In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-state estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning frame-work to state estimation problems. Spe...

Jet engines are nonlinear dynamical systems for which an exact mathematical model cannot be used for estimator design, because it is either not available or so complex that it does not fit the necessary assumptions. Thus, classical analytical tools for studying standard system properties like observability, which is very important in estimator design, cannot be directly applied. Generally, for ...

This paper presents a novel nonlinear estimator called the fuzzy finite memory (FFM) state for electro-hydraulic active suspension systems, based on techniques and impulse response. The Takagi-Sugeno model is introduced to effectively describe highly systems with actuator dynamics. Compared conventional estimator, which has an infinite structure requires whole data from initial current time, pr...

The paper addresses the rhythmic stabilization of periodic orbits in a wedge billiard with actuated edges. The output feedback strategy, based on the sole measurement of impact times, results from the combination of a stabilizing state feedback control law and a nonlinear deadbeat state estimator. It is shown that the robustness of both the control law and the observer leads to a simple rhythmi...

A Kalman filter (KF) estimator has been formulated using a sequence of reduced-order models representing a whole batch behavior for providing the estimates of dynamic composition in a ternary batch distillation process operated in an optimal-reflux policy. A set of full-order models is firstly obtained by linearizing around different pseudo-steady state operating conditions along batch optimal ...

In automotive powertrain control strategy, engine torque estimators are preferred to expensive sensors for obvious economic reasons. In the literature several works have been presented proposing both instantaneous engine torque and mean engine torque estimators. The first ones are based on complex models and tipically are suitable for diagnosis purposes, while the latter estimate the torque by ...

Abstract — In this work a state estimator for a continuous bioprocess is presented. To reach this aim a nonlinear filtering technique, based on the recursive application of the Bayes rule and Monte Carlo techniques, is used. To the best of author’s knowledge, not many applications in the biotechnological area applying such techniques have been reported. Generally, a bioprocess has strong nonlin...

This paper introduces a family of blind feedforward nonlinear least-squares (NLS) estimators for joint estimation of the carrier phase and frequency offset of general quadrature amplitude modulated (QAM) transmissions. As an extension of the Viterbi and Viterbi (V&V) estimator, a constellation-dependent optimal matched nonlinear estimator is derived such that its asymptotic (large sample) varia...

The paper introduces a new vision-based range estimator for leader-follower formation control, based upon the Immersion and Invariance (I&I) methodology. The proposed reduced-order nonlinear observer is simple to implement, easy to tune and achieves global asymptotical convergence of the observation error to zero. Observability conditions for the leader-follower system are analytically derived ...