor based on measurements of the stator voltages and currents and of the angular velocity of the shaft. The identification algorithm has been tested on data obtained from a nonlinear, continuous-time simulation model.

A reduced-order robust observer using nonlinear parameter estimation for induction motors (Tlili, A.S. Braiek, E.B. Oct 2002) Systems, Man and Cybernetics, 2002 IEEE International Conference This paper presents an original approach for the design of a robust observer for a fifth order model of induction motors (IM), defined by a linear system with polytopic representation and linear parameter variation (LPV), to reconstruct the non measurable state variables and mainly the rotor flux. This approach is based on the notion of robust detectability and the using of the linear matrix inequality (LMI) tools to calculate the gain of the polytopic reduced-order robust observer, and the identification online of the variable parameter (the rotor time constant) using a special nonlinear observer. The dynamical performances of the proposed observer is then analyzed and compared.

Speed sensorless linear parameter variant H’ control of the induction motor (Fodor, D. Toth, R. Dec 2004) Decision and Control, 2004. CDC. 43rd IEEE Conference The paper shows the design of a robust control structure for the speed sensorless vector control of the IM, based on the mixed sensitivity (MS) linear parameter variant (LPV) H’ control theory. The controller makes possible the direct control of the flux and speed of the motor with torque adaptation in noisy environment. The whole control system is tested by intensive simulations and according to the results it shows good dynamic and robust performance. Implementation issues based on a DSP TMS320F243 development platform are also presented.

Variable Speed Drive of Single Phase Induction Motor Using Frequency Control Method (Aung Zaw Latt Ni Ni Win, April 2009) Education Technology and Computer, 2009. ICETC '09. International Conference Single-phase induction motors are widely used in home appliances and industrial control. The multispeed operation and multipurpose operation are provided by controlling the speed of these motors. This research paper is variable speed drive of induction motor using frequency control method. It is to develop the solid state control system to be reliable and economically feasible to use with fractional horse power motors. The proposed variable speed drive includes power conversion section (AC to DC and DC to AC), used the switching element of IRF 840 N-channel MOSFET. The four IRF 840 MOSFETS are used as H-bridge inverter to provide the alternating current to the motor. In this drive, C124 transistors and MJE 13002 transistors are used as driver circuit to drive the H-bridge inverter. There are two power supplies in this drive. The 12 V power supply is used for frequency control circuit and driver circuit. The 300 V power supply is used for H-bridge inverter. In this drive, pulse width modulation SG3525A IC is used to control the frequency. The frequency range of the constructed variable drive circuit is 16 Hz to 56 Hz at constant voltage for changing the speed of induction motor. In this research paper, drive schemes of single-phase induction motor, principle operations of components used in constructed variable speed drive, and design calculation to construct this drive are included. Moreover, the experimental tests of this drive when driving a fractional horse power single-phase induction motor are described.

Chuck Lewin, April 10, 2006: New Developments in Commutation and Motor Control Techniques

Riccardo Di Gabriele, ³Controllo vettoriale di velocità di un motore asincrono mediante il Filtro di Kalman Esteso´, Tesi di Laurea, Università degli Studi di L¶Aquila, Anno Accademico 1997-98

Phase-Control Alternatives for Single-Phase AC Motors Offer Smart, Low-Cost, Solutions(2003)
by Howard Abramowitz, Ph.D EE , President, AirCare Automation Inc. Abstract - Single Phase AC motors continue to be the primary solution for air-movement,
pumping and compressor applications. Their low cost and availability make them ideal for lowperformance systems. DC Brushless platforms attempt to address these applications but their higher cost and complexity continue to form impenetrable barriers of entry. They are viewed as overkill for most applications. Single-Phase Inverter Drives have come on the scene, making headway, but remain complex and costly. Phase-Control solutions are being revisited and upgraded to fill the void. This paper explores the performance limitations and trade-offs of the

Phase-Control solution and clarifies the boundaries under which the TRIAC Phase-control is a preferred method of speed control and the improvements provided by smart phase-control. (6 April 2011)

Sponsor Documents

Or use your account on DocShare.tips

Hide

Forgot your password?

Or register your new account on DocShare.tips

Hide

Lost your password? Please enter your email address. You will receive a link to create a new password.

Back to log-in

Close