Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

Abstract

As technology improves, the complexity of controlled systems increases as well. Alongside it, these systems need to face new challenges, which are made available by this technology advancement. To overcome these challenges, the incorporation of AI into control systems is changing its status, from being just an experiment made in academia, towards a necessity. Several methods to perform this integration of AI into control systems have been considered in the past. In this work, an approach involving GP to produce, offline, a control law for a reentry vehicle in the presence of uncertainties on the environment and plant models is studied, implemented and tested. The results show the robustness of the proposed approach, which is capable of producing a control law of a complex nonlinear system in the presence of big uncertainties. This research aims to describe and analyze the effectiveness of a control approach to generate a nonlinear control law for a highly nonlinear system in an automated way. Such an approach would benefit the control practitioners by providing an alternative to classical control approaches, without having to rely on linearization techniques.

Universal Stabilisation System for Control Object Motion along the Optimal Trajectory

“…with the constraint ( 16) that the particular solution of the system (15) from the given initial state (3) reaches the given terminal state (4) with the optimal value of the given criterion (17).…”

“…GP is not the most convenient method of symbolic regression because after the crossover operation the codes of mathematical expressions change length and the number of identical arguments of a mathematical expression should be equal to the number of occurrences of that argument in the desired mathematical expression. GP has also been applied to solve control problems [16,17].…”

Revisiting Classical Controller Design and Tuning with Genetic Programming

“…The first one involves the generation of control structures tailored to specific plants or systems. For instance, research developed in [ 40 ] aims to simultaneously generate four controllers for a helicopter to perform hovering maneuvers; studies in [ 41 ] describe the automated synthesis of optimal controllers using multi-objective genetic programming for a two-mass-spring system; in [ 42 ], the objective is about the control of a turbulent jet system; and work in [ 43 ] seeks a control structure for a two-dimensional version of the Goddard rocket problem. In all these studies, despite dealing with complex plants or systems, their function sets only included basic arithmetic operators, like exponential and trigonometric operations.…”

Revisiting Classical Controllers Design and Tuning with Genetic Programming

“…The first one involves the generation of control structures tailored to specific plants or systems. For instance, research developed in [40] aims to simultaneously generate four controllers for a helicopter to perform hovering maneuvers; studies in [41] describe the automated synthesis of optimal controllers using multi-objective genetic programming for a two-mass-spring system; in [42], the objective is about the control of a turbulent jet system; and work in [43] seeks a control structure for a twodimensional version of the Goddard rocket problem. In all these studies, despite dealing with complex plants or systems, their function sets only included basic arithmetic operators, like exponential and trigonometric operations.…”