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 paper deals with the design challenge of low power LNA. As the biasing voltage is reduced, there is degradation in transit gain, efficiency, and operating frequency. Therefore, an optimized biasing metric has been introduced, depicted the tradeoff between power consumption and above specified parameters. In the last decay, many scholars optimized the single objective and multi-objective function using particle swarm optimization (PSO). In this paper, voltage gain has been taking as objective function. Aspect ratio (W/L) of all transistors and other parameters of LNA is optimized using PSO. The result is simulated in 90-nm CMOS technology in Cadence virtuoso software. All the simulation has been done for a frequency range of 3–8 GHz. Optimization is obtained at operating frequency of 4.1 GHz. The results depicted the performance of LNA with minimum 1.1 dB NF, −16.4 dB S11, and 17.1 dB S21 at frequency 4.1 GHz with 0.9 V power supply and 1.7 mW power consumption.

Optimization of Low Power LNA Using PSO for UWB Application

Controlling of robot has always became an exigent work for engineers these controllers were came into existence since 1950, when a PID controller was used to control complex systems the structure of this controller was very easy to design and has always remain a most popular controller in the industry because of its lucidity and low cost. it is sufficient enough to handle linear system but was unfit for nonlinear system, researchers and scientist has tried to resolve the drawbacks of PID by fuzzy logic controller which was effective for nonlinear system further as the year passes by new controlling techniques has arrived which were more powerful in terms of controlling in comparison to the previous ones. neuro logic controller came to handle those systems which are complex, highly nonlinear and poorly dynamically analyzed and further this controller was combine with the Traditional controllers and give rise to neuro PID controller and neuro Fuzzy PID controller. This paper presents a detailed review on various controlling techniques which are being used in the controlling of robotic manipulator starting from the development of the first controller till now.

Assesment of Traditional and Hybrid Controller for Controlling Robotic Manipulator System

The objective of the study is to investigate the thermal, electrical, and exergetic performance of a hybrid photovoltaic–thermal (PVT) system under the influence of copper oxide (CuO) nanofluid and phase change material (Vaseline (petroleum jelly)) as a heat storage medium. A mathematical model was developed with the help of various energy-balance equations over the layers of the hybrid system. The performance evaluation of the PVT system was performed using pure water, CuO-water nanofluid (0.2 and 0.4% weight fractions), and CuO-water nanofluid 0.4% weight fraction with Vaseline as a phase change material. The results of the overall analysis show that the performance of the PVT system is better using CuO-water nanofluid (0.4% wt. fraction) with PCM as compared to the water-cooled PVT system and CuO-water nanofluid. The results obtained from the study show indicate that the cell temperature of PVT was reduced by 4.45% using nanofluid cooling with PCM compared to a water-cooled PVT system. Moreover, the thermal, electrical, and overall efficiencies improved by 6.9%, 4.85%, and 7.24%, respectively, using 0.4% wt. fraction of CuO-water nanofluid with PCM as compared to PVT water-cooled systems. The performance of the PVT system was also investigated by changing the mass flow rate (MFR). The increase in mass flow rate (MFR) from 0.05 kg/s to 0.2 kg/s tends to enhance the electrical and overall efficiencies from 12.89% to 16.32% and 67.67% to 76.34%, respectively, using 0.4% wt. fraction of CuO-PCM as fluid.

https://www.mdpi.com/2227-9717/11/6/1602/pdf?version=1684913383

Application of Copper Oxide Nanofluid and Phase Change Material on the Performance of Hybrid Photovoltaic–Thermal (PVT) System

This paper proposes a back stepping controller for the class of discrete-time nonlinear system in the presence of input nonlinearities like saturation and dead-zone. A robust adaptive neural network (NN) control is investigated for a general class of uncertain single-input-single-output (SISO) discrete-time nonlinear systems with unknown system dynamics and input nonlinearities i.e. combination of saturation and dead-zone. For input nonlinearities, discrete-time SISO nonlinear system in combination with back stepping and Lyapunov synthesis is proposed for adaptive neural network design with guaranteed stability. The actuator nonlinearities are assumed to be unknown and compensated by a pre compensator using Chebyshev neural network (CNN) and unknown nonlinear functions are also approximated by CNN. Weight update laws, based on Lyapunov theory are derived to make this scheme adaptive and the convergence properties are shown. Simulation results validate the effectiveness of proposed scheme.

Adaptive Backstepping Control for a Class of Uncertain Discrete-Time Nonlinear Systems with Input Nonlinearities

/pdf/detection-of-tumor-in-brain-depended-on-k-means-clustering-1gcloroz1o.pdf

Vinay Kumar Deolia

Papers

Optimization of Low Power LNA Using PSO for UWB Application

Assesment of Traditional and Hybrid Controller for Controlling Robotic Manipulator System

Application of Copper Oxide Nanofluid and Phase Change Material on the Performance of Hybrid Photovoltaic–Thermal (PVT) System

Adaptive Backstepping Control for a Class of Uncertain Discrete-Time Nonlinear Systems with Input Nonlinearities

Detection of tumor in brain depended on k-means clustering using GUI