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This paper introduces a methodology of multiscale system identification using wavelet basis functions. Being specific, it deals with subspace identification, for approximation of any multiscale process by employing a number of linear time invariant models at different scales. The idea is to estimate low dimensional state-space models in projection space at appropriate scales. The efficacy of proposed approach has been demonstrated by modeling a nuclear reactor coupled with thermal hydraulics in prediction as well as in simulation framework. Outcome of the multiscale subspace modeling approach is compared with that in single scale to bring out the advantage of the proposed method at different signal to noise ratios. In case of multiscale subspace process identification, the mean squared error in output prediction is found to be small, which suggests improvement in modeling. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a method for fault detection and diagnosis (FDD) of real time systems has been developed. A modeling framework termed as real time discrete event system (RTDES) model is presented and a mechanism for FDD of the same has been developed. The use of RTDES framework for FDD is an extension of the works reported in the discrete event system (DES) literature, which are based on finite state machines (FSM). FDD of RTDES models are suited for real time systems because of their capability of representing timing faults leading to failures in terms of erroneous delays and deadlines, which FSM-based ones cannot address. The concept of measurement restriction of variables is introduced for RTDES and the consequent equivalence of states and indistinguishability of transitions have been characterized. Faults are modeled in terms of an unmeasurable condition variable in the state map. Diagnosability is defined and the procedure of constructing a diagnoser is provided. A checkable property of the diagnoser is shown to be a necessary and sufficient condition for diagnosability. The methodology is illustrated with an example of a hydraulic cylinder. [Copyright &y& Elsevier]

Online estimation of internal states and parameters is often required for process monitoring, fault diagnosis and real time optimization. The conventional approach to tracking drifting parameters is to model their variations as a random walk process and estimate them simultaneously with the states. However, tuning of the random walk model is not a trivial exercise. Recently, moving window based simultaneous state and parameter estimation schemes have been proposed under the assumption that the parameters change slowly and remain constant within the window. These formulations make use of the conventional recursive Bayesian estimators or moving horizon estimator (MHE) for state estimation. While simultaneous smoothing of states along with filtering gives an edge to MHE over the conventional recursive estimators, MHE is computation intensive scheme. Recently, developed Receding-horizon Nonlinear Kalman (RNK) filter combines advantages of MHE (simultaneous smoothing) and recursive estimators (less computation time). In this work, we extend the RNK formulation to carry out simultaneous state and parameter estimation schemes under the assumption that parameters change at a slow rate and remain constant within the RNK window. Two maximum likelihood (ML) based parameter estimation schemes are developed by constructing partial likelihood and complete likelihood functions. Both formulations are further extended under the Bayesian framework to derive their maximum a-posteriori (MAP) versions by incorporating prior information of the parameters. The only tuning parameter in the proposed parameter estimation schemes is the window size, which is relatively easy to select. The efficacies of the proposed formulations are evaluated using simulation studies on two reactor systems and experimental data obtained from the benchmark quadruple tank system. Simulation studies reveal that the complete likelihood based estimation schemes have an edge over the partial likelihood based estimation schemes. Also, the proposed estimation schemes generate reasonably accurate state and parameter estimates with a significant reduction in the average computation time. • Developed RNK based simultaneous and sequential state and parameter estimators • Estimation problem formulation by minimizing partial & complete likelihood functions • Developed maximum likelihood and maximum a-posteriori based estimation formulations • Evaluated efficacy of the estimators using simulation studies and experimental data [ABSTRACT FROM AUTHOR]

Energy management of Parallel Hybrid Electric Vehicles (PHEVs) involves computation of energy efficient torque-speed set-points for the engine and the motor. Application of Nonlinear Model Predictive Control (NMPC) for this problem has been reported in the recent literature. In this work, a fast iterative implementation of the NMPC, known as Model Predictive Static Programming (MPSP) is deployed for the first time for energy management of PHEVs, with an appropriate multi-objective cost function and the applicable equality and inequality constraints. Additionally, a systematic approach based on the Theory of Dominance is used to tune parameters of the controller efficiently. Performance is evaluated with a validated PHEV model on the ADVISOR simulation environment, in terms of various performance metrics on standard drive cycles. The results show that the performance of the MPSP-based technique is close to the ideal performance achieved with Dynamic Programming (DP) and improved over a standard implementation using the Linear Time-Varying MPC (LTV-MPC) and Sequential Quadratic Programming MPC (SQP-MPC) algorithm. Moreover, its execution time on an industry standard micro-controller board is seen to be significantly less than that of LTV-MPC and SQP-MPC. This algorithm therefore appears to be one of the best candidates for on-board vehicle implementation of an optimal energy management strategy for a PHEV. • A new improved energy management strategy for a Parallel HEV based on the MPSP framework. • Numerical implementation showing improved performance over a contemporary MPC algorithm. • Low execution time and feasibility of an on-board implementation. • A new effective approach for tuning the algorithm. [ABSTRACT FROM AUTHOR]