The decoupled control of torque and flux has made field-oriented control an attractive choice for high performance induction motor drives. However, changes in the speed tracking trajectory and external disturbances make it difficult to achieve an acceptable closed-loop tracking performance, especially when traditional linear controllers are used. This paper addresses this issue by applying dire...

Abstract: An indirect adaptive neural control with Integral-Plus-State (IPS) action, is proposed. The control scheme contain one identification and state estimation Recurrent Trainable Neural Network. The identified plant parameters and the estimated state vector are used to compute an adaptive IPS control. Two control schemes are proposed, containing one or two integrals in the control law. Th...

This paper introduces a new application of adaptive control theory to power control in a code division multiple access (CDMA) cellular system operating over mobile fading radio channels. Conventional feedback power control algorithms allow the base station to send a power command to either raise or lower each user’s transmission power according to a bang-bang-like control policy. In this paper,...

The paper proposed to use recurrent Fuzzy-Neural Multi-Model (FNMM) identifier for decentralized identification of a distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is used as a plant data generator. It is reduced to a lumped system using the ort...

A comparative study of various control systems using neural networks is done. The paper proposes to use a Recurrent Trainable Neural Network (RTNN) identifier with backpropagation method of learning. Two methods of adaptive neural control with integral plus state action are applied – an indirect and a direct trajectory tracking control. The first one is the indirect Sliding Mode Control (SMC) w...

In this paper, an adaptive backstepping control system with a fuzzy integral action is proposed to control the mover position of a linear induction motor (LIM) drive. First, the indirect field oriented control LIM is derived. Then, an integral backstepping design for indirect field oriented control of LIM is proposed to compensate the uncertainties which occur in the control. Finally, the adapt...

In this paper, a decentralized group formation algorithm for task allocation in complex adaptive systems is proposed. Compared with current related works, this decentralized algorithm takes system architectures into account and allows not only neighboring agents but also indirect linked agents in the system to help with a task. A system adaptation strategy is also developed for discovering effe...

In this paper we propose an almost optimal indirect adaptive controller for input/state dynamical systems. The control part of the adaptive scheme is based o n a modified L Q control law: by adding a time varying gain to the certainty equivalent control law we avoid the conflict between identification and control.

This paper presents a hybrid neural net adaptive control method for damaged aircraft. The hybrid method utilizes an adaptive scheme that adjusts a dynamic inversion control model to account for damaged plant dynamics. The model inversion adaptive scheme is based on two approaches: 1) an indirect adaptive law based on the Lyapunov theory, and 2) a recursive least squares method for parameter est...

The paper is concerned with stable adaptive schemes for semi-active control of suspension systems, which can deal with uncertainties in a nonlinear model of MR damper. To compensate for unknown nonlinear hysteresis dynamics of the MR damper, an adaptive inverse controller is implemented by indirect and direct methods. In the indirect method, on-line identification of a forward model of MR dampe...