System Identification and Control using Probabilistic Incremental Program Evolution Algorithm

2000 ◽  
Vol 12 (6) ◽  
pp. 675-681 ◽  
Author(s):  
Yuehui Chen ◽  
◽  
Shigeyasu Kawaji
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Prior Knowledge ◽  
Discrete Time ◽  
Control Method ◽  
Fitness Function ◽  
Operating Conditions ◽  
Learning Ability ◽  
Program Evolution ◽  
And Control

An indispensable ability for intelligent control is to comprehend and learn about plants, disturbances, environment, and operating conditions. In this paper, the Probabilistic Incremental Program Evolution (PIPE) algorithm, with its self-organizing and learning ability, is used as a promising tool for such purposes. The previous work on evolutionary control by using tree structure based evolutionary algorithm was inverse control in general. In this case, Genetic Programming (GP) was usually used to evolving a directly control law of nonlinear systems. It is difficult to design a better fitness function that should reflect the characteristics of nonlinear systems, and a prior knowledge about operating conditions is usually needed. In this paper, a new identification and control method is proposed without prior knowledge of the plant. Firstly, the input-output behavior of the discrete-time nonlinear system is approximated by the individual structure of PIPE (PIPE Emulator). Secondly, a model based evolutionary controller (PIPE Emulator-based controller) of nonlinear system is designed. Simulation results for a typical nonlinear discrete-time system show the feasibility and effectiveness of the proposed method.

Fast neural learning and control of discrete-time nonlinear systems

1995 ◽  
Vol 25 (3) ◽  
pp. 478-488 ◽  
Author(s):  
Liang Jin ◽  
P.N. Nikiforuk ◽  
M.M. Gupta
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Neural Learning ◽  
And Control

Disturbance Adaptive Discrete-Time Sliding Control With Application to Engine Speed Control

1998 ◽  
Vol 123 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Mooncheol Won ◽  
J. K. Hedrick
Keyword(s):  
Nonlinear Systems ◽  
Discrete Time ◽  
Control Method ◽  
Sliding Mode ◽  
Input Saturation ◽  
Adaptive Sliding Mode Control ◽  
Control Law ◽  
Sliding Surface ◽  
Sliding Control ◽  
Error Dynamics

This paper presents a discrete-time adaptive sliding control method for SISO nonlinear systems with a bounded disturbance or unmodeled dynamics. Control and adaptation laws considering input saturation are obtained from approximately discretized nonlinear systems. The developed disturbance adaptation or estimation law is in a discrete-time form, and differs from that of conventional adaptive sliding mode control. The closed-loop poles of the feedback linearized sliding surface and the adaptation error dynamics can easily be placed. It can be shown that the adaptation error dynamics can be decoupled from sliding surface dynamics using the proposed scheme. The proposed control law is applied to speed tracking control of an automatic engine subject to unknown external loads. Simulation and experimental results verify the advantages of the proposed control law.

Intervention Time in Target-Oriented Chaos Control

2021 ◽  
Vol 31 (09) ◽  
pp. 2150134
Author(s):  
Juan Segura
Keyword(s):  
Discrete Time ◽  
Chaos Control ◽  
Control Method ◽  
Single Species ◽  
Positive Equilibrium ◽  
Population Stability ◽  
Determine Parameter ◽  
And Control ◽  
Parameter Values ◽  
Proportional Feedback

The timing of interventions plays a central role in managing and exploiting biological populations. However, few studies in the literature have addressed its effect on population stability. The Seno equation is a discrete-time equation that describes the dynamics of single-species populations harvested according to the proportional feedback method at any moment between two consecutive censuses. Here we study a discrete-time equation that generalizes the Seno equation by considering the management and exploitation of populations through the target-oriented chaos control method. We investigate the combined effect of timing, targeting, and control on population stability, focusing on global stability. We prove that high enough control values create a positive equilibrium that attracts all positive solutions. We also prove that it is possible to determine parameter values to stabilize the controlled populations at any preset population size. Finally, we investigate the parameter combinations for which the management and exploitation are optimized in different scenarios.

scholarly journals Designing Stipulated Gains of Aircraft Stability and Control Augmentation Systems for Semiglobal Trajectories Tracking

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Mohamed Mostafa Y. B. Elshabasy ◽  
Yongki Yoon ◽  
Ashraf Omran
Keyword(s):  
Control Method ◽  
Simple Procedure ◽  
Operating Conditions ◽  
Stability And Control ◽  
Wide Range ◽  
Flight Envelope ◽  
The Stability ◽  
Classical Control ◽  
And Control ◽  
Aircraft Stability And Control

The main objective of the current investigation is to provide a simple procedure to select the controller gains for an aircraft with a largely wide complex flight envelope with different source of nonlinearities. The stability and control gains are optimally devised using genetic algorithm. Thus, the gains are tuned based on the information of a single designed mission. This mission is assigned to cover a wide range of the aircraft’s flight envelope. For more validation, the resultant controller gains were tested for many off-designed missions and different operating conditions such as mass and aerodynamic variations. The results show the capability of the proposed procedure to design a semiglobal robust stability and control augmentation system for a highly maneuverable aircraft such as F-16. Unlike the gain scheduling and other control design methodologies, the proposed technique provides a semi-global single set of gains for both aircraft stability and control augmentation systems. This reduces the implementation efforts. The proposed methodology is superior to the classical control method which rigorously requires the linearization of the nonlinear aircraft model of the investigated highly maneuverable aircraft and eliminating the sources of nonlinearities mentioned above.

A novel global harmony search method based off-line tuning of RFNN for adaptive control of uncertain nonlinear systems

2015 ◽  
Vol 8 (1) ◽  
pp. 69-98 ◽  
Author(s):  
Fouad Allouani ◽  
Djamel Boukhetala ◽  
Fares Boudjema ◽  
Gao Xiao-Zhi
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Control Method ◽  
Harmony Search ◽  
Optimization Method ◽  
Uncertain Nonlinear Systems ◽  
Content Type ◽  
Supervisory Controller ◽  
Fuzzy Neural ◽  
Simulation Results

Purpose – The two main purposes of this paper are: first, the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search (GHS) which is a stochastic optimization algorithm recently developed, with the ant colony optimization (ACO) algorithm. Second, design of a new indirect adaptive recurrent fuzzy-neural controller (IARFNNC) for uncertain nonlinear systems using the developed optimization method (GHSACO) and the concept of the supervisory controller. Design/methodology/approach – The novel optimization method introduces a novel improvization process, which is different from that of the GHS in the following aspects: a modified harmony memory representation and conception. The use of a global random switching mechanism to monitor the choice between the ACO and GHS. An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the IARFNNC global structure. Findings – First, to analyze the performance of GHSACO method and shows its effectiveness, some benchmark functions with different dimensions are used. Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search (HS), GHS, improved HS (IHS) and conventional ACO algorithm. In addition, simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS, its variants, particle swarm optimization, and genetic algorithms applied to the same problem. Originality/value – The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS. The proposed control method is applicable to any uncertain nonlinear system belongs in the class of systems treated in this paper.

Identification and control of nonlinear systems using PieceWise Auto-Regressive eXogenous models

2019 ◽  
Vol 41 (14) ◽  
pp. 4050-4062
Author(s):  
Zeineb Lassoued ◽  
Kamel Abderrahim
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Linear Systems ◽  
Experimental Results ◽  
Pole Placement ◽  
Nonlinear Behaviour ◽  
Auto Regressive ◽  
Identification Approach ◽  
Arbitrary Precision ◽  
And Control

In this paper, we consider the problems of nonlinear system representation and control. In fact, we propose a solution based on PieceWise Auto-Regressive eXogenous (PWARX) models since these models are able to approximate any nonlinear behaviour with arbitrary precision. Moreover, the identification and control approaches of linear systems can be extended to these models because the parameters of each sub-model are linearly related to the output. The proposed solution is based on two steps. The first allows to represent the nonlinear system by a PWARX model using the identification approach. The second consists in designing a controller for each sub-model using the pole placement strategy. Simulation and experimental results are presented to illustrate the performance of the proposed approach.

Data-driven model-free sliding mode learning control for a class of discrete-time nonlinear systems

2020 ◽  
Vol 42 (13) ◽  
pp. 2533-2547
Author(s):  
Lei Cao ◽  
Shouli Gao ◽  
Dongya Zhao
Keyword(s):  
Nonlinear System ◽  
Nonlinear Systems ◽  
Discrete Time ◽  
Sliding Mode ◽  
Learning Control ◽  
Linearization Method ◽  
Data Driven ◽  
Controlled System ◽  
Model Free ◽  
The Mathematical Model

This paper proposes a data-driven model-free sliding mode learning control (MFSMLC) for a class of discrete-time nonlinear systems. In this scheme, the control design does not depend on the mathematical model of the controlled system. The nonlinear system can be transformed into a dynamic linear data system by a novel dynamic linearization method. A recursive learning control algorithm is designed for the nonlinear system that can drive the sliding variable reach and remain on the sliding surface only by using output and input data. Moreover, the chattering is reduced because there is no non-smooth term in MFSMLC. After the strict stability analysis, the effectiveness of MFSMLC is validated by MATLAB simulations.

A Method of Finding Discrete Controls for Switched Dynamical Systems by Applying Continued Fractions

2021 ◽  
Vol 24 (2) ◽  
pp. 175-183
Author(s):  
S. M. Khryashchev
Keyword(s):  
Control Systems ◽  
Discrete Time ◽  
Continuous Time ◽  
Continued Fractions ◽  
Control Method ◽  
Initial State ◽  
Final State ◽  
Control Goal ◽  
Controllability Of Systems ◽  
And Control

Control systems with a finite number of control settings are considered. It is assumed that any polysystem operates in continuous time and control switchings occur at some certain discrete time instants. A control goal is to transfer a polysystem from an initial state to a final state. Controllability of systems switched in discrete time is studied. Controls are constructed by using the theory of generalized multicomponent continued fractions and the congruences theory. Applications of the proposed control method to specific systems are discussed.

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