다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

In this paper, an adaptive fuzzy optimal control design is addressed for a class of unknown nonlinear discrete-time systems. The controlled systems are in a strict-feedback frame and contain unknown functions and nonsymmetric dead-zone. For this class of systems, the control objective is to design a controller, which not only guarantees the stability of the systems, but achieves the optimal control performance as well. This immediately brings about the difficulties in the controller design. To this end, the fuzzy logic systems are employed to approximate the unknown functions in the systems. Based on the utility functions and the critic designs, and by applying the backsteppping design technique, a reinforcement learning algorithm is used to develop an optimal control signal. The adaptation auxiliary signal for unknown dead-zone parameters is established to compensate for the effect of nonsymmetric dead-zone on the control performance, and the updating laws are obtained based on the gradient descent rule. The stability of the control systems can be proved based on the difference Lyapunov function method. The feasibility of the proposed control approach is further demonstrated via two simulation examples.

Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone

Reactive matching is extended to wide bandwidths using the impedance property of LC-ladder filters. In this paper, we present a systematic method to design wideband low-noise amplifiers. An SiGe amplifier with on-chip matching network spanning 3-10 GHz delivers 21-dB peak gain, 2.5-dB noise figure, and -1-dBm input IP3 at 5 GHz, with a 10-mA bias current.

A 3-10-Ghz low-noise amplifier with wideband LC-ladder matching network

In the paper, an adaptive tracking control design is studied for a class of nonlinear discrete-time systems with dead-zone input. The considered systems are of the nonaffine pure-feedback form and the dead-zone input appears nonlinearly in the systems. The contributions of the paper are that: 1) it is for the first time to investigate the control problem for this class of discrete-time systems with dead-zone; 2) there are major difficulties for stabilizing such systems and in order to overcome the difficulties, the systems are transformed into an n -step-ahead predictor but nonaffine function is still existent; and 3) an adaptive compensative term is constructed to compensate for the parameters of the dead-zone. The neural networks are used to approximate the unknown functions in the transformed systems. Based on the Lyapunov theory, it is proven that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to a small neighborhood of zero. Two simulation examples are provided to verify the effectiveness of the control approach in the paper.

Adaptive NN Tracking Control of Uncertain Nonlinear Discrete-Time Systems With Nonaffine Dead-Zone Input

In the framework of the networked control systems (NCSs), the components are connected with each other over a shared band-limited network. The merits of NCSs include easy extensibility, resource sh...

A survey on sliding mode control for networked control systems

This article demonstrates a Chebyshev Neural Network (CNN) based sliding mode controller for uncertain nonlinear systems. The uncertainties in the system are classified as mismatched parame...

A Chebyshev Neural Network based sliding mode controller for uncertain discrete-time-delayed nonlinear systems

This paper proposes the work on the back stepping controller for the class of Discrete-time nonlinear system under amplitude a rate saturation, dead-zone constraints. A robust adaptive neural network (NN) control is developed for a general class of uncertain single-input-single output (SISO) discrete time nonlinear system with unknown system dynamics and input nonlinear ties. Here ( ) function is employed to saturate the amplitude and rate of system input. For input nonlinear ties like saturation and dead zone, discrete time SISO nonlinear system in combination with back stepping and Lyapunov synthesis is proposed for adaptive NN design with guaranteed stability. To reduce the effect of dead-zone a dead-zone compensator or dead-zone inverse is designed. Chebyshev Neural Networks (CNNs) are used to approximate the unknown nonlinear functions in system dynamics. New weight update laws are derived to make this scheme adaptive and show the stability of this scheme. Finally simulation results are presented in this paper to show the effectiveness of proposed scheme.

Control of Discrete-Time Nonlinear Systems Using Backstepping Technique in the Presence of Saturation and Dead-Zone Constraints

This paper presents a low-power (LP) and low-voltage (LV) ultra-wideband (UWB) low-noise amplifier (LNA) using source degeneration topology. A biasing metric for LV and LP is designed in deep-submicrometer CMOS technologies. Using this biasing metric with source degeneration and current reused technique LNA is analyzed for a low voltage 0.65V, low power of 0.80-mW. Measurement results show 11.942 −dB voltage gain, 03–5.5 GHz bandwidth, − <2dB NF.

A low power, low voltage UWB low-noise amplifier using source degeneration technique

This paper has investigated the NPIC (Nonlinear proportional integral Controller) and Genetic algorithm has applied to obtain the minimum value of error. The objective function has been created using Integral absolute error (IAE) This controller provides a unique combination of proportional and integral controller along with a nonlinear factor present in the controller. which makes the controller much more effective in comparison to linear controller, as it has always remained a challenge to control a robotic manipulator which is highly non linear. so this controller provides an approach to control a nonlinear manipulator through nonlinear controller. He abstracts should summarize the contents of the paper in short terms, i.e., 150-250 words.

Optimization of NPIC Controller using Genetic Algorithm



In this paper, a chaotic interleaving scheme is proposed for further performance
improvement of Interleave Division Multiple Access (IDMA). Interleavers are used to generate
permuted sequence of signals to be transmitted with optimum correlation.



Random interleaving (RI) was the fundamental taxonomy and used to distinguish the different
users in IDMA scheme. The logistic map based chaotic interleaving scheme is suggested for
IDMA. The system is also combined with Minimum Mean Square Error (MMSE) equalizer to combat
the effect of Inter Symbol Interference (ISI).



For the sake of simplicity, no pulse shaping is assumed at the transmitter and ISI channel is
considered to be a fixed 3 tap channel. Simulations are performed in MATLAB. BPSK modulation is
used. Furthermore, the results show that the IDMA with proposed logistic map based chaotic
interleaver outperforms the traditional random interleaving scheme in terms of BER without the need
of extra memory and computational complexity.



The obtained results show the noticeable performance improvement of CI-IDMA with
MMSE equalization in deep fading situations. Chaos based interleaver also offers other advantages
such as less requirement of memory and small implementation complexity.


Vinay Kumar Deolia

Papers

A Chebyshev Neural Network based sliding mode controller for uncertain discrete-time-delayed nonlinear systems

Control of Discrete-Time Nonlinear Systems Using Backstepping Technique in the Presence of Saturation and Dead-Zone Constraints

A low power, low voltage UWB low-noise amplifier using source degeneration technique

Optimization of NPIC Controller using Genetic Algorithm

MMSE Equalization Based Performance of Chaotic Interleaving Scheme for Iterative IDMA System