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Elements Of Power System Analysis

Safe reinforcement learning: A control barrier function optimization approach

Value iteration and adaptive optimal output regulation with assured convergence rate

&lt;p style='text-indent:20px;'&gt;The hydraulic servo actuators (HSA) are often used in the industry in tasks that request great powers, high accuracy and dynamic motion. It is well known that HSA is a highly complex nonlinear system, and that the system parameters cannot be accurately determined due to various uncertainties, inability to measure some parameters, and disturbances. This paper considers control problem of the HSA with unknown dynamics, based on adaptive dynamic programming via output feedback. Due to increasing practical application of the control algorithm, a linear discrete model of HSA is considered and an online learning data-driven controller is used, which is based on measured input and output data instead of unmeasurable states and unknown system parameters. Hence, the ADP based data-driven controller in this paper requires neither the knowledge of the HSA dynamics nor exosystem dynamics. The convergence of the ADP based control algorithm is also theoretically shown. Simulation results verify the feasibility and effectiveness of the proposed approach in solving the optimal control problem of HSA.&lt;/p&gt; 

Data-driven control of hydraulic servo actuator based on adaptive dynamic programming

A text book of electrical technology , A text book of electrical technology , کتابخانه دیجیتالی دانشگاه علوم پزشکی و خدمات درمانی شهید بهشتی

A text book of electrical technology

In this article, a data-driven distributed control method is proposed to solve the cooperative optimal output regulation problem of leader-follower multiagent systems. Different from traditional studies on cooperative output regulation, a distributed adaptive internal model is originally developed, which includes a distributed internal model and a distributed observer to estimate the leader's dynamics. Without relying on the dynamics of multiagent systems, we have proposed two reinforcement learning algorithms, policy iteration and value iteration, to learn the optimal controller through online input and state data, and estimated values of the leader's state. By combining these methods, we have established a basis for connecting data-distributed control methods with adaptive dynamic programming approaches in general since these are the theoretical foundation from which they are built.

Reinforcement Learning-Based Cooperative Optimal Output Regulation via Distributed Adaptive Internal Model.

This paper basically represents how to deblurring images using a wiener filter [1]. Basically wiener filter is used to produce an estimate of a desired or target random process by linear time-invariant filtering [2] of an observed noisy process, assuming known stationary signal and noise spectra, and additive noise. The Wiener filter minimizes the mean square error between the estimated random process and the desired process. In here it has been introduced about the image processing capabilities in MATHLAB. Dividing some steps it has been shown how an image can be processed with the help of MATHLAB [3][4] command. Moreover how image can be effected by noise & how noise can be removed from image this also described in this paper properly

/pdf/deblurring-images-using-a-wiener-filter-8ohqufawij.pdf

Deblurring Images using a Wiener Filter

This paper is designed solar tracking system with mirror booster using microcontroller. Solar energy is rapidly becoming an alternative means of electrical source all over the world. To make effective use of solar energy, its efficiency must be maximized. A feasible approach to maximizing the power output of solar array is by sun tracking. This paper deals with the design and construction of solar tracking system by using a stepper motor, gear motor, photo diode. Mirror is used as booster to maximize the efficiency. The whole frame will travel circularly and the mirror will travel from south to north and vice-versa. The prototype is considered around a programmed microcontroller which controls the system by communicating with sensors and motor driver based on movement of the sun. The performance and characteristics of the solar tracker are experimentally analyzed.

/pdf/microcontroller-based-automatic-solar-tracking-system-with-4vcyqbv4sb.pdf

Microcontroller Based Automatic Solar Tracking System with Mirror Booster

This study proposes a novel distributed predictive cruise control (PCC) algorithm based on reinforcement learning. The algorithm aims at reducing idle time and maintaining an adjustable speed depending on the traffic signals. The effectiveness of the proposed approach has been validated through Paramics microscopic traffic simulations by proposing a scenario in Statesboro, Georgia. For different traffic demands, the travel time and fuel consumption rate of vehicles are compared between non-PCC and PCC algorithms. Microscopic traffic simulation results demonstrate that the proposed PCC algorithm will reduce the fuel consumption rate by 4.24% and decrease the average travel time by 3.78%.

Distributed predictive cruise control based on reinforcement learning and validation on microscopic traffic simulation

This model describes the production and destruction mechanism of nitrogen plasma at atmospheric pressure. We have studied the mechanisms of chemical dissociation, ionization, ion conversion and recombination in nitrogen plasmas, with kinetic temperature (Tg) of the free electrons being higher than the kinetic temperature (Tg) of heavy species. Therefore, the investigation of nitrogen plasma species in a wide range of pressure from 1 Torr to 760 Torr is interesting phenomena for obtaining the equilibrium state when the nitrogen species breakdown. In order to calculate the species densities to reach thermodynamic equilibrium under various conditions, a set of chemical kinetic reactions of nitrogen under consideration have been simulated. It solves the particle balance equations for a set of interacting species. In this study 16 reactions and 4 species of Nitrogen N, N2, N+, N2+ and electron have been considered. The densities of the charged and neutral species are modeled by continuity equations which includes the relevant plasma-chemical kinetics. Nitrogen species density is guided by continuity equation where chemical processes and Arrhenius form are used to follow the change of species density over the time. To calculate the species densities over pressure, temperature and time the continuity equations of the 16 reactions for the 5 species under consideration giving their initial pressure, densities and temperatures, with the latter held constant have been solved. The variations of species densities have been investigated as a function of pressure ranging from 1 to 760 Torr. This model shows that as the pressure is increased the species densities of nitrogen plasma also increase from pressure 1 to 200 Torr and after pressure above 200 Torr the species densities become almost saturated. The change of species densities at various temperatures ranging from 2000 Kelvin to 25000 Kelvin is successfully investigated. The destruction and production rates of the nitrogen species also have been calculated within the time ranging from 0 to 19nS and it shows that the density of nitrogen plasma increases with time. In our study we have considered the gas and electron temperature as 10k Kelvin and 4eV respectively.

/pdf/kinetic-modelling-of-atmospheric-pressure-nitrogen-plasma-4dtlsrkkil.pdf

Mohammed Mynuddin

Papers

Reinforcement Learning-Based Cooperative Optimal Output Regulation via Distributed Adaptive Internal Model.

Deblurring Images using a Wiener Filter

Microcontroller Based Automatic Solar Tracking System with Mirror Booster

Distributed predictive cruise control based on reinforcement learning and validation on microscopic traffic simulation

Kinetic Modelling of Atmospheric Pressure Nitrogen Plasma