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Identification and control problems for unknown chaotic dynamical systems are considered. Our aim is to regulate the unknown chaos to a fixed point or a stable periodic orbit. This is realized by following two contributions. First, a dynamic neural network is used as identifier. The weights of the neural networks are adjusted by the sliding mode technique. Second, we derive a local optimal controller via the neuroidentifier to remove the chaos in a system. The identification error and trajectory error are guaranteed to be bounded. The controller proposed in this paper is effective for many chaotic systems, including the Lorenz system, Duffing equation, and Chua's circuit. Index Terms--Chaos, neural networks, identification, sliding mode control, learning.

In this paper, optimal control strategies for temperature trajectory determination in order to maximize thermophilic bacteria in a fed-batch solid-state fermentation reactor are proposed. This process is modeled by nonlinear differential equations, which has been previously validated experimentally with scale reactor temperature profiles. The dynamic input aeration rate of the reactor is determined to increase microorganisms growth of a selective substrate for edible mushroom cultivation. In industrial practice, the process is comprised of three thermal stages with constant input air flow and three types of microorganisms in a 150-hour lapse. Scytalidium thermophilum and actinobacteria are desired in order to obtain a final biomass composition with acceptable microorganisms concentration. The Steepest Descent gradient algorithm in continuous time and the Gradient Projection algorithm in discrete-time are used for the process optimal control. A comparison of simulation results in the presence of disturbances is presented, where the resulting temperature trajectories exhibit similar tendencies as industrial data. [ABSTRACT FROM AUTHOR]

In this paper, a reduced order neural observer (RONO) with a time-varying learning rate is proposed. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. A time-varying learning rate is designed in order to improve the learning of the neuronal network in presence of disturbances and parameter variations. This work includes the stability proof of the time-varying learning. The applicability of the developed observer is illustrated via simulations for a nonlinear anaerobic digestion process. [ABSTRACT FROM AUTHOR]

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