Physical implementation of optimal control over a direct current

In the next manuscript we will show an interesting physical application of the technique of optimal control over a direct current, more exactly on a direct current motor. For this, the mathematical support or scaffolding that allows to establish the model of the problem will be presented in the development of the document, which contains a basic structure supported in a general dynamic system (linear or non-linear) and an objective function that will depend on the system. Although in essence the optimal control problems correspond to infinite spaces, the finite-dimensional case will be analyzed initially and then made an extension to the case of a quadratic linear regulator for an interval of infinite duration. The results of the implementation of this optimal control technique on a direct current motor will be analyzed by means of Bole diagrams that will represent the frequency response of the system and a follow-up diagram of set points and rejection of disturbances.

Artificial Neural Networks Models Based on ARX and State Space Forms and Adaptive Control PID/LQR of Systems Based on Peltier Cells

To improve the performance of a thermal plant based on Peltier cell actuators, an online parametric estimation via artificial neural networks and adaptive controller is presented. The control actions are based on adaptive digital controller and an adaptive quadratic linear regulator approaches. The Artificial neural networks topology is based on ARX and NARX models, and its training algorithms, such as accelerated backpropagation and recursive least square. The Control strategies are design-oriented to adaptive digital PID controller and quadratic linear regulator framework. The proposal is evaluated on temperature control of an object that is inside of a chamber.

Comparison of electronic load using linear regulator and boost converter

<span lang="EN-US">Direct current (DC) electronic load is a useful equipment for testing the electrical system. It can emulate various load at a high rating. The electronic load requires a power converter to operate and a linear regulator is a common option. Nonetheless, it is hard to control due to the temperature variation. This paper proposed a DC electronic load using the boost converter. The proposed electronic load operates in the continuous current mode and control using the integral controller. The electronic load using the boost converter is compared with the electronic load using the linear regulator. The results show that the boost converter able to operate as an electronic load with an error lower than 0.5% and response time lower than 13 ms.</span>

An RFID-Based Self-Biased 40 nm Low Power LDO Regulator for IoT Applications

There are emerging applications, like bridge structural health monitoring, continuous patient condition and outdoor aiding of the elderly and the disabled, where Internet of things (IoT) nodes are used with very limited accessibility and no connection to the main supply network. They may also be exposed to harsh environmental conditions. These are applications where power and available area constraints are of great concern. In this paper, we design a 1.1 V low dropout (LDO) linear regulator in 40 nm technology to be embedded in IoT nodes. To address these constraints, we used state-of-the-art, variability-aware resistor-less sub-threshold biased CMOS-only ultra low power consumption configurations having low active area. The proposed LDO is internally compensated with embedded 18 pF Miller and 10 pF load capacitances. It can supply 1 mA maximum load current with 0.8 uA quiescent current. The dropout voltage of the regulator is 200 mV with minimum input voltage of 1.3 V. The efficiency of the regulator is 84%, which is about 99% of the maximum achievable efficiency for a 200 mV dropout voltage. The whole circuit, consisting of the embedded voltage reference and the Miller and load capacitances, takes less than 0.007 mm2 of the die size with 1 μW power consumption.