Home About us Contact
|
Home About us Contact | ||
|
|
||
Subspace Identification (subspace + identification)
Selected AbstractsImproved adaptive control for the discrete-time parametric-strict-feedback formINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 12 2009Graciela Adriana González Abstract Adaptive control design for a class of single-input single-output nonlinear discrete-time systems in parametric-strict-feedback form is re-visited. No growth restrictions are assumed on the nonlinearities. The control objective is to achieve tracking of a reference signal. As usual, the algorithm derives from the combination of a control law and a parameter estimator (certainty equivalence principle). The parameter estimator strongly lies on the regressor subspace identification by means of an orthogonalization process. Certain drawbacks of previous schemes are analyzed. Several modifications on them are considered to improve the algorithm complexity, control performance and numerical stability. As a result, an alternative control scheme is proposed. When applied to the proposed class of systems, global boundedness and convergence remain as achieved objectives while improving the performance issues of previous schemes. Copyright © 2009 John Wiley & Sons, Ltd. [source] A tuning algorithm for multivariable restricted structure control systems using subspace identificationINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 9-10 2004A. Sánchez Abstract This paper presents a new method to design a restricted structure controller driving a multivariable plant using subspace identification methods. The algorithm employs data collected from closed-loop operation to identify an open-loop model of the plant using subspace identification. The method also requires knowledge of the first N impulse responses from the controller. The multivariable controller parameters are calculated by minimising a finite horizon LQG criterion subject to nonlinear constraints. Three simulation case studies are presented. Copyright © 2004 John Wiley & Sons, Ltd. [source] Recursive subspace identification based on instrumental variable unconstrained quadratic optimizationINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 9-10 2004G. Mercère The problem of the recursive formulation of the MOESP class of subspace identification algorithms is considered and two novel instrumental variable approaches are introduced. The first one leads to an RLS-like implementation, the second to a gradient type iteration. The relative merits of both approaches are analysed and discussed, while simulation results are used to compare their performance with one of the existing techniques. Copyright © 2004 John Wiley & Sons, Ltd. [source] Evaluation of controller performance,use of models derived by subspace identificationINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 7-9 2003S. Bezergianni Abstract A new approach is presented for the estimation of the controller, process, and disturbance models necessary for the calculation of the relative variance index, which was introduced in an earlier paper (Control Eng. Practice 2000; 8:791,797), for the performance of SISO controllers. It involves the use of dynamically, sufficiently rich segments from the normal operating data and the use of the subspace identification technique to estimate the systems mentioned above. This approach improves the estimation accuracy of the performance index in relation to the method presented previously. The estimated models enable the comparison of the present controller performance with that of optimally tuned PI or IMC controllers. This helps identify the potential benefits of either retuning or redesigning the assessed controller. Copyright © 2002 John Wiley & Sons, Ltd. [source] Closed-loop identification of the time-varying dynamics of variable-speed wind turbinesINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 1 2009J. W. van Wingerden Abstract The trend with offshore wind turbines is to increase the rotor diameter as much as possible to decrease the costs per kWh. The increasing dimensions have led to the relative increase in the loads on the wind turbine structure. Because of the increasing rotor size and the spatial load variations along the blade, it is necessary to react to turbulence in a more detailed way: each blade separately and at several separate radial distances. This combined with the strong nonlinear behavior of wind turbines motivates the need for accurate linear parameter-varying (LPV) models for which advanced control synthesis techniques exist within the robust control framework. In this paper we present a closed-loop LPV identification algorithm that uses dedicated scheduling sequences to identify the rotational dynamics of a wind turbine. We assume that the system undergoes the same time variation several times, which makes it possible to use time-invariant identification methods as the input and the output data are chosen from the same point in the variation of the system. We use time-invariant techniques to identify a number of extended observability matrices and state sequences that are inherent to subspace identification identified in a different state basis. We show that by formulating an intersection problem all states can be reconstructed in a general state basis from which the system matrices can be estimated. The novel algorithm is applied on a wind turbine model operating in closed loop. Copyright © 2008 John Wiley & Sons, Ltd. [source] Input design for model order determination in subspace identificationAICHE JOURNAL, Issue 8 2003Pratik Misra Subspace identification methods require that the inputs to the process being identified be persistently exciting. This may be inadequate for subspace identification of ill-conditioned multivariable processes, because the process model order may be underestimated, leading to subsequent identification of poor models. To remedy the problem, it is proposed that inputs must be used that excite a process to be identified in a way that produces as uncorrelated process outputs as possible. This can be accomplished either in open or in closed-loop fashion. Simulations on a high-purity distillation column and on a heat exchanger illustrate the merit of the proposed approach. [source] Enhanced Performance Assessment of Subspace Model-Based Predictive Controller with Parameters TuningTHE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 4 2007Qiang Zhang Abstract This study focuses on performance assessment of model predictive control. An MPC-achievable benchmark for the unconstrained case is proposed based on closed-loop subspace identification. Two performance measures can be constructed to evaluate the potential benefit to update the new identified model. Potential benefit by tuning the parameter can be found from trade-off curves. Effect of constraints imposed on process variables can be evaluated by the installed controller benchmark. The MPC-achievable benchmark for the constrained case can be estimated via closed-loop simulation provided that constraints are known. Simulation of an industrial example was done using the proposed method. Cette étude porte sur l'évaluation de la performance du contrôle prédictif par modèles (MPC). On propose un banc d'essai adapté au MPC pour le cas non contraint en se basant sur l'identification de sous-espaces en boucle fermée. Deux mesures de performance sont élaborées pour évaluer l'avantage potentiel de mettre à jour le nouveau modèle identifié. L'avantage potentiel par réglage du paramètre peut s'obtenir à partir des courbes de compromis. L'effet des contraintes imposé sur les variables de procédé peut être évalué par le banc d'essai de contrôleur installé. Le banc d'essai adapté au MPC pour le cas contraint peut être estimé par la simulation en boucle fermée dans la mesure où les contraintes sont connues. On a réalisé la simulation d'un exemple industriel à l'aide de la méthode proposée. [source] | |||||||||||||