This paper fully discusses how to design an observer based decentralized fuzzy adaptive controller for a class of large scale multivariable non-canonical nonlinear systems with unknown functions of subsystems’ states. On-line tuning mechanisms to adjust both the parameters of the direct adaptive controller and observer that guarantee the ultimately boundedness of both the tracking error and tha...

This paper investigates the problem of adaptive neural networks output feedback control for a class of stochastic nonlinear system with unmodeled dynamics and unmeasured states. To avoid the problem of ‘explosion of complexity’ inherent in the conventional backstepping design procedure, the dynamic surface control (DSC) technique is applied to the control design of the investigated systems with...

Abstract Neural network (NN) ensembles can reduce large prediction variance of NN and improve accuracy. For highly non-linear problems with insufficient data set, the accuracy models becomes unstable, resulting in a decrease ensembles. Therefore, this study proposes frequency-based ensemble that identifies core values, which are members to be used expected concentrated near true response. The c...

Abstract A critic-observer decentralized force/position approximate optimal control method is presented to address the joint trajectory and contacted force tracking problem of modular reconfigurable manipulators (MRMs) with uncertain environmental constraints. The dynamic model MRM systems formulated as an integration subsystems via extensive state observer (ESO) associated effect interconnecte...

Customized optical filtering and light-dependent neural filtering were implemented in an ideal-observer model for an L-alternative forced-choice visual task. The model was applied to a contrast threshold visual task with adaptive optics correction of ocular higher-order (HO) aberrations under different light regimes, for which experimental data have previously been obtained (J. Mod. Opt.55, 791...

In this paper, an adaptive observer for multivariable nonlinear systems that present an unknown general state equation and known linear output equation is developed. The observer is based on linearly parameterized neural networks and Lyapunov methods are used for stability analysis. We consider a more general class of systems than in previous works and the usual SPR (strictly positive real) ass...

in this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and wavelet-artificial neural network (wavelet-ann) models were applied to model rainfall-runoff (rr) relationship. for this purpose, the daily stream flow time series of hydrometric station of hajighoshan on gorgan river and the daily rai...

This paper presents a novel control method for a general class of nonlinear systems using neural networks (NNs). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a high-gain observer to estimate the derivatives of the system output, an adap...