Showing papers in "International Journal of Adaptive Control and Signal Processing in 1997"
TL;DR: In this article, a model-based adaptive friction compensation on a DC motor servomechanism is proposed to deal with structured normal forces and temperature variations, assuming that a nominal friction model is known and that the friction variations can be suitably structured.
Abstract: This paper illustrates the application of a model-based adaptive friction compensation on a DC motor servomechanism. The dynamic friction model and the control structure studied previously by the authors were used as a basis for this study. The paper first proposes a two-step off-line method to estimate the nominal static and dynamic parameters associated with the model. Then two adaptive globally stable mechanisms are introduced to deal with structured normal forces and temperature variations. Assuming that a nominal friction model is known and that the friction variations can be suitably structured, adaptation is performed on the basis of only one parameter. The paper presents experimental results validating the identification of the dynamic friction model and the adaptive control scheme. These results show that the adaptive loop improves over a fixed compensation scheme and over a PID controller without friction compensation. © 1997 by John Wiley & Sons, Ltd.
466 citations
TL;DR: In this article, a model based on phase plane analysis is derived for an elastic shaft with internal damping connected to a backlash and extended to include the case of a flexible coupling, where some of the backlash gap is filled with rubber.
Abstract: Backlash is the most important non-linearity that limits the performance of speed control in industrial drives and is an important impediment to position control as well. A new simple model based on phase plane analysis is derived for an elastic shaft with internal damping connected to a backlash The model is extended to include the case of a so-called flexible coupling, where some of the backlash gap is filled with rubber. Extensive measurements on an industrial drive with a flexible coupling are compared with simulations and fit very well. With internal damping the classical dead-zone model gives an unphysical behaviour of the dynamics. However, the new model converges to the classical dead-zone model when the damping tends to zero. © 1997 by John Wiley & Sons, Ltd.
173 citations
TL;DR: In this paper, a temperature control in an industrial-scale distributed collector solar field is discussed. But the control problem consists of keeping constant the field outlet oil temperature by acting on the oil flow, which is chosen so as to minimize a receding horizon quadratic cost.
Abstract: This paper deals with temperature control in an industrial-scale distributed collector solar field. The need for adaptive control arises from the highly time-varying behaviour of both the plant dynamics and the solar radiation effects in the collector field due to atmospheric changes. The control problem consists of keeping constant the field outlet oil temperature by acting on the oil flow. The manipulated variable is chosen so as to minimize a receding horizon quadratic cost. The underlying control law is of model-based predictive control type, adaptively implemented by a MUSMAR algorithm, modified so as to exploit the information conveyed by the accessible disturbances. Experimental results are reported showing the significance on the achievable performance of the feedforward term depending on the accessible disturbances. © 1997 John Wiley & Sons, Ltd.
79 citations
TL;DR: In this article, a robust adaptive control scheme for a distributed collector field of a solar power plant is presented. But the main characteristic of solar power plants is that the primary energy source, solar radiation, cannot be manipulated.
Abstract: This paper presents the application of a robust adaptive control scheme to a distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong disturbances in the process. The controller uses a robust identification mechanism combined with a finite horizon receding controller to cope with the process dynamics having bounded uncertainties. The controller has been tested using a proven non-linear computer model of the field. Results obtained in the real plant are also shown. © 1997 John Wiley & Sons, Ltd.
77 citations
TL;DR: In this paper, an adaptive controller is presented for continuous time and discrete time plants, which results in global stability if the plant is open loop stable and minimum phase, and local stability otherwise.
Abstract: Saturation non-linearities are perhaps the most commonly present type of non-linearities in dynamic systems. It is therefore important for the controller present to function satisfactorily in the presence of input saturation. In this paper we present an adaptive controller which leads to satisfactory performance in the presence of magnitude saturation of the control input. For both continuous time and discrete time plants, the adaptive controller is shown to result in global stability if the plant is open loop stable and minimum phase, and local stability otherwise. Robustness properties of the resulting adaptive controller are established. The performance is verified through experimental results obtained from adaptive control of a precision machine tool axis. © 1997 by John Wiley & Sons, Ltd.
60 citations
TL;DR: In this paper, the integrator backstepping technique was used for the control of rigid link, electrically driven robot manipulators in the presence of arbitrary uncertain manipulator inertia parameters and actuator parameters.
Abstract: By using the integrator backstepping technique, the control of rigid link, electrically driven robot manipulators is addressed in the presence of arbitrary uncertain manipulator inertia parameters and actuator parameters. The control scheme developed is computationally simple owing to the avoidance of the derivative computation of the regressor matrix. Semiglobal asymptotic stability of the controller is established in the Lyapunov sense. Simulation results are included to demonstrate the tracking performance.
49 citations
TL;DR: In this article, an adaptive modal positive position feedback (AMPPF) method is presented for controlling the vibration and shape of flexible structures, which combines the attractive attributes of the independent modal space control (IMSC) of Meirovitch and the Positive Position Feedback (PPF), and the adaptation laws governing the stable variation in the AMPPF controller parameters are derived using the Lyapunov stability theorem.
Abstract: An adaptive modal positive position feedback (AMPPF) method is presented for controlling the vibration and shape of flexible structures. The proposed strategy combines the attractive attributes of the independent modal space control (IMSC) of Meirovitch and the positive position feedback (PPF) of Goh and Caughey. The controller is designed in the uncoupled modal space using only modal position signals to damp the vibration of undamped modes. The parameters of the AMPPF controller are also adjusted in an adaptive manner in order to follow the performance of an optimal reference model. In this way, optimal damping and zero steady state errors can be achieved even in the presence of uncertain or changing structural parameters. The adaptation laws governing the stable variation in the AMPPF controller parameters are derived using the Lyapunov stability theorem. The effectiveness of the AMPPF in controlling the vibration and shape of a variable mass cantilevered beam is demonstrated experimentally. The performance obtained with the AMPPF algorithm is compared with those of other classical control algorithms. The results obtained emphasize the potential of the AMPPF algorithm as an efficient means for controlling flexible structures with uncertainties in real time. © 1997 by John Wiley & Sons, Ltd
42 citations
TL;DR: In this paper, a non-adaptive non-linear controller is designed using a modified input-output linearization technique which accounts for the implicit output equation in the reaction invariant model.
Abstract: Adaptive non-linear control strategies for a pH neutralization process are developed and evaluated via simulation. A non-adaptive non-linear controller is designed using a modified input–output linearization technique which accounts for the implicit output equation in the reaction invariant model. For simplicity the reaction invariants are assumed to be available for feedback. Because the model exhibits significant time-varying behaviour, the input–output linearizing controller is combined with non-linear parameter estimators which account for unmeasured buffering changes. Simulation results demonstrate that a novel indirect adaptive strategy is most suitable for experimental implementation where the reaction invariants must be estimated and sampling is required. © 1997 by John Wiley & Sons, Ltd.
39 citations
TL;DR: In this paper, an indirect adaptive output feedback control result which applies to discrete time systems containing a dead-zone nonlinearity and linear dynamics is presented, where a plant parametrization that is linear in a set of unknown parameters is first developed.
Abstract: In this paper an indirect adaptive output feedback control result which applies to discrete time systems containing a dead-zone nonlinearity and linear dynamics is presented. A plant parametrization that is linear in a set of unknown parameters is first developed. This is accomplished by forcing the regional descriptions of the dead-zone to be entirely contained in the definition of a regressor. This is convenient for estimation, since adaptation for the parameters of the separate regions of the non-linearities is implicitly accommodated by the regressor. Unfortunately, this parametrization necessitates multiple estimates of the zeros of the plant. To overcome the difficulty of designing a feedback/feedforward controller given multiple estimates of the zeros, we implement a non-linear 'zero-resolving' prefilter. This prefilter to the plant allows one to design a feedback/feedforward controller based on one set of zeros.
24 citations
TL;DR: In this article, the authors consider adaptive control of systems containing non-smooth non-linear friction forces and divide the unknown friction force into two parts so that it can be parametrized by a model linear in parameters.
Abstract: This paper considers adaptive controls of systems containing non-smooth non-linear friction forces. The idea is to divide the unknown friction force into two parts so that it can be parametrized by a model linear in parameters. Then linear adaptive techniques apply. © 1997 by John Wiley & Sons, Ltd.
23 citations
TL;DR: In this article, the adaptive inverse approach employs an adaptive controller structure consisting of an adaptive inverse for cancelling the effect of an unknown nonlinearity and a fixed (or adaptive) linear control law for a known (or unknown) linear dynamics.
Abstract: In this paper we unify our recent results in adaptive control of systems with unknown non-smooth non-linearities such as dead-zone, backlash and hysteresis characteristics at the input or output of a linear dynamics. Our adaptive inverse approach employs an adaptive controller structure consisting of an adaptive inverse for cancelling the effect of an unknown non-linearity and a fixed (or adaptive) linear control law for a known (or unknown) linear dynamics. Despite the bilinear dependence on the unknown parameters, a linearly parametrized error system is constructed which enables us to design robust adaptive laws for updating the controller parameters to ensure closed loop signal boundedness and improve system tracking performance. © 1997 by John Wiley & Sons, Ltd.
TL;DR: In this article, an adaptive predictive control system (APCS) was applied to the control and optimization of the processes in a coal power station, and the results obtained, corresponding to a Research and Development project at the Pasajes de San Juan power station in Spain, demonstrate the suitability of the APCS application in this field as well as the important benefits that may be derived from it.
Abstract: This paper presents the summary application of an adaptive predictive control system (APCS) to the control and optimization of the processes in a coal power station. The results obtained, corresponding to a Research and Development project at the Pasajes de San Juan power station, which belongs to the company Iberdrola, demonstrate the suitability of the APCS application in this field as well as the important benefits that may be derived from it. © 1997 John Wiley & Sons, Ltd.
TL;DR: In this article, an indirect adaptive control for a discrete-time non-linear system that is fully input-output linearizable is developed, where unknown parameters of the system are identified by using a multi-output RLS algorithm.
Abstract: An indirect adaptive control for a discrete-time non-linear system that is fully input–output linearizable is developed. The unknown parameters of the system are identified by using a multi-output RLS algorithm. Based on the certainty equivalence principle, the estimated parameters are then utilized in the controller design. Stability of the adaptively controlled closed-loop system is shown by using the Lyapunov method. Numerical simulations are included to illustrated the performance of the proposed strategy. © 1997 John Wiley & Sons, Ltd.
TL;DR: The MIMO adaptive GPC is shown to have good regulatory plus servo-tracking properties and the extension of this algorithm by incorporating a variable forgetting factor with a lower bound in its value is implemented on real plant data to demonstrate 'alertness' of the estimator.
Abstract: This paper deals with on-line identification and constrained long-range predictive control of multivariable systems. It extends a recently proposed augmented upper diagonal factorization identification (AUDI) algorithm to identify input-output models of multivariable systems with distinct time delays. The multi-input, multi-output (MIMO AUDI) algorithm can simultaneously identify the process model order and process parameters. The MIMO AUDI algorithm is implemented by decomposing a MIMO system into as many multi-input, single-output (MISO) subsystems as the number of outputs and then identifying each MISO subsystem separately. The performance of the new MIMO AUDI algorithm is demonstrated by application to input-output data from a real process. The extension of this algorithm by incorporating a variable forgetting factor with a lower bound in its value is implemented on real plant data to demonstrate 'alertness' of the estimator. This paper evaluates the performance of the MIMO adaptive generalized predictive control algorithm with and without constraints by experimental application on a computer-interfaced, pilot-scale process. The MIMO adaptive GPC is shown to have good regulatory plus servo-tracking properties.
TL;DR: In this article, a neural-network-based algorithm for non-linear control of chaotic systems is presented, which relies on the method proposed by Ott et al. to stabilize unstable periodic orbits by appropriate small changes in a control parameter.
Abstract: A simple but efficient neural-network-based algorithm for non-linear control of chaotic systems is presented. The scheme relies on the method proposed by Ott et al. (Phys. Rev. Lett.,64, 1196 (1990)) to stabilize unstable periodic orbits by appropriate small changes in a control parameter. In contrast with this, our approach does not make use of an analytical description of the system evolution. The dynamics is evaluated by a self-organizing Kohonen network with an altered learning rule, which is able to learn the map of the system and to determine the positions of unstable periodic orbits of a given period. At the end of learning, a set of control neurons is generated which target the system along a quasi-optimal path towards the orbit. Besides its intrinsic tolerance against weak noise, the main advantage of the algorithm is its ability to take into account system constraints that occur in practical applications. The mean value of the control parameter and the range of allowed changes can be chosen in advance, and if more than one fixed point exists, the algorithm adapts to the most appropriate one concerning the control effort. © 1997 by John Wiley & Sons, Ltd.
TL;DR: In this paper, the authors describe methods for adjusting set membership techniques, which are only applicable to equation error models, to the case of output error models and a method for building the optimal outer-bounding orthotope is also proposed.
Abstract: In the context of set membership identification the feasible parameter set is defined as the set of plant parameters which are consistent with the model structure, the assumptions on (unknown but bounded) disturbances and all available measurements. It appears more convenient in practice to build an outer-bounding set, typically an ellipsoid, a parallelotope or an orthotope. This paper describes methods for adjusting set membership techniques, which are only applicable to equation error models, to the case of output error models. A method for building the optimal outer-bounding orthotope is also proposed. Equation error and output error methods are evaluated on the example of the estimation of a missile state space model in the presence of measurement noise and neglected dynamics.
TL;DR: In this paper, an orthotopic set membership identifier, a robust stabilizing controller and a pole placement controller are proposed for the adaptive robust control design problem for low-dimensional systems.
Abstract: The adaptive robust control design problem for low-dimensional systems is addressed in this paper. The proposed scheme consists of an orthotopic set membership identifier, a robust stabilizing controller and a pole placement controller. The identifier provides the minimum (volume-wise) orthotopic region of parameters which is consistent with the data and model structure. Based on this orthotope, the robust controller computes the feasible set of stabilizing controller gains. Subsequently a pole assignment controller selects a subset from the stabilizing gains in order to place the closed-loop poles deeper inside the unit disc. Simulation studies are offered to validate the efficiecy of the proposed method.
TL;DR: In this article, a simple, practical and unified method is presented for detecting parameter identifiability problems caused by non-persistent excitation, overparametrization and/or output feedback within the system to be identified.
Abstract: A simple, practical and unified method is presented for detecting parameter identifiability problems caused by non-persistent excitation, overparametrization and/or output feedback within the system to be identified. All the required information is generated inherently by the multiple-model least-squares (MMLS) method and/or the augmented UD identification (AUDI) algorithm developed by the authors, so very little extra computation is required. Several examples are included to illustrate the principles involved and their application.
TL;DR: In this paper, the authors summarize the stability results already derived for predictive and adaptive predictive control, discuss them from an intuitive and practical implementation perspective, and illustrate them by means of two simulated examples.
Abstract: This paper summarizes the stability results already derived for predictive and adaptive predictive control, discusses them from an intuitive and practical implementation perspective and, from the same perspective, illustrates them by means of two simulated examples. In this way it recalls the limits of stability when applying predictive control and how they are related to the modelling errors, which may change as the process dynamics changes. Also it recalls how, by adding adaptation to the predictive scheme, this source of instability may be compensated for. Already within the adaptive predictive formulation it considers the limits of stability for different scenarios, particularly when a reduced-order adaptive predictive model cannot account for unmodelled process dynamics.
TL;DR: A global stability proof of the overall adaptive scheme is given using arguments similar to those used in discrete-time direct model reference adaptive control but properly modified to account for the di⁄erent structure of CMRAC with respect to DMRAC.
Abstract: SUMMARY The discrete-time version of continuous-time combined model reference adaptive control (CMRAC) is presented in this paper. A global stability proof of the overall adaptive scheme is given using arguments similar to those used in discrete-time direct model reference adaptive control (DMRAC) but properly modified to account for the di⁄erent structure of CMRAC with respect to DMRAC. ( 1997 by John Wiley & Sons, Ltd.
TL;DR: This paper considers detection of baseband signals in partial response signalling (PRS) systems in the presence of additive, coloured noise and adopts two approaches for designing predictors, which offer substantial performance improvements over conventional detectors.
Abstract: In this paper we consider detection of baseband signals in partial response signalling (PRS) systems in the presence of additive, coloured noise. The additive noise in the system is a mixture of Gaussian noise and impulsive noise modelled as an alpha-stable process. The dependence in observation samples results from the excess bandwidth in the matched filters of the receivers. The detectors proposed are based on a noise estimation-cancellation technique. In particular, by exploiting past decisions as well as past received samples, we estimate the noise and subsequently cancel it. We adopt two approaches for designing predictors: in the first we use a minimum mean square error (MMSE) criterion and we employ Volterra filters as predictors; in the second we use the minimum dispersion (MD) criterion and we limit our attention to linear predictors. The effects of the predictor order, the number of exploited samples and the filtering allocation on the system performance are examined through Monte Carlo simulations. It is demonstrated that the proposed detectors, while having simple structures, offer substantial performance improvements over conventional detectors.
TL;DR: In this article, a model reference composite state feedback control is proposed to guarantee the global exponential stability with any pre-specified convergence rate for a class of model reference control systems with time-varying delay, matched uncertainties and input nonlinearity.
Abstract: In this note the concept of the tracking time, a measure of the transient behaviour, for model reference control systems is introduced. A model reference composite state feedback control is proposed to guarantee the global exponential stability with any pre-specified convergence rate for a class of model reference control systems with time-varying delay, matched uncertainties and input non-linearity. Moreover, an estimate of the tracking time is derived for such systems. A numerical example is also given to illustrate our main result.
TL;DR: In this article, a time series of the future exterior boundary condition, used for the predictive control, is predicted by the Kalman filter technique, and the basic equation of ground temperature is discretized by the finite element method in space and the Crank-Nicolson method in time.
Abstract: Recently, applications of the control of ground temperature have been widely used for the maintenance of lawns and underground storage tanks, road surface temperature control, etc. This paper presents a predictive bang-bang control to perform a real-time and practical control method. A time series of the future exterior boundary condition, used for the predictive control, is predicted by the Kalman filter technique. The basic equation of the ground temperature is discretized by the finite element method in space and the Crank–Nicolson method in time. To obtain the predictive control temperature, the performance function is minimized at every time step by the Sakawa–Shindo method. © 1997 John Wiley & Sons, Ltd.
TL;DR: Adaptive algorithms for parameter estimation of a class of non-linear systems are developed in this paper, and the salient feature of these algorithms is that there is no need to solve any Lyapunov equation, which may prove useful in dealing with high-dimensional systems.
Abstract: Adaptive algorithms for parameter estimation of a class of non-linear systems are developed in this paper. The salient feature of these algorithms is that there is no need to solve any Lyapunov equation, which may prove useful in dealing with high-dimensional systems. Since the algorithms are constructed by using the accessible system signals, the derivatives of these signals are not required. It is shown that the presented algorithms include the existing method as a special case. A numerical example is included to demonstrate the method. © 1997 John Wiley & Sons, Ltd.
TL;DR: In this paper, four different adaptive predictive control schemes, the DMC, GPC, VGPC and DGPC algorithms, are selected for real-time application at the turbogenerator plant.
Abstract: After a short description of a turbogenerator plant, some of the most important general aspects for adaptive predictive control schemes are introduced. Four different adaptive predictive schemes, the DMC, GPC, VGPC and DGPC algorithms, are selected for real-time application at the turbogenerator plant. The results are critically discussed and advantages and disadvantages are highlighted. These investigations show that all applied control algorithms provide good results. However, considering all the different aspects, the DMC and DGPC algorithms seem to be most appropriate for practical applications.
TL;DR: A recursive algorithm for the estimation of transport delay that uses Bayesian probabilistic apparatus to test the probability of each possible value and is a highly modular algorithm which may be implemented on parallel (VLSI) hardware and is hence suitable for applications requiring fast data processing.
Abstract: The paper describes a recursive algorithm for the estimation of transport delay. Based on the assumption that the delay is a discrete quantity with a finite number of values, the algorithm uses Bayesian probabilistic apparatus to test the probability of each possible value. The computation of probabilities uses the output of the recursive modified Gram–Schmidt (RMGS) algorithm. The result is a highly modular algorithm which may be implemented on parallel (VLSI) hardware and is hence suitable for applications requiring fast data processing. © 1997 John Wiley & Sons, Ltd.
TL;DR: In this article, the long-range predictive control strategy is extended to include a terminal matching condition so that the final control law minimizes the squares of prediction errors over a small future prediction horizon and at steady state.
Abstract: The long-range predictive control strategy is extended to include a terminal matching condition so that the final control law minimizes the squares of prediction errors over a small future prediction horizon and at steady state. The weighting on the terminal condition is a better alternative than a large prediction horizon, which requires a heavy computational load. It also leads to the formulation of an approximate long range predictive controller with knowledge of only a few initial step response coefficients and the steady state gain. Evaluations of steady state error weighting show that the weighting provides similar effects to ordinary control weighting but in addition has the stabilizing effects of a large predictive control horizon. An adaptive version of this control strategy has been successfully applied to the control of mean arterial blood pressure. © 1997 by John Wiley & Sons, Ltd.
TL;DR: In this article, the stability of a Kalman filter based on an adaptive estimator in the time average sense for a time-varying stochastic system with correlated noise is obtained under a persistent excitation condition.
Abstract: The stability of a Kalman filter based on an adaptive estimator in the time average sense for a time-varying stochastic system with correlated noise is obtained under a persistent excitation condition. The stabilities of the closed-loop system and estimating error are established by designing an adaptive control law and restricting the growth rates of input and output signals. The stabilities of the extended least squares algorithm with forgetting factor and with covariance modification in the time average sense and sample average sense respectively are obtained.