High gain Balun-low-noise-amplifier (LNA) is proposed for tuner of digital televisions (DTVs). The proposed Balun-LNA is based on CS-CG (common-source-common-gate) structure. To improve the isolasion and frequency response, Balun-LNA has cascode transistors before load resistors. Balun-LNA uses current-bleeding circuit to increasie transconductance of CS transistor, so that current-bleeding transistor has transconductance of N-1 times larger than transconductance of cascode transistor. Thereby, transconductance and current of CS transistor are increased N times, as N-1 times of current pass to current-bleeding transistor. Therefore current of CG and CS stages stay identical. Also, Balun-LNA employs a positive feedback to satisfy input impedance matching and CG transistor has higher transconductance. By increasing transconductance of CS and CG transistors, the proposed Balun-LNA achieves to high voltage gain. Accordingly, CG and CS tansistors have symmetrical currents and loads at the differential output of the proposed Balun-LNA. Symmetrical loads cause the balanced differential outputs. This proposed Balun-LNA is designed in 90-nm CMOS technology and covers the frequency range of 40 MHz to 1GHz. This Balun-LNA achieves the voltage gain of 22.6 dB, S11 of less than -10 dB and the Minimum NF of 5 dB. This Balun-LNA operates at the nominal supply voltage of 2.2v.

this paper studies output feedback control of pure-feedback systems with immeasurable states and completely non-affine property. Since availability of all the states is usually impossible in the actual process, we assume that just the system output is measurable and the system states are not available. First, to estimate the immeasurable states a state observer is designed. Relatively fewer results have been proposed for pure feedback systems because the cascade and non-affine properties of pure-feedback systems make it difficult to find the explicit virtual controls and actual control. Therefore, by employing the singular perturbation theory in back-stepping control procedure, the virtual/actual control inputs are derived from the solutions of a series of fast dynamical equations which can avoid the “explosion of complexity’’ inherently existing in the conventional back-stepping design. The stability of the resulting closed-loop system is proved by Tikhonov’s theorem in the singular perturbation theory. Finally, the detailed simulation results are provided to demonstrate the effectiveness of the proposed controller, which can overcome the non-affine property of pure-feedback systems with lower complexity and fewer design parameters.

This article deals with the design of an adaptive controller for switched non-strict feedback nonlinear systems. In the studied system, the switching signal is arbitrary, the states are not measurable, and the signs of the control gain functions that describe the control directions are completely unknown. First, the unknown nonlinear functions in the switched system are approximated using the universal approximation theorem. Then, the unmeasured states are estimated using the linear state observer, and the controller is designed through an adaptive back-stepping design procedure. Due to the appropriate change of coordinates, 1) neither fuzzy nor radial basis function is used in the design of the controller, 2) only one adaptation law is designed to estimate the unknown parameters in the switched non-strict feedback nonlinear system, and 3) there is no Nussbaum function in the proposed adaptive controller so, the large control signal in the initial stages and the consequent damage to the actuators can be prevented. These features can lead to the simplicity of controller design and the reduction of computational burden. Therefore, the proposed method can be used for practical systems. The stability of the closed-loop system is proved using Lyapunov stability theory. It is shown that, in addition to the semi-globally uniformly ultimately boundedness of all closed loop signals, the tracking error converges to a small neighborhood around zero. In the end, the efficiency of the proposed control method is confirmed through the simulation results of an example.

Due to the growth of renewable energies and the need for sustainable electrical energy, AC microgrids (MGs) have been the subject of intense research. Medium voltage MGs will soon have a special place in the power industry. This paper uses a new and effective control scheme for islanded inverter-based medium voltage MGs using the master-slave (MS) technique. The controllers only need local measurements. The designed controls are based on adaptive input-output feedback linearization control (AIOFLC). These controls have a high-performance response; and are robust against some uncertainties and disturbances. The use of the designed control scheme makes the output voltage of distributed generation (DG) sources have negligible harmonics. Besides, the generated voltage and active/reactive powers track their references effectively. The model of the inverter-based DGs is considered in a stationary reference frame, and there is no need for any coordinate frame transformation. The control method presented in this paper can be used for MGs with any number of inverter-based DGs and parallel inverters. The effectiveness of the proposed control scheme is evaluated by simulation in SIMULINK/MATLAB environment and compared with that of feedback linearization control (FLC) and conventional sliding mode control (CSMC).

In this article, a new method is proposed to design robust dynamic output feedback control for switched uncertain continuous-time linear parameter varying (LPV) systems. The system matrices are supposed to depend on both the time-varying scheduling and uncertain parameters. Hysteresis switching law is exploited for the switching controller synthesis. Firstly, the robust switching controllers are robustly designed so that the stability and the induced L2-gain performance of the switched closed-loop uncertain LPV system can be guaranteed. The usual switching logics can cause discontinuous chattering control signal which are not acceptable in a practical situation. Accordingly, a smooth switching strategy for control design has been tackled in the following. By utilizing an independent family of parameter-dependent Lyapunov matrices and slack variables, performance improvement is achieved when compared to the previous works. The proposed method is formulated in terms of solutions to a set of parameter-dependent LMIs using the presented iterative algorithm. Finally, an inverted pendulum on a cart is illustrated to verify the advantages of the presented approach.

In this study, we try to control the temperature of a building consisting of four rooms with large time delays. In design process we use Krasovskii-Lyapunov method to guaranteed the stability of time delayed system. Because of difficulty of state estimation, an output feedback controller design method is used. for this purpose, we use linear Matrix Inequalities (LMI) and to solve LMIs, Matlab yalmip toolbox is utilized. Simulation results show that, by using our designed controller, even in presence of delay, uncertainty and disturbance, our system is stable and has proper performance.

An adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. Another key feature of this structure is the fact that, it does not need model of the system. In this scheme, neurolinearizer has few weights, so it is practical in adaptive situations. Online training of neurolinearizer is compared to model predictive recurrent training. Relationships between this controller and neural network based model reference adaptive controller are established. A CSTR reactor and pH control in a neutralization process illustrate performance of this method. Simulation studies show a superior performance with respect to a PI controller.

In this study, an observer-based tracking controller is proposed and evaluated experimentally to solve the trajectory tracking problem of robotic manipulators with the torque saturation in the presence of model uncertainties and external disturbances. In comparison with the state-of-the-art observer-based controllers in the literature, this paper introduces a saturated observer-based controller based on a radial basis function neural network. This technique helps the controller produce feasible control signals for the robot actuators. As a result, it efficiently diminishes the actuators saturation risk and consequently, a better transient performance is obtained. The stability analyses of the dynamics of the tracking errors and state estimation errors are given with the help of a Lyapunov-based stability analysis method. The theoretical analyses will systematically prove that the errors are semi-globally uniformly ultimately bounded and they converge to a small set around the origin whose size is adjustable by a suitable tuning of parameters. At last, some real experiments are performed on a laboratory robotic arm to illustrate the efficiency of the proposed control system for real industrial applications