A new iterative learning controller is proposed for a general unknown discrete time-varying nonlinear non-affine system represented by NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. The proposed controller is composed of an iterative learning neural identifier and an iterative learning controller. Iterative learning control and iterative learning identification ar...

An adaptive control technique can be applicable to reorient spacecraft with uncertain properties such as mass, inertial and various misalignments. A nonlinear quaternion feedback controller is chosen as a baseline attitude controller. A linearly added adaptive input supported by neural networks to the baseline controller can estimate and eliminate the uncertain spacecraft property adaptively. T...

This work targets the automated minimum-energy optimization of Quantized Neural Networks (QNNs) networks using low precision weights and activations. These networks are trained from scratch at an arbitrary fixed point precision. At iso-accuracy, QNNs using fewer bits require deeper and wider network architectures than networks using higher precision operators, while they require less complex ar...

The main problem of vehicle vibration comes from road roughness. An active suspension systempossesses the ability to reduce acceleration of sprung mass continuously as well as to minimizesuspension deflection, which results in improvement of tire grip with the road surface. Thus, braketraction control and vehicle maneuverability can be improved consider ably .This study developeda new active su...

FACTS technology has considerable applications in power systems, such as; improving the steady stateperformance, damping the power system oscillations, controlling the power flow, and etc. STATCOM is oneof the most important FACTS devices used in the parallel compensation, enhancing transient stability andetc. Since three phase fault is widespread in power systems, in this paper STATCOM is used...

Abstract The hybrid quantum–classical learning scheme provides a prominent way to achieve quantum advantages on near-term devices. A concrete example toward this goal is the neural network (QNN), which has been developed accomplish various supervised tasks such as classification and regression. However, there are two central issues that remain obscure when QNN exploited tasks. First, classifier...

Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth qubit count. Here we investigate how noise affects neural network (QNN) for state discrimination, applicable on near-term devices as it fulfils the above criteria. We find that when simulating gradient calculation noisy device, large number of parameters is disadvantageous. By introducing new small...

in this paper, an adaptive neuro fuzzy inference system (anfis) based control is proposed for the tracking of a micro-electro mechanical systems (mems) gyroscope sensor. the anfis is used to train parameters of the controller for tracking a desired trajectory. numerical simulations for a mems gyroscope are looked into to check the effectiveness of the anfis control scheme. it proves that the sy...

In this paper, a recurrent fuzzy-neural network (RFNN) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. Since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

Neural networks offer a unique approach to controlling a dynamically changing system. By updating synaptic weights of an interconnected, algebraic framework, desired system objectives can be reached despite an unknown operating environment. This research implements a neural network for controlling the attitude of a satellite with unknown system disturbances. The training and updating of the con...