In this paper we present a new approach to design the input control to track the output of a non-minimum phase nonlinear system. Therefore, a ascade control scheme that combines input–output feedback linearization and gradient descent control method is proposed. Therein, input–output eedback linearization forms the inner loop that compensates the nonlinearities in the input–output behavior, and...

A nonlinear dynamic output feedback control method for a 5-degree-offreedom AMB is presented in this paper. This method is based on the system structure of AMB and a quadratic-like Lyapunov function. It is shown that backstepping and completing square techniques enable the construction of the voltage input by using measured output only. The control law guarantees the asymptotic stability of the...

This paper discusses the systematic design of an adaptive feedback linearizing neurocontroller for a high-order model of the synchronous machine/infinite bus power system. The power system is first modelled as an input-output nonlinear discrete-time system approximated by two neural networks. The approach allows a simple linear pole-placement controller (which is itself not a neural network) to...

This paper addresses suboptimal control of nonlinear systems which can be feedbacklinearized from input to output. The case of input-to-state linearizable systems is also covered as a special case. The method is thus applicable to all nonlinear systems which can be partially linearized using the method of output-feedback linearization while having a stable internal (or zero) dynamics. The well-...

unknown noise and artifacts present in medical signals with  non-linear fuzzy filter will be estimate and then removed. an adaptive neuro-fuzzy interference system which has a nonlinear  structure presented  for the noise function prediction by before samples. this paper is about a neuro-fuzzy method to estimate unknown noise of electrocardiogram (ecg) signal. adaptive neural combined with fuzz...

This paper presents an indirect adaptive fuzzy control scheme for a class of single-input-single-output (SISO) nonlinear systems. A Takagi-Sugeno (T-S) fuzzy model is employed as a dynamical model of the partially known nonlinear system. Both the structure and the parameters of the T-S model are identified on-line. A T-S model based feedback linearization controller (FLC) is designed and a Lyap...

In this paper, output tracking control of a helicopter based unmanned aerial vehicle model is investigated. First, based on Newton-Euler equations, a dynamical model is derived by considering the helicopter as a rigid body upon which a set of forces and moments act. Second, we show that the model cannot be converted into a controllable linear system via exact state space linearization. In parti...

This paper presents an adaptive feedback linearization approach to derive helicopter. Ideal feedback linearization is defined for the cases when the system model is known. Adaptive feedback linearization is employed to get asymptotically exact cancellation for the inherent uncertainty in the knowledge of the given parameters of system. The control algorithm is implemented using the feedback lin...

This paper proposes a high-precision intelligent adaptive backstepping control system (HPIABCS) for the position control of permanent-magnet synchronous motor (PMSM) servo drive. The HPIABCS incorporates an ideal backstepping controller, a dynamic recurrent-fuzzy-wavelet-neural-network (DRFWNN) uncertainty observer and a robust H∞ controller. First, a backstepping position controller is designe...