Mehdi Gholami

On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system.

A Clustering-Based Bounded-Error Approach for Identification of PWA Hybrid Systems

A new bounded-error approach for the identification of discrete time hybrid systems in the piece-wise affine (PWA) form is introduced. The PWA identification problem involves the estimation of the number of affine submodels, the parameters of affine submodels and the partition of the PWA map from data. By imposing a bound on the identification error, we formulate the PWA identification problem as a MIN PFS problem (partition into a minimum number of feasible subsystems) and propose a greedy clustering-based method for tackling it. The proposed approach yields to better results than the greedy randomized relaxation algorithm used in previous methods. Also, it is not sensitive to the overestimation of model orders and changes in the tuning parameters and therefore finding a right combination of the tuning parameters of the algorithm to get a model with prescribed bounded prediction error is simple

Active fault diagnosis for hybrid systems based on sensitivity analysis and EKF

An active fault diagnosis (AFD) approach for different kinds of faults is proposed. The AFD approach excites the system by injecting a so-called excitation input. The input is designed off-line based on a sensitivity analysis in order that the maximum sensitivity for each individual system parameter is obtained. Using the maximum sensitivity results in better precision in the estimation of the corresponding parameter. The fault detection and isolation is done by comparing the nominal parameters with those estimated by an extended Kalman filter. In this study, Gaussian noise is used as the input disturbance as well as the measurement noise for simulation. This method is implemented on a large scale livestock hybrid ventilation model which was obtained during previous research.

Stochastic Stability of Markovianly Switched Systems

This technical note examines the stochastic stability of noisy dynamics in discrete and continuous time. The notion of moment stability in the wide sense (MSWS) is presented as a generalization of ϵ-moment stability. MSWS is intentionally not based on stochastic convergence properties, since in most practically appearing systems convergence to any equilibrium is not present. A sufficient criterion for both MSWS and ergodicity is presented for a class of systems comprising a finite set of noisy dynamical systems among which switching is governed by a Markov chain. Stability/instability properties for each separate subsystem are assumed to be quantified by a Lyapunov function candidate together with an associated growth rate equation. For the set of Lyapunov functions, a compatibility criterion is assumed to be fulfilled, bounding the ratio between pairs of Lyapunov functions.

Passive Fault Tolerant Control of Piecewise Affine Systems Based on H Infinity Synthesis

Abstract In this paper we design a passive fault tolerant controller against actuator faults for discretetime piecewise affine (PWA) systems. By using dissipativity theory and H ∞ analysis, fault tolerant state feedback controller design is expressed as a set of linear matrix inequalities (LMIs). In the current paper, the PWA system switches not only due to the state but also due to the control input. The method is applied on a large scale livestock ventilation model.

Active fault diagnosis for hybrid systems based on sensitivity analysis and adaptive filter

An active fault diagnostic (AFD) approach for diagnosis of actuator faults is proposed. The AFD approach excites the system by injecting a so-called excitation input. Here, the input is designed off-line based on sensitivity analysis such that the maximum sensitivity for each individual system parameter is obtained. Using maximum sensitivity, results in a better precision in the estimation of the corresponding parameter. The fault detection and isolation is done by comparing the nominal parameters with those estimated by an adaptive filter. Gaussian noise is used as the input disturbance as well as the measurement noise for simulation. The method is implemented and demonstrated on the large scale livestock hybrid ventilation model which was obtained during previous research.

Recursive Identification of Piecewise Affine Hybrid Systems

In this paper we propose a procedure for recursive identification of discrete time piecewise affine (PWA) hybrid systems. The PWA identification problem involves the estimation of both the parameters of affine submodels and the partition of the PWA map from data. In this paper the submodel parameters estimation problem is solved via recursive k-plane clustering algorithm and the problem of region estimation is performed by incremental proximal support vector machine. Also, the effect of mode switching on the estimated parameters convergence is investigated

Passive fault tolerant control of piecewise affine systems with reference tracking and input constraints

A passive fault tolerant controller (PFTC) based on state feedbac is proposed for discrete-time piecewise affine (PWA) systems. The controller is tolerant against actuator faults and is able to track the reference signal while the control inputs are bounded. The PFTC problem is transformed into feasibility of a set of linear matrix inequalities (LMIs). The method is applied on a large-scale live-stock ventilation model.

Reconfigurability of Piecewise Affine Systems Against Actuator Faults

Abstract In this paper, we consider the problem of reconfigurability of peicewise affine (PWA) systems. Actuator faults are considered. A system subject to a fault is considered as reconfigurable if it can be stabilized by a state feedback controller and the optimal cost of the performance of the systems is admissible. Sufficient conditions for reconfigurability are derived in terms of feasibility of a set of Linear Matrix Inequalities (LMIs). The method is implemented on a large scale livestock hybrid ventilation model which was obtained during previous research.

Multi-Zone hybrid model for failure detection of the stable ventilation systems

In this paper, a conceptual multi-zone model for climate control of a live stock building is elaborated. The main challenge of this research is to estimate the parameters of a nonlinear hybrid model. A recursive estimation algorithm, the Extended Kalman Filter (EKF) is implemented for estimation. Since the EKF is sensitive to the initial guess, in the following the estimation process is split up into simple parts and approximate parameters are found with a non recursive least squares method in order to provide good initial values. Results based on experiments from a real life stable facility are presented at the end.