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091 Self-tuning pole-assignment controller with long-range prediction
529
Abstracts
086
Stability of Estimating Errors for Time.Varying ARMAX Systems Ruisheng IA, Ximplng Lai, Huimln Hong, pp 363-366
The stability of the Kalman-filtor-hased parameter estimator for multi-dimensional stochastic time-varying systems with cosrdated noise is established. The main features are: (i) the time-vasying parameter matrix On is adapted to a Markov model representation, On+ I =HOn+en+i, and the known invertible matrix H is only restrictedin that the maximum eigenvalue of H//r is less than or equal to one; (ii)the system noise is a moving average process W.n+ClnW.n.i+ ...+Crnwn.r, where C/n (~1.2,...~')are unknown ume-varymg parameter matrices.
O87
Hopfleld.Based Adaptive Observers: Next Generation of Luenberger State Estimators R. Shoureshi, S. Reynold Chu, pp 367-370
Neural networks have been employed to expand the application areas of the Luenberger state estimators. A Hopfield network is applied to the problem of system identification and state estimation. This paper presents the mathematical derivation of an adaptive observer based on the integration of the two techniques. The general case of multiple-input and -output systems is considered. Analysis includes the case of noise presence in the meamrements. Simulation results for different plant conditions are detailed.
088
Experimental Medelllng of Variable Displacement Radial Pleton Pumps by Using a BI.Directional Computer-Controlled Hydraulic Dynamometer F. Conrad, E. Trostman, M. Zhang, pp 371-374
An experimental modelling of hydraulic pumps based on experimentally obtained data using statistical techniques is proposed, and a bi-diroctional computer comrolled hydraulic dynamometer system is briefly described. Compared to the existing theoreticalmodds, this methed can provide am empirical model with higher accuracy and validation, so that the requirements of system design, simulation and fault diagnosis can be satisfied. A modelling result for a variable-displacement radial piston pump is presented as an example, and is discussed in this paper.
089
Optimal Predictive Control Zhu Kuanyi, pp 375-378
The paper presents a novel predictive control based on a quadratic cost function. The system to be controlled is described by am input-ontput description, and a reference model is chosen that the system will follow. Then the quadratic cost function minimizes a sequence of the tracking errors between the desired future outputs of the reference model and those of the system. The paper presents a new formulation of the future output prediction, which allows recursive solution of the cost function, and the achievement of recursive control algorithms.
O9O Stabilizing Predictive Controllers for a Class of Non Linear Recursive Systems G. Bastin, J.M. Dion, L. Dugard, V. Wetz, pp 379.382 This paper is mainly concemed with the stab'dization properties of one-step weighted p~iicfive controllers 0VPC) for non-linear recursive systems that can be non-linear with respect to both past inputs and outputs , though linear with respect to the most recast input. It is shown that the dosed-lonp stabilityanalysis miles on the analysis of a scalar multistep iterative map. Some illustrative examples are given.
091
Self.Tuning Pole.Assignment Controller with Long. Range Prediction T. Yamamoteb Z.H. Luo, Y. Sakawa, S. Omatu, pp 383-388
It is reported that the generalized predictive controller originally proposed by Clarke, et ad., is useful for use in real plants since it has a long-range prediction scheme. This method, however, does not guarantee the stability of the closed-loop system. This paper presents a pule-assignment control algorithm based on the original generalized predictive control. A priori information about timedelay is not needed in this method. Furthermore, the paper demonstrates the boundedness of input and output signals, by stability analysis. 092
Adaptive Quadratic Control for Stochastic Systems Han-Fu Chen, pp 389-392
For completely observed stochastic systems with unknown coeffidents, adaptive control based on the least squares (IS) estimates is defined so that the quadratic costs are minimized and the LS estimates for unknown parameters are consistently estimated. The feature of the proposed adaptive control scheme is that it requires neither the minimum-phase condition nor stability of the open-loop system. Both discrete- and continuous-time systems are discussed in the paper. 093
Adaptive Control Via a Simple Switching Algorithm JI Feng Zhang, P.E. Calnes, pp 393-396
This paper presents an adaptive stabilization control for systems with unknown constant parameters and stochastic disturbances, which may be neither open-loop stable nor minimum-phase. The ideas come from previous work by the authors, but here the construction procedure of an adaptive control system is simplified. The computational load is also significantly reduced, so the adaptive control presented in this paper is more practical. Furthermore, parameter estimation is carried out in only a fmite time period and, unlike in previous work, the parameter estimates are generated by ordinary differentialequations rather than by stochasticdifferentialequations.
094
Adaptive Control Performance with Time Varying Parameters V. Solo, pp 397-399
This paper discusses performance analysis (as measured by meansquared tracking error) of a simple stochastic adaptive control system with time-varying parameters. Both theoreticalresultsand simulations are presented. 095
Tracking the Output of a Poorly Known Linear System R. Rishel, pp 401-403
The paper considers tracking the output of linear stochastic systems whose parameters am unknown. For long-term discounted quadratic cost, optimal control is shown to depend on estimates of the state of the linear system considered as if the parameters were known, and on the conditional density of the unknown parameters.
O96 An Approach to Adaptive Boundary Control of Linear Stochastic Distributed Parameter Systems T.E. Duncan, B. Pasik-Duncan, pp 405.408 A linear dlstrlbuted-parameter system for the adaptive control problem is modelled by an evolution equation with m infinitesimal generator for an analytic semigroup. The noise in the system is a cylindrical white noise. The unknown parameters appear affinely both in the infmitesimal generator of the semisroup and in the unbounded linear transformation of the control. A family of leastsquares estimates of these parameters is shown to be strongly consistent. The certainty equivalence adaptive control for an ergodic quadratic cost functional is shown to be self-tuning and self-optimizing.
Abstracts
086
Stability of Estimating Errors for Time.Varying ARMAX Systems Ruisheng IA, Ximplng Lai, Huimln Hong, pp 363-366
The stability of the Kalman-filtor-hased parameter estimator for multi-dimensional stochastic time-varying systems with cosrdated noise is established. The main features are: (i) the time-vasying parameter matrix On is adapted to a Markov model representation, On+ I =HOn+en+i, and the known invertible matrix H is only restrictedin that the maximum eigenvalue of H//r is less than or equal to one; (ii)the system noise is a moving average process W.n+ClnW.n.i+ ...+Crnwn.r, where C/n (~1.2,...~')are unknown ume-varymg parameter matrices.
O87
Hopfleld.Based Adaptive Observers: Next Generation of Luenberger State Estimators R. Shoureshi, S. Reynold Chu, pp 367-370
Neural networks have been employed to expand the application areas of the Luenberger state estimators. A Hopfield network is applied to the problem of system identification and state estimation. This paper presents the mathematical derivation of an adaptive observer based on the integration of the two techniques. The general case of multiple-input and -output systems is considered. Analysis includes the case of noise presence in the meamrements. Simulation results for different plant conditions are detailed.
088
Experimental Medelllng of Variable Displacement Radial Pleton Pumps by Using a BI.Directional Computer-Controlled Hydraulic Dynamometer F. Conrad, E. Trostman, M. Zhang, pp 371-374
An experimental modelling of hydraulic pumps based on experimentally obtained data using statistical techniques is proposed, and a bi-diroctional computer comrolled hydraulic dynamometer system is briefly described. Compared to the existing theoreticalmodds, this methed can provide am empirical model with higher accuracy and validation, so that the requirements of system design, simulation and fault diagnosis can be satisfied. A modelling result for a variable-displacement radial piston pump is presented as an example, and is discussed in this paper.
089
Optimal Predictive Control Zhu Kuanyi, pp 375-378
The paper presents a novel predictive control based on a quadratic cost function. The system to be controlled is described by am input-ontput description, and a reference model is chosen that the system will follow. Then the quadratic cost function minimizes a sequence of the tracking errors between the desired future outputs of the reference model and those of the system. The paper presents a new formulation of the future output prediction, which allows recursive solution of the cost function, and the achievement of recursive control algorithms.
O9O Stabilizing Predictive Controllers for a Class of Non Linear Recursive Systems G. Bastin, J.M. Dion, L. Dugard, V. Wetz, pp 379.382 This paper is mainly concemed with the stab'dization properties of one-step weighted p~iicfive controllers 0VPC) for non-linear recursive systems that can be non-linear with respect to both past inputs and outputs , though linear with respect to the most recast input. It is shown that the dosed-lonp stabilityanalysis miles on the analysis of a scalar multistep iterative map. Some illustrative examples are given.
091
Self.Tuning Pole.Assignment Controller with Long. Range Prediction T. Yamamoteb Z.H. Luo, Y. Sakawa, S. Omatu, pp 383-388
It is reported that the generalized predictive controller originally proposed by Clarke, et ad., is useful for use in real plants since it has a long-range prediction scheme. This method, however, does not guarantee the stability of the closed-loop system. This paper presents a pule-assignment control algorithm based on the original generalized predictive control. A priori information about timedelay is not needed in this method. Furthermore, the paper demonstrates the boundedness of input and output signals, by stability analysis. 092
Adaptive Quadratic Control for Stochastic Systems Han-Fu Chen, pp 389-392
For completely observed stochastic systems with unknown coeffidents, adaptive control based on the least squares (IS) estimates is defined so that the quadratic costs are minimized and the LS estimates for unknown parameters are consistently estimated. The feature of the proposed adaptive control scheme is that it requires neither the minimum-phase condition nor stability of the open-loop system. Both discrete- and continuous-time systems are discussed in the paper. 093
Adaptive Control Via a Simple Switching Algorithm JI Feng Zhang, P.E. Calnes, pp 393-396
This paper presents an adaptive stabilization control for systems with unknown constant parameters and stochastic disturbances, which may be neither open-loop stable nor minimum-phase. The ideas come from previous work by the authors, but here the construction procedure of an adaptive control system is simplified. The computational load is also significantly reduced, so the adaptive control presented in this paper is more practical. Furthermore, parameter estimation is carried out in only a fmite time period and, unlike in previous work, the parameter estimates are generated by ordinary differentialequations rather than by stochasticdifferentialequations.
094
Adaptive Control Performance with Time Varying Parameters V. Solo, pp 397-399
This paper discusses performance analysis (as measured by meansquared tracking error) of a simple stochastic adaptive control system with time-varying parameters. Both theoreticalresultsand simulations are presented. 095
Tracking the Output of a Poorly Known Linear System R. Rishel, pp 401-403
The paper considers tracking the output of linear stochastic systems whose parameters am unknown. For long-term discounted quadratic cost, optimal control is shown to depend on estimates of the state of the linear system considered as if the parameters were known, and on the conditional density of the unknown parameters.
O96 An Approach to Adaptive Boundary Control of Linear Stochastic Distributed Parameter Systems T.E. Duncan, B. Pasik-Duncan, pp 405.408 A linear dlstrlbuted-parameter system for the adaptive control problem is modelled by an evolution equation with m infinitesimal generator for an analytic semigroup. The noise in the system is a cylindrical white noise. The unknown parameters appear affinely both in the infmitesimal generator of the semisroup and in the unbounded linear transformation of the control. A family of leastsquares estimates of these parameters is shown to be strongly consistent. The certainty equivalence adaptive control for an ergodic quadratic cost functional is shown to be self-tuning and self-optimizing.