Variational Genetic Programming for Optimal Control System Synthesis of Mobile Robots

Diveev, Askhat; Ibadulla, S.I.; Konyrbaev, N.B.; Shmalko, Elizaveta

doi:10.1016/j.ifacol.2015.12.018

Cited by 20 publications

(6 citation statements)

References 4 publications

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“…The method of parsing matrices [9] encodes mathematically in the form of an integral nonsquare matrix consisting of vectors describing the calls of the computational blocks. The method of variational genetic programming [10] is the application of the principle of a small basic solution to genetic programming. The method of variational analytical programming [11] is the application of the principle of a small basic solution to analytic programming.…”

Section: The Methods Of Symbolic Regressionmentioning

confidence: 99%

Evolutional Methods for Creating Artificial Intelligence of Robotic Technical Systems

Diveev¹,

Ibadulla²

2018

ARTTE

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This paper considers evolutionary methods of symbolic regression for the creation of artificial intelligence of robotic systems. Methods of symbolic regression are reviewed and the features of their application to the solution of the problem of synthesis of control of robotic systems are indicated. The measure of the complexity of artificial intelligence is determined and the advantage of using the principle of small variations of the basic solution is shown, while creating intelligent control systems. A method of variational genetic programming is described and an example of its use for the synthesis of intellectual control is given.

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Section: The Methods Of Symbolic Regressionmentioning

confidence: 99%

Evolutional Methods for Creating Artificial Intelligence of Robotic Technical Systems

Diveev¹,

Ibadulla²

2018

ARTTE

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“…which does not have a free control vector in the right part. Any partial solution of the differential Equation (17) from initial conditions (11) achieves terminal condition (12), performing all conditions on phase constraints (14) with optimal value of the quality criterion (15).…”

Section: The Problem Of General Control Synthesis As Machine Learning Controlmentioning

confidence: 99%

“…The terminal condition (12) and the phase constraints are added into quality criterion (15), and the integral of the domain of initial conditions is changed onto sum of all initial state points.…”

Section: The Problem Of General Control Synthesis As Machine Learning Controlmentioning

confidence: 99%

Machine Learning Control Based on Approximation of Optimal Trajectories

2021

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The paper is devoted to an emerging trend in control—a machine learning control. Despite the popularity of the idea of machine learning, there are various interpretations of this concept, and there is an urgent need for its strict mathematical formalization. An attempt to formalize the concept of machine learning is presented in this paper. The concepts of an unknown function, work area, training set are introduced, and a mathematical formulation of the machine learning problem is presented. Based on the presented formulation, the concept of machine learning control is considered. One of the problems of machine learning control is the general synthesis of control. It implies finding a control function that depends on the state of the object, which ensures the achievement of the control goal with the optimal value of the quality criterion from any initial state of some admissible region. Supervised and unsupervised approaches to solving a problem based on symbolic regression methods are considered. As a computational example, a problem of general synthesis of optimal control for a spacecraft landing on the surface of the Moon is considered as supervised machine learning control with a training set.

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“…To generate a code of the binary analytic programming we combine into one ordered set a set of arguments (9) of mathematical expression, a set of unit elements (12) , , , , , ,…”

Section: Complete Binary Variational Analytic Programmingmentioning

confidence: 99%

“…In these methods [1][2][3][4][5][6][7][8][9] at search of the optimal solution by a genetic algorithm, it is necessary to carry out the main crossover operation in certain points, or it is essential to adjust the new solution after accomplishment of the crossover. Use of the principle of small variations [10] of the basic solution, as in variational methods of genetic and analytical programming [11][12][13], does not significantly change the situation. It is possible to obtain an incorrect code after some variations, and this requires a correction of the code.…”

Section: Introductionmentioning

confidence: 99%

Complete Binary Variational Analytic Programming for Synthesis of Control at Dynamic Constraints

Diveev

Shmalko

2017

ITM Web Conf.

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Abstract. Dynamic constraints arise at control of the robot moving in the neighborhood of other robots. In this case, together with ensuring stability of the robot in a goal point it is necessary to provide instability for the robot in the area of state space where is some other robot. For the solution of this task, we use a new method of symbolic regression -a method of binary variational analytical programming. The new method has advantages in comparison with genetic or analytical programming in case of performing crossover, and it uses the principle of small variations of the basic solution to increase the efficiency of an algorithm of search of the optimal solution.

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Variational Genetic Programming for Optimal Control System Synthesis of Mobile Robots

Cited by 20 publications

References 4 publications

Evolutional Methods for Creating Artificial Intelligence of Robotic Technical Systems

Evolutional Methods for Creating Artificial Intelligence of Robotic Technical Systems

Machine Learning Control Based on Approximation of Optimal Trajectories

Complete Binary Variational Analytic Programming for Synthesis of Control at Dynamic Constraints

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