bluuebunny/crossref_metadata_2025_split huggingface dataset:
An autosynthesizing non‐linear control system using a rule‐based expert system
Authors: Taylor James H. , Stringer Philip G. 
Published: July 2007

AbstractAn advanced non‐linear control system is obtained by combining recent developments in non‐linear control system synthesis with a rule‐based system approach to real‐time control. The basic problem to be addressed is the control of a non‐linear plant which is sufficiently sensitive to both operating point and input amplitude that the desired control system performance can be obtained only with a non‐linear controller that is ‘retuned’ or ‘resynthesized’ whenever the operating point changes significantly. (The term ‘changes significantly’ in this context signifies an operating point change that causes the non‐linear control system input/output behaviour to change substantially in an undesirable way.) This is accomplished by a hierarchically organized intelligent control system with a conventional reprogrammable controller under the direction of a rule‐based expert system. The function of the expert system is to(i) monitor the behaviour of the non‐linear control system to determine when retuning or resynthesis is required; if the behaviour is satisfactory, then continue passive monitoring; else(ii) when retuning or resynthesis is required: set up and execute experiments to derive the model information required to tune or synthesize a new non‐linear controller in terms of a given structure and parametrization; execute the retuning/resynthesis procedure; reprogramme the controller (download the parametrization); and recommission the updated non‐linear control system and return to normal operation.In essence, the rule‐based system provides supervisory control (‘meta‐control’) for a conventional reprogrammable non‐linear controller that will autotune or autosynthesize as required. Autosynthesis takes place to accommodate variability in plant behaviour due to operating point changes, and the nonlinear controller thus synthesized corrects for amplitude sensitivity. This concept represents one way to combine artificial intelligence with control; it will be discussed and illustrated by example below.
An expert system for interaction analysis of multivariable systems
Authors: Lieslehto J. , Koivo H. N. 
Published: July 2007

AbstractIn this paper a demonstration expert system for interaction analysis of time‐invariant, linear, multivariable systems has been developed. The principle is to choose an area that is significant in control design but still limited to be implementable. To our knowledge no such systems have been reported in the literature. The paper first briefly describes the general idea of the developed expert system. Next the software structure of the expert system is given. A short description of the interaction analysis and conclusions finish the paper.
Adaptive lqg controller with loop transfer recovery
Authors: Tay T. T. , Moore J. B. 
Published: July 2007

AbstractIn this paper we propose for scalar plants an adaptive LQG controller with adaptive input sensitivity function/loop transfer recovery of an associated adaptive LQ design. The sensitivity recovery can be viewed as a frequency‐shaped loop recovery where the weights involve a sensitivity function. The adaptive loop/sensitivity recovery is achieved by feeding back the estimation residuals to the control through a stable bounded input, bounded output (BIBO) adaptive filter Qk. For simplicity we consider fixed but uncertain plants in the model set and identification schemes where there are consistent parameter estimates. For non‐minimum phase plants an asymptotic partial recovery is achieved via a recursive least squares update of the BIBO filter Qk. The degree of recovery can be prescribed a priori between zero and the maximum possible. For the case of minimum phase plant estimates, full loop recovery may be achieved asymptotically by prescribing a maximum degree of recovery.The motivation for proposing the new adaptive control algorithm is to enhance robustness of adaptive LQG designs, taking advantage of the robustness enhancement properties of sensitivity/loop recovery for off‐line designs. The robustness properties of the new algorithm are demonstrated by simulation results.
Lp‐stability of estimation errors of kalman filter for tracking time‐varying parameters
Authors: Zhang J. F. , Guo L. , Chen H. F. 
Published: July 2007

AbstractThe Kalman filtering algorithm, owing to its optimality in some sense, is widely used in systems and control, signal processing and many other fields. This paper presents a detailed analysis for theLp‐stability of tracking errors when the Kalman filter is used for tracking unknown time‐varying parameters. The results of this paper differ from the previous ones in that the regression vector (in a linear regression model) or the output matrix (in state space terminology) is random rather than deterministic. The context is kept general so that, in particular, the time‐varying parameter is allowed to be unbounded, and no assumption of stationarity or independence for signals is made.
A long‐range adaptive controller for time‐varying plants with sudden changes
Authors: Costa B. M. , Lemos J. M. 
Published: July 2007

AbstractThis paper is concerned with fault detection for adaptive control. Experimental results of the long‐range adaptive controller MUSMAR coupled with a fault detection mechanism are presented. The experiments were performed on pilot systems in real time. The fault detection mechanism was used to improve the performance of the MUSMAR controller during the occurrence of faults.
Decentralized indirect adaptive control of interconnected systems
Authors: Datta A. , Ioannou P. 
Published: July 2007

AbstractContinuous time decentralized indirect adaptive controllers are proposed for a large‐scale system composed of N interconnected linear subsystems with unknown parameters. Each local adaptive law utilizes a normalizing signal which is generated using the local input and the outputs of all the subsystems. This requires the exchange of output signals between the different subsystems, so that the scheme is no longer totally decentralized. Nevertheless, the degree of computational complexity is about the same as that of a totally decentralized scheme and this makes the scheme very attractive.
A retrospective self‐tuning controller
Authors: Goodhart S. G. , Burnham K. J. , James D. J. G. 
Published: July 2007

AbstractA new self‐tuning control algorithm appropriate for industrial systems which exhibit non‐linear characteristics and fast dynamic behaviour is proposed. The resulting self‐tuning controller makes use of a weighted sum of the control input generated from a standard self‐tuning control algorithm combined with a set of control input suggestions evaluated retrospectively. Use is made of a past sequence of steady state gains evaluated over a prespecified retrospective horizon together with a filtered prediction of the current system output which is obtained from a steady state Kalman filter. Adopting such an approach provides an element of corrective action based on information which would otherwise be disregarded by a standard self‐tuning scheme.Simulation studies involving a number of identified linear models of an industrial hydraulic servo system are presented. In terms of reduced control input variance and increased accuracy of set‐point following, the effectiveness of the new retrospective self‐tuning controller is compared to that of a standard self‐tuning pole placement controller and results indicate that significant improvements in overall performance are possible.
Modelling and self‐adjusting control of an absorption column
Authors: Najim K. 
Published: July 2007

AbstractThis paper describes the model and solution of the constrained optimal control problem associated with a packed absorption column. The control problem is solved using a learning automaton operating in a random environment. On the basis of physical and chemical laws, a model has been developed. It consists of three hyperbolic partial non‐linear differential equations. A solution of diethanolamine (the absorbent) is used to absorb the CO2 contained in a gas mixture. The primary manipulated variables are the flow rate of the absorbent and the concentration of CO2 in the gas mixture. The control objective is to maintain the concentration of CO2 close to a desired value, subject to control limit restriction, in order to avoid the flooding of the column. It leads to a stochastic programming problem, the solution of which is closely associated with the behaviour of an automaton in a random environment corresponding to the column. Detailed computer simulation results which demonstrate the performance of this automaton controller are presented
Adaptive predictive control of a continuous fermentation process
Authors: Dahhou B. , Chamilothoris G. , Roux G. 
Published: July 2007

AbstractThis work is concerned with the automatic control of biotechnological processes, in particular the regulation of a biomass concentration inside a continuous fermentation process. To overcome the difficulties often encountered with classical regulators, we use adaptive scheme techniques. We give a predictive control scheme using on‐line estimation of the specific growth rate (considered as a time‐varying parameter) which takes into account the feed effect on this parameter and calculate the dilution rate at each step.
Linear adaptive control of a single‐tether system
Authors: Greene M. E. , Carter J. T. , Walls J. L. 
Published: July 2007

AbstractA control law for a single‐tether orbiting satellite system based on a reduced order linear adaptive control technique is presented. The main advantages of this technique are its design simplicity and the facts that specific system parameters and model linearization are not required when disigning the conroller. Two controllers are developed: one which uses only tension in the tether as control actuation and one which uses both tension and in‐plane thrusters as control actaution. Both a sixth‐order non‐linear and an 11th‐order bead model of a tethered satellite system are used for simulation purposes, demonstrating the ability of the controller to manage an uncertain system. Retrieval and stationkeeping results using these non‐linear models and the linear adaptive controller demonstrate the feasibility of the method. The robustness of the controller with respect to parameter uncertainties is also demonstrated by changing the non‐linear model and parameters whithin the model without redesigning the controller.
Model reference adaptive control applied to electrical machines
Authors: Unbehauen H. , Keuchel U. 
Published: July 2007

AbstractThis contribution reports about a discrete‐time robust adaptive control strategy for single‐input/single‐output systems with arbitrary zeros. The adaptive algorithm is mainly based on a direct model reference method. The basic idea presented here is to divide the feedback law into a direct adaptive and a fixed part. By a certain choice of the non‐adaptive controller parameters the robustness of the adaptive control loop with respect to unmodelled dynamics of the plant may be increased. The non‐adaptive part of the controller can be reformulated as a bypass to the plant and will be denoted as the ‘correction network’ owing to its action on the open‐loop zeros of the augmented plant. The new adaptive control strategy removes the major drawbacks of model reference control and is investigated for speed control of a DC motor and voltage control of a synchronous generator.
Robust adaptive control of robotic manipulators without the regressor matrix
Authors: Yuan Jing , Stepanenko Yury 
Published: July 2007

AbstractA new adaptive controller is presented here for rigid‐body robotic manipulators. It is stable and robust with respect to a class of external disturbances. The robustness of the adaptive controller is established without the ‘slow‐varying’ assumption and the computationally demanding regressor matrix. The control law consists of a non‐adaptive PD control part and an adaptive control part. It uses two adaptive matrices to compensate two uniformly bounded coefficient matrices derived from the original dynamics. A α σ|q̇|‐modified adaptive law is designed to adjust the adaptive matrices. A Lyapunov‐type stability analysis indicates that the closed‐loop system is uniformly ultimately bounded. The tracking error and compensation error will eventually converge into a closed region, which can be made arbitrarily small by adjusting the controller parameters. Simulation results are included to demonstrate the performance of the proposed controller.
The method of recursive aim inequalities in adaptive control theory
Authors: Bondarko V. A. , Yakubovich V. A. 
Published: July 2007

AbstractThe method of recursive aim inequalities has been used in the theory of adaptive control since the late 1960s by a group of Leningrad mathematicians. The problem is reduced to an infinite system of recursive inequalities on the vectors of unknown parameters. These inequalities can be solved by using specially developed, finitely convergent algorithms. The method is illustrated by the solution of adaptive control problems for linear discrete systems under different assumptions on the plant and disturbances.
Adaptation problems in expert systems
Authors: Gorodetsky V. I. 
Published: July 2007

AbstractExpert system (ES) theory problems have been traditionally considered without any essential connection with common control problems. However, nowadays, because of progress in ES theory development in practice and because of its wide use for solving complex control problems, new items in this field of research are being stressed, many of which are connected with traditional automation control.The combined study of adaptation and ES problems makes it possible to draw significant practical conclusions for systems of both types. In this work an analysis of general problems and of possible approaches to solving them in some special types of ESs based on Bayes inference is carried out. The main attention is focused on such ES characteristics as: (i) inference stability (with respect to variations in probabilistic approximation measures of rule truth); (ii) knowledge base adaptation (rule modification and new rule creation or approximation measure rule tranformation); (iii) knowledge base consistency maintenance.
The Kalman‐Yakubovich Lemma for non‐stationary systems and its applications
Authors: Savkin A. V. 
Published: July 2007

AbstractAn existence problem for non‐stationary Lyapunov functions is considered. This problem is connected according to a certain rule with the linear Hamiltonian system that is called adjoint. We apply this method to obtain conditions of absolute stability and adaptive stabilization for non‐linear non‐stationary systems.
A comparative study of normalized and unnormalized tuning errors in parameter adaptive control
Authors: Morse A. S. 
Published: July 2007

AbstractIn the past year several advances in the development of adaptive control for non‐linear systems have made it clear that algorithms not using error normalization have several significant advantages over algorithms which do, in spite of their much greater complexity. In this paper simple examples are used to give a detailed account of the essential differences in structure between adaptive algorithms of the normalized and unnormalized types. It is explained why only the latter readily generalize to non‐linear process models with non‐global Lipschitz non‐linearities. It is shown that one of these algorithms is capable of stabilizing linear process models with different relative degrees.
Adaptive regulation: Lyapunov design with a growth condition
Authors: Praly Laurent 
Published: July 2007

AbstractWe propose a new Lyapunov design of an adaptive regulator under some restriction on the dependence of a Lyapunov function on the parameters.This restriction has been introduced by Praly et al. Its interest is to involve only a Lyapunov function and not explicitly the system non‐linearities. We show it is satisfied by strict pure feedback systems with polynomial growth non‐linearities and some other non‐feedback linearizable systems.Our new Lyapunov design leads to an adaptive regulator where the adapted parameter vector is transformed before being used in the control law; namely, the so‐called certainty equivalence principle is not applied. Unfortunately, the implementation of this regulator needs the explicit solution of a fixed point problem, so in a second stage we propose a more practical solution obtained by replacing the fixed point static equation by a dynamical system with this fixed point as equilibrium.
Adaptive instrumental variable methods for frequency estimation
Authors: Kangas Ari , Stoica Petre , Söderström Torsten 
Published: July 2007

AbstractThis paper considers adaptive instrumental variable methods (IVMs) for estimating the frequencies of noise‐corrupted sinusoidal signals. Adaptive implementations of both the unweighted and the optimally weighted IVMs are given. Since the optimal weighting matrix depends on the (unknown) signal parameters, we examine ways to estimate this quantity on‐line. A theoretical and an experimental study show that the optimally weighted IVM gives more accurate and less biased estimates than the unweighted IVM. The improved accuracy is obtained at the expense of increasing computational complexity.The IVMs determine the AR part of an exactly parametrized ARMA description of the sinusoids‐in‐noise process. A fast, adaptive algorithm to estimate the frequencies from the AR coefficients is proposed. The method is based on simultaneous estimation of all roots of the AR polynomial.
The α‐adaptive performance in pole assignment adaptive controller design
Authors: Wang L. , Owens D. H. 
Published: July 2007

AbstractChoice of the desired closed‐loop polynomial plays an important role in pole assignment adaptive controller design, but it is difficult to specify a ‘good’ closed‐loop polynomial before the process is known. This paper suggests the desired closed‐loop polynomial to be chosen as a function of the identified model so that the gain of the adaptive controller becomes ‘controllable’ by the designer. Simulation examples are used to show that the proposed strategy has advantages over the conventionally fixed closed‐loop polynomial in both design procedure and performance achievement.
Discretization of analogue filters via H∞ model‐matching theory
Authors: Toivonen H. T. 
Published: July 2007

AbstractApproximation of analogue filters by digital filters is performed using H∞ model‐matching theory. In this approach the input signal is assumed to belong to a frequency‐weighted ball in the Lebesgue space L2 of continuous square‐integrable signals and a digital filter is designed so as to minimize the norm of the worst error between the outputs of the digital and analogue filters. An analysis of the frequency response shows that if the set of input signals is sufficiently band‐limited, the procedure corresponds to the minimization of a weighted minimax frequency response error criterion. Numerical examples show that the approach offers an efficient procedure for discretizing general multivariable systems.
Hybrid adaptive‐robust control for a robot manipulator
Authors: Dawson D. M. , Qu Z. , Lewis F. L. 
Published: July 2007

AbstractIn this paper we present a hybrid adaptive‐robust controller to achieve trajectory following for a robot manipulator. The controller consists of two parts: a proportional‐derivative (PD) feedback loop and an adaptive‐robust law for the manipulator dynamics. An advantage of this method is that our controller takes advantage of the manipulator dynamic structure to allow the designer to select a controller that is a combination of an adaptive approach and a robust‐adaptive approach.
Adaptive control of non‐completely controlled mechanical systems using dynamic feedback linearization and estimation design
Authors: Boustany F. , D'Andréa‐Novel B. 
Published: July 2007

AbstractWe propose here an indirect adaptive scheme for a class of non‐completely controlled mechanical systems comprising a classical manipulator terminated by a simple pendulum. In the case of known parameters a dynamic state feedback produces full linearization. An adaptive version is obtained by a simple estimation method together with a certainty equivalence law. Global stability is proved without growth conditions. Simulation results for an overhead crane are given.
A skid‐to‐turn missile autopilot design: The generalized predictive adaptive control approach
Authors: Bendotti P. , M'Saad M. 
Published: July 2007

AbstractThis paper deals with an adaptive control application involving a skid‐to‐turn (STT) missile. More specifically, the generalized predictive adaptive control approach is used to accommodate the yaw channel control problem. The time domain behaviour and stability margins are shown to demonstrate the adaptive control performances vis‐a‐vis those of the underlying fixed parameter control.
Implementation of self‐tuning generalized predictive controllers for the process industry
Authors: Camacho Eduardo F. , Bordons Carlos 
Published: July 2007

AbstractThis paper presents a set of simple Ziegler‐Nichols‐type functions for implementing generalized predictive controllers (GPCs). By using this set of functions, the implementation of GPCs is simplified considerably and the heavy computational requirements needed are avoided. The functions have been obtained for a CARIMA model that is valid for most processes in industry, as is the case of plants whose behaviour can be described in terms of a static gain, time constant and effective dead time.
Non‐linear learning control of robot manipulators without requiring acceleration measurement
Authors: Qu Zhihua , Zhuang Hanqi 
Published: July 2007

AbstractA new class of non‐linear learning control laws is introduced for a robot manipulator to track a given trajectory in performing a series of tasks. The learning control scheme is applicable to robots with both resolute and prismatic joints, requires only position and velocity feedback, and removes the acceleration measurement required by the existing results. It has been shown that under the proposed learning control the tracking errors are always guaranteed to be asymptotically stable with respect to the number of trials. The proposed control is robust in the sense that exact knowledge about the non‐linear dynamics is not required except for bounding functions on the magnitudes. In addition, the new learning scheme can be used without assumptions such as repeatability of robot motion, repeatability of tasks and resetting of initial tracking errors.
Multi‐window spectrum estimation — A linear algebraic approach
Authors: Onn Ruth , Steinhardt Allan O. 
Published: July 2007

AbstractThe purpose of this paper is to elucidate the multi‐window method for spectral estimation and sinusoid detection. The multi‐window method, devised by D. J. Thomson, has many features that distinguish it from other spectrum estimation techniques. It has an elegant derivation, starting from first principles and using optimality arguments. It lends itself to performance analysis and provides a built‐in measure of its reliability. In contrast to Thomson's integral equation derivation, we use linear algebraic methods both to treat the problem and to develop the solution. In addition to the tutorial, we present an overview of published generalizations and analyses of the method.