Recurrent neural networks with fixed time convergence for linear and quadratic programming

In this paper, a new class of recurrent neural networks which solve linear and quadratic programs are presented. Their design is considered as a sliding mode control problem, where the network structure is based on the Karush-Kuhn-Tucker (KKT) optimality conditions with the KKT multipliers considered as control inputs to be implemented with fixed time stabilizing terms, instead of common used activation functions. Thus, the main feature of the proposed network is its fixed convergence time to the solution. That means, there is time independent to the initial conditions in which the network converges to the optimization solution. Simulations show the feasibility of the current approach.Consejo Nacional de Ciencia y Tecnologí

Diseño de un controlador basado en observador para un sistema de torsión

Este artículo presenta el diseño de un controlador basado en observador para un sistema de torsión. Se propone un esquema de un controlador dinámico continuo para regular una clase de sistemas mecánicos totalmente actuados con fricción seca. El sistema físico se puede observar en la Fig. 1, y el cual es explicado por (1) y (2). En este caso, el objetivo de control es regular la variable no actuada, limitando las amplitudes de la posición y velocidad de la articulación sobre la que se ejecuta la acción. Se diseñará un observador de primer orden y posteriormente se verifican los resultados obtenidos por el controlador en modos deslizantes en comparación con un controlador PID. El desempeño de los controladores propuestos se ilustran con resultados numéricos y experimentales, para los cuales se tendrán en cuenta los siguientes factores: respuesta dinámica transitoria (rapidez y estabilidad relativa) y seguimiento de señales de referencia (error de estado estable).Universidad Nacional de ColombiaColcienciasBanco Mundia

T-fold sequential-validation technique for out-of-distribution generalization with financial time series data

The temporal structure in financial time series (FTS) data demands non-trivial considerations in the use of cross-validation (CV). Such frequently used technique is based on statistical learning theory, which is founded on the assumption that training samples are i.i.d. Although there is progress in studying fundamental phenomenons in certain learning methods such as feature selection imbalance during the learning stage, it is currently widely accepted that there will be no reason to expect good out of sample results from a learning process without such strong assumption. In FTS, there are conditions under which sub-sampling data leads to overshadow the effect of non-deterministic relationships between features and the target variable among different samples. Such effect remains unnoticed given the use of the additivity property in the decomposition of objective functions for the Learning Process. Moreover, it reduces to a particular operation the relationship among samples without information attribution. We present a technique that controls information leakage and decomposes the global probability distribution into local probability distributions, providing identification of each sample contribution to the learning process, maintaining information sparsity, therefore, relaxing the effects of the i.i.d. assumption. Parametric stability, as a result, is presented for exchange rate prediction using different predictive models.ITESO, A.C

A discontinuous recurrent neural network with predefined time convergence for solution of linear programming

The aim of this paper is to introduce a new recurrent neural network to solve linear programming. The main characteristic of the proposed scheme is its design based on the predefined-time stability. The predefined-time stability is a stronger form of finite-time stability which allows the a priori definition of a convergence time that does not depend on the network initial state. The network structure is based on the Karush-Kuhn-Tucker (KKT) conditions and the KKT multipliers are proposed as sliding mode control inputs. This selection yields to an one-layer recurrent neural network in which the only parameter to be tuned is the desired convergence time. With this features, the network can be easily scaled from a small to a higher dimension problem. The simulation of a simple example shows the feasibility of the current approach.Consejo Nacional de Ciencia y Tecnologí

Diseño de un observador y un controlador de velocidad de un motor DC por modos deslizantes

En este artículo se presenta una estructura de estimación y control de estados mediante modos deslizantes para un modelo lineal. Se presenta un sistema mecánico de un Motor DC, al cual se le diseñaran un observador y un controlador deslizante de primer y segundo orden. Los resultados se comparan con un diseño de un PID. Los dos diseños se prueban ante cambios de referencia, perturbaciones y ruido. In this paper a structure for estimation and control of states using sliding modes is presented for a linear model. A mechanical system of a DC motor is presented with a designed observer and a sliding controller of first and second order. The results are compared with a design of a PID. The two designs are tested against reference changes, disturbances and noise. — In this paper a structure for estimation and control of states using sliding modes is presented for a linear model. A mechanical system of a DC motor is presented with a designed observer and a sliding controller of first and second order. The results are compared with a design of a PID. The two designs are tested against reference changes, disturbances and noise. Keywords — Dc Motor, Observer, Sliding modes, State spaceUniversidad Nacional de ColombiaColciencia

Diseño de un Observador y un Controlador de velocidad de un motor DC por modos deslizantes

En este artículo se presenta una estructura de estimación y control de estados implementando las normas básicas de diseño de observadores y controladores mediante modos deslizantes para un modelo lineal. Se presenta un sistema mecánico de un Motor DC, al cual se le diseñaran un observador y un controlador deslizante de primero y segundo orden. Los resultados se comparan con un diseño de un PID. Los dos diseños se prueban ante cambios de referencia, perturbaciones y ruido.Universidad Nacional de ColombiaColcienciasBanco Mundia

An Augmented Lagrangian Neural Network for the Fixed-Time Solution of Linear Programming

In this paper, a recurrent neural network is proposed using the augmented Lagrangian method for solving linear programming problems. The design of this neural network is based on the Karush-Kuhn-Tucker (KKT) optimality conditions and on a function that guarantees fixed-time convergence. With this aim, the use of slack variables allows transforming the initial linear programming problem into an equivalent one which only contains equality constraints. Posteriorly, the activation functions of the neural network are designed as fixed time controllers to meet KKT optimality conditions. Simulations results in an academic example and an application example show the effectiveness of the neural network

Integral high order sliding mode control of a brake system

The aim of this paper is to present the design of a robust sliding mode control scheme for a vehicle system which consists of active brake systems. The proposed control strategy is based on the combination of high order sliding mode control methods and integral sliding mode control, taking advantage of the block control principle. The brake controller induces the antilock brake system feature by means of tracking the slip rate of the car, improving the stability in the braking process and preventing the vehicle from skidding.Cinvesta

Real time leak detection and isolation in pipelines: a comparison between Sliding Mode Observer and algebraic steady state method

The purpose of this paper is to compare two different algorithms used to detect and isolate water leaks in a pipeline. One method is based on a Sliding Mode Observer and the second method is an Algebraic method obtained from the pipeline model in steady state. Because of the simplicity of both methods, they can be easily implemented. The methods were tested offline with real time data and the Algebraic method was also implemented online. Satisfactory results are shown through some experiments.Consejo Nacional de Ciencia y Tecnologí

State and Parameter Estimation of a CSTR

In the continuation of authors’ studies on estimation and control for Continuous Stirred-Tank Reactors (CSTR), a new structure to estimate the concentration of reactive state, the global heat transfer coefficient, and the heat of reaction parameters is proposed here. This scheme consist of an Observer Based Estimator (OBE) connected in cascade with a High Order Sliding Mode Observer (HOSMO). The OBE estimates the global heat transfer coefficient, and the HOSMO estimates the heat of reaction, and the concentration of reactive. Numerical simulations show that the whole structure presents a good performance in presence of parametric variations, which often are presented in chemical processes.Universidad Nacional de ColombiaCINVESTA