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A variable structure neural network control (VSNNC) is proposed for a class of uncertain nonlinear SISO systems, in which neural network is used as an estimator for the system unknown nonlinear functions. And variable structure control strategy is improved by adding the continuous function item to control input. It can adjust discontinuous item in control variable adaptively according to the distance...
A state-space technique for control of nonlinear multi-input multi-output (MIMO) systems identified by an Additive Nonlinear Autoregressive eXogenous (ANARX) model is presented. Controlled system is identified by Neural Network based Simplified Additive NARX (NN-SANARX) model linearized by dynamic feedback. The neural network based model is represented in the discrete-time state-space form. The problem...
Three-motor synchronous speed-regulation system is a multi-input multi-output (MIMO), nonlinear, and coupling complex control system. This paper focuses on the system of induction motors powered by current-track type SPWM transducers, establishes the mathematical model of the system in the way of analytical expression. Self-tuning PID controllers based on RBF neural network and neuron decoupling compensator...
A state-space technique for control of nonlinear SISO systems identified by an Additive Nonlinear Autoregressive eXogenous (ANARX) model is presented. Two cases are shown. In the first case system model is given explicitly in the form of ANARX structure. In the second case controlled system is identified by Neural Network based Simplified Additive NARX (NN-SANARX) model linearized by dynamic feedback...
A new adaptive decoupling control algorithm is presented for a family of complex MIMO systems with strong couplings and severe nonlinearity such as the hovering submarines. The couplings between different channels are decoupled with the modified Feedback Decoupling Control Algorithm (MFDC), and the subsystems is adjusted with the online parameters estimation algorithm based on the neural networks...
In this paper, two novel adaptive PID-like controllers capable of controlling multi-variable, non-linear multi-input multiple-output (MIMO) systems are proposed. The proposed controllers are based on neural networks techniques and the learning algorithms are derived according to minimization of the error between the output of the system and the desired output. At first, two kinds of PID-like neural...
This paper presents the use of neural network control approaches for a two inputs -two outputs (TITO) coupled tank liquid levels with disturbances effects and set-point changes in dynamic system. Hybrid PI-neural network (hybrid PI-NN) and PID neural network (PID-NN) controllers are the techniques used in this investigation to actively control the liquid levels of coupled tank system. Unlike traditional...
This paper proposes a radial basis function neural network adaptive backstepping controller (RBFNN_ABC) for multiple-input multiple-output (MIMO) nonlinear systems in block-triangular form. The control scheme incorporates the adaptive neural backstepping design technique with a first-order filter at each step of the backstepping design to avoid the higher-order derivative problem, which is generated...
The induction motor is a MIMO, nonlinear and high coupling system. The reversibility of the induction motor is testified. Consequently, a pseudo-linear system is completed by constructing a neural network inverse (NNI) system and combining it with the motor system. The inverse can transform the MIMO nonlinear system into two SISO linear subsystems (i.e., rotor speed and flux subsystems). In order...
How to design the flight control system (FCS) of small-scale unmanned helicopter is still a difficult challenge today. In this paper, one novel control approach based on Multivariable PID Neural Network (MPIDNN) was firstly used to design the FCS of small-scale unmanned helicopter. MPIDNN is suitable for controlling the multi-input multi-output (MIMO), nonlinear, highly coupled, uncertain and dynamic...
To design the flight control system (FCS) of small-scale unmanned helicopter is still a difficult challenge today. The hardware and software architecture of FCS was designed in this paper. And one novel control approach based on multivariable PID neural network (MPIDNN) was firstly used to design the FCS of small-scale unmanned helicopter on the hardware platform. MPIDNN is suitable for controlling...
In order to actualize decoupling control for nonlinear multivariate coupling system, A multivariate self-adapting decoupling control method based on CMAC and PID was proposed in this studies, and its algorithm was designed in detail. The control strategy utilizes PID controller and CMAC to combine a composite controller. Outputs of multiple same composite controllers are mapped by MIMO linear neural...
The induction motor is a MIMO, nonlinear and high coupling system. The reversibility of the induction motor is testified. Consequently, a pseudo-linear system is completed by constructing a neural network inverse (NNI) system and combining it with the motor system. The inverse can transform the MIMO nonlinear system into two SISO linear subsystems (i.e., rotor speed and flux subsystems). In order...
In this paper, we present multirate sampling type dynamic quantizers for linear control systems with discrete-valued signal constraints. Although our previous works have derived an optimal dynamic quantizer and verified its effectiveness by simulations and experiments, the result can not be applied to SIMO plants with unstable zeros. This paper overcomes the drawback by using a multirate sampling...
In the practical application of decouple control for MIMO system, precise mathematical model of controlled object is greatly depended on and cause unsatisfactory control effects. Complexity of algorithms based on large-scale neural network affects the practicability and real-time performance of control algorithms. A kind of MIMO decouple control system based on double-neuron adaptive predictive and...
Different kinds of multi-module DC-DC converters have been introduced to obviate demands for different level of voltage and power in input and output. The suitable control scheme for this type of converter necessitates equal voltage sharing for series connected modules and equal current sharing for the parallel connected modules for all operating conditions even with existence of dissimilarity in...
In this paper, an application of Neural Networks based Additive Nonlinear AutoRegressive eXogenous (NN-ANARX) structure is investigated for modeling and control of nonlinear multi-input-multi-output (MIMO) systems. A novel analytical technique for calculation of control signal is proposed. After that the ANARX-based dynamic output feedback linearization control algorithm is applied for control of...
A dynamic output feedback linearization technique for model reference control of nonlinear systems identified by an additive nonlinear autoregressive exogenous (ANARX) model. ANARX structure of the model can be obtained by training a neural network of the specific restricted connectivity structure. Linear discrete time reference model is given in the form of transfer function defining desired zeros...
In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output (MIMO) nonlinear time-delay systems with unknown nonlinear dead-zones and known gain signs. The unknown time-varying delay uncertainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. By utilizing the integral-type Lyapunov function and constructing...
According to the performance of fossil fuel-fired units, that profound coupling nonlinearity and which made the mathematical model very difficult. Inverse method control for the design of MIMO system of unit coordinated control system is suggested. the realization of the inverse system up is not adopt traditional meaning is of the obvious analyze, but adopt the dynamic neural network, the inverse...
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