scholarly journals Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1868
Author(s):  
Francesco Marchetti ◽  
Edmondo Minisci
Keyword(s):  
Nonlinear System ◽  
Control Systems ◽  
Control Law ◽  
Reentry Vehicle ◽  
Control Approach ◽  
Controlled Systems ◽  
Technology Advancement ◽  
Highly Nonlinear ◽  
Classical Control ◽  
Nonlinear Control Law

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.

scholarly journals Solving a Class of Nonlinear Inverse Problems Using a Feedback Control Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Mahmoud Tarokh
Keyword(s):  
Feedback Control ◽  
Inverse Problems ◽  
Control Systems ◽  
Stability And Convergence ◽  
Science And Engineering ◽  
New Approach ◽  
Control Approach ◽  
Highly Nonlinear ◽  
Feedback Control Systems ◽  
The Stability

Inverse problems have applications in many branches of science and engineering. In this paper we propose a new approach to solving inverse problems which is based on using concepts from feedback control systems to determine the inverse of highly nonlinear, discontinuous, and ill-conditioned input-output relationships. The method uses elements from least squares solutions that are formed within a control loop. The stability and convergence of the inverse solution are established. Several examples demonstrate the applicability of the proposed method.

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

scholarly journals Robust Synchronization of Delayed Chaotic FitzHugh-Nagumo Neurons under External Electrical Stimulation

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Rehan ◽  
Keum-Shik Hong
Keyword(s):  
Electrical Stimulation ◽  
Nonlinear Control ◽  
Control Law ◽  
Input Control ◽  
Control Schemes ◽  
Multiple Input ◽  
Cognitive Diseases ◽  
Single Input ◽  
Error Dynamics ◽  
Nonlinear Control Law

Synchronization of chaotic neurons under external electrical stimulation (EES) is studied in order to understand information processing in the brain and to improve the methodologies employed in the treatment of cognitive diseases. This paper investigates the dynamics of uncertain coupled chaotic delayed FitzHugh-Nagumo (FHN) neurons under EES for incorporated parametric variations. A global nonlinear control law for synchronization of delayed neurons with known parameters is developed. Based on local and global Lipschitz conditions, knowledge of the bounds on the neuronal states, the Lyapunov-Krasovskii functional, and theL2gain reduction, a less conservative local robust nonlinear control law is formulated to address the problem of robust asymptotic synchronization of delayed FHN neurons under parametric uncertainties. The proposed local control law guarantees both robust stability and robust performance and provides theL2bound for uncertainty rejection in the synchronization error dynamics. Separate conditions for single-input and multiple-input control schemes for synchronization of a wide class of FHN systems are provided. The results of the proposed techniques are verified through numerical simulations.

Control Theory and Decision Making in Organisations A Reconnaissance

1970 ◽  
Vol 3 (3) ◽  
pp. T46-T48 ◽  
Author(s):  
G. L. Mallen
Keyword(s):  
Control Theory ◽  
Control Systems ◽  
Decision Process ◽  
Transfer Functions ◽  
Social Systems ◽  
Simulation Methods ◽  
Industrial Dynamics ◽  
Simulation Approach ◽  
Decision Systems ◽  
Classical Control

Differences between the domains of application of classical control theory and applied cybernetics are examined. It is suggested that a unifying concept for the understanding of both simple mechanical control systems and complex social systems is that of the decision process. Simple decision systems are equated to those for which transfer functions can be specified. Complex systems demand a simulation approach. No prescriptive organisational control theory based on simulation methods yet exists but one is required and is seen to be emerging from such diverse fields as artificial intelligence and Industrial Dynamics.

A Control Approach to Guide Nonpharmaceutical Interventions in the Treatment of COVID-19 Disease Using a SEIHRD Dynamical Model

2021 ◽  
Vol 30 (3) ◽  
pp. 323-346
Author(s):  
Fernando Pazos ◽  
◽  
Flavia E. Felicioni ◽  
Keyword(s):  
Public Health ◽  
Ad Hoc ◽  
Early Stage ◽  
Control Law ◽  
Parametric Uncertainties ◽  
Social Unrest ◽  
Control Approach ◽  
Health Measures ◽  
Optimal Intensity ◽  
Nonpharmaceutical Interventions

The recent worldwide epidemic of COVID-19 disease, for which there are no medications to cure it and the vaccination is still at an early stage, led to the adoption of public health measures by governments and populations in most of the affected countries to avoid the contagion and its spread. These measures are known as nonpharmaceutical interventions (NPIs), and their implementation clearly produces social unrest as well as greatly affects the economy. Frequently, NPIs are implemented with an intensity quantified in an ad hoc manner. Control theory offers a worthwhile tool for determining the optimal intensity of the NPIs in order to avoid the collapse of the healthcare system while keeping them as low as possible, yielding concrete guidance to policymakers. A simple controller, which generates a control law that is easy to calculate and to implement is proposed. This controller is robust to large parametric uncertainties in the model used and to some level of noncompliance with the NPIs.

scholarly journals Sliding mode control of the automobile electro-coating conveying mechanism with a nonlinear disturbance observer

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879574 ◽  
Author(s):  
Wei Yuan ◽  
Guoqin Gao
Keyword(s):  
Sliding Mode Control ◽  
Disturbance Observer ◽  
High Performance ◽  
Sliding Mode ◽  
External Disturbance ◽  
Nonlinear Disturbance Observer ◽  
Control Approach ◽  
Mode Control ◽  
Highly Nonlinear ◽  
Nonlinear Disturbance

The trajectory-tracking performance of the automobile electro-coating conveying mechanism is severely interrupted by highly nonlinear crossing couplings, unmodeled dynamics, parameter variation, friction, and unknown external disturbance. In this article, a sliding mode control with a nonlinear disturbance observer is proposed for high-accuracy motion control of the conveying mechanism. The nonlinear disturbance observer is designed to estimate not only the internal/external disturbance but also the model uncertainties. Based on the output of the nonlinear disturbance observer, a sliding mode control approach is designed for the hybrid series–parallel mechanism. Then, the stability of the closed-loop system is proved by means of a Lyapunov analysis. Finally, simulations with typical desired trajectory are presented to demonstrate the high performance of the proposed composite control scheme.

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