-This paper proposes autonomous hybrid system (AHS) with estimator based inverse dynamics controller along with extended kalman filter and artificial neural network based state estimators ensuring best performance and robustness by minimum ISE in controlling non-measurable state variables of autonomous hybrid systems. With the help of experimental setup of benchmark model of AHS (hybrid three-tank system), the detailed performance comparison of these proposed methods was made both qualitatively and quantitatively with that of existing method (AHS with unscented kalman filter and inverse dynamics controller (UKFC)) under different operating conditions such as servo and regulatory operations, process-model parameter uncertainties, initial condition mismatch, and different types of faults in the system, subsequently the results are reported.
In this paper, the authors have proposed an estimation-based control scheme. Artificial neural network and unscented Kalman filter are used for state estimation and inverse dynamics controller is implemented that utilizes the estimation of unmeasured state variables. The benchmark example taken for the analysis and implementation is nonlinear autonomous three-tank hybrid system and non-autonomous switched mode non-isothermal continuous stirred tank reactor, which are subjected to state and measurement noise. The performance comparison of both proposed estimation-based control scheme has been carried out and results are presented. Further, the proposed scheme is validated with three-tank hybrid system experimental setup.
A novel artificial neural network based state estimator has been proposed to ensure the robustness in the state estimation of autonomous switching hybrid systems under various uncertainties. Taking the autonomous switching three-tank system as benchmark hybrid model working under various additive and multiplicative uncertainties such as process noise, measurement error, process–model parameter variation, initial state mismatch, and hand valve faults, real-time performance evaluation by the comparison of it with other state estimators such as extended Kalman filter and unscented Kalman Filter was carried out. The experimental results reported with the proposed approach show considerable improvement in the robustness in performance under the considered uncertainties.
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