Abstract
State Estimation Based Inverse Dynamics Controller (SEBIDC), which utilizes an Artificial Neural Network (ANN) based state estimation scheme for nonlinear autonomous hybrid systems which are subjected to state disturbances and measurement noises that are stochastic in nature.
Highlights
Systems in which it may be required to model inherent process discontinuities, where the continuous behavior is drastically changed, or use actuators and sensors which are often fundamentally discontinuous, or use discrete events that can be a useful abstraction to model various mode switching used in the specification and control of the basically continuous process, require hybrid systems modeling and control approach ([1], [2], [3] and [4])
As in the case of extended Kalman filter (EKF) and Unscented Kalman Filter (UKF), Artificial Neural Network (ANN) based state estimation is recursive in nature. Even though it has the same framework of Kalman filter based state estimator, it is designed for eliminating the analytical and statistical linearization [14] used in the case of EKF and UKF
From Table.5, it can be seen that the Integral Square Error (ISE) is improved to 0.0276 from 0.9231 for level h1 and to 0.0228 from 1.0579 for level h2 when compared to best existing related work based on UKF
Summary
Systems in which it may be required to model inherent process discontinuities, where the continuous behavior is drastically changed, or use actuators and sensors which are often fundamentally discontinuous, or use discrete events that can be a useful abstraction to model various mode switching used in the specification and control of the basically continuous process, require hybrid systems modeling and control approach ([1], [2], [3] and [4]). In this approach, the estimated states are obtained by using Taylor series expansion of the nonlinear state transition operator. The estimated states are obtained by using Taylor series expansion of the nonlinear state transition operator It requires analytical computation of Jacobians at each time step, which is considered to be computationally demanding for complex nonlinear systems. An inverse dynamics controller is utilized for controlling the non-measurable states of the system so that the computational burden is very much reduced when compared with the model predictive control scheme implemented in [7], [12] and [13] without compromising the performance.
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