scholarly journals Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances

2020 ◽  
Vol 10 (20) ◽  
pp. 7073
Author(s):  
Roxana Recio-Colmenares ◽  
Kelly Joel Gurubel-Tun ◽  
Virgilio Zúñiga-Grajeda
Keyword(s):  
Nonlinear Systems ◽  
Trajectory Tracking ◽  
Control Strategy ◽  
Control Law ◽  
Uncertain Nonlinear Systems ◽  
External Disturbances ◽  
Ant Lion Optimizer ◽  
Highly Nonlinear ◽  
Whale Optimization ◽  
Ant Lion

In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The stabilization of states along the desired trajectory is ensured via a control Lyapunov function. The optimal parameters of the control law are identified by different nature-inspired metaheuristic algorithms, namely: Ant Lion Optimizer, Grey Wolf Optimizer, Harris Hawks Optimization, and Whale Optimization Algorithm. Finally, a highly nonlinear biological system subject to parameter uncertainties and external disturbances (Activated Sludge Model) is proposed to validate the control strategy. Simulation results demonstrate that the control law with Ant Lion Optimizer outperforms the other optimization methods in terms of trajectory tracking in the presence of disturbances. The control law with Harris Hawks Optimization shows a better convergence of the neural states in presence of parameter uncertainty.

H ∞ Based Adaptive Fuzzy Control of a Tower Crane System

2012 ◽  
Author(s):  
Mansour Karkoub ◽  
Tzu Sung Wu ◽  
Chien Ting Chen
Keyword(s):  
Fuzzy Control ◽  
Time Delays ◽  
Adaptive Fuzzy Control ◽  
Tracking Performance ◽  
Control Technique ◽  
Control Law ◽  
External Disturbances ◽  
Adaptive Fuzzy ◽  
Tower Crane ◽  
Highly Nonlinear

Tower cranes are very complex mechanical systems and have been the subject of research investigations for several decades. Research on tower cranes has focused on the development of dynamical models (linear and nonlinear) as well as control techniques to reduce the swaying of the payload. Inherently, the dynamical model of the tower crane is highly nonlinear and classified as under-actuated. The crane system has potentially six degrees of freedom but only three actuators. Also, the actuators are far from the payload which makes the system non-colocated. The dynamic model describing the motion of the payload from point to point is affected by uncertainties, time delays and external disturbances which may lead to inaccurate positioning, reduce safety and efficacy of the overall system. It is proposed here to use an H∞ based adaptive fuzzy control technique to control the swaying motion of a tower crane. This technique will overcome modeling inaccuracies, such as drag and friction losses, effect of time delayed disturbances, as well as parameter uncertainties. The proposed control law for payload positioning is based on indirect adaptive fuzzy control. A fuzzy model is used to approximate the dynamics of the tower crane; then, an indirect adaptive fuzzy scheme is developed for overriding the nonlinearities and time delays. The advantage of employing an adaptive fuzzy system is the use of linear analytical results instead of estimating nonlinear system functions with an online update law. The adaptive fuzzy scheme fuses a Variable Structure (VS) scheme to resolve the system uncertainties, and the external disturbances such that H∞ tracking performance is achieved. A control law is derived based on a Lyapunov criterion and the Riccati-inequality to compensate for the effect of the external disturbances on tracking error so that all states of the system are uniformly ultimately bounded (UUB). Therefore, the effect can be reduced to any prescribed level to achieve H∞ tracking performance. Simulations are presented here to illustrate the performance of the proposed control design.

LS-SVM Adaptive Back-Stepping Control for a Class of Uncertain Nonlinear Systems

2012 ◽  
Vol 588-589 ◽  
pp. 1409-1413
Author(s):  
Guo Dong Zhu ◽  
Hui Lin ◽  
Chen Wang
Keyword(s):  
Support Vector Machine ◽  
Nonlinear Systems ◽  
Least Squares ◽  
System Stability ◽  
Stability And Convergence ◽  
Support Vector ◽  
Control Law ◽  
Uncertain Nonlinear Systems ◽  
Machine Theory ◽  
Back Stepping Control

Based on back-stepping control design, adaptive control and least squares support vector machine theory, a new least squares support vector machine adaptive back-stepping control law was designed for strictly block type of feedback nonlinear systems control with uncertainties. Least squares support vector machine theory method to approximate a nonlinear function of uncertain nonlinear systems by analyzing the disadvantage of common back-stepping. New control law of the nonlinear systems is achieved without accurate mathematical model. The method overcomes the impact of the bounded uncertainties on the control system. On this basis, the system stability and convergence are proved by Lyapunov method. Simulation results indicate that the designed control law has strong robustness and adaptability, uncertainties that exist in the strict block feedback nonlinear systems.

Heterogeneous Nonlinear Systems Synchronization Based on Adaptive Super Twisting

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Oscar Salas-Peña ◽  
Jesús De León-Morales
Keyword(s):  
Nonlinear Systems ◽  
Control Strategy ◽  
Heterogeneous System ◽  
Degrees Of Freedom ◽  
Heterogeneous Systems ◽  
Experimental Results ◽  
Uncertain Nonlinear Systems ◽  
Sliding Modes ◽  
Control Scheme ◽  
Twisting Algorithm

In this work, the synchronization of a group of heterogeneous uncertain nonlinear systems is addressed. A strategy based on adaptive super twisting algorithm is proposed, in order to synchronize the outputs of the heterogeneous systems. With the aim of implementing the proposed control strategy, unmeasurable states are estimated by means of high-order sliding modes differentiators. This control scheme increases robustness against unknown dynamics and disturbances, whose bounds are not required to be known. Finally, experimental results for synchronizing a heterogeneous system platform, constituted by an inertial stabilization platform (ISP) and a helicopter of two degrees-of-freedom (DOF), are used to illustrate the performance of the proposed control scheme.

Robust sliding mode control of uncertain nonlinear systems with chattering alleviating scheme

2021 ◽  
pp. 2140042
Author(s):  
Bhausaheb B. Musmade ◽  
Balasaheb M. Patre
Keyword(s):  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Uncertain Systems ◽  
Sliding Mode ◽  
External Disturbance ◽  
Uncertain Nonlinear Systems ◽  
Model Uncertainties ◽  
External Disturbances ◽  
Controlled System ◽  
Mode Control

In this paper, a class of uncertain nonlinear systems is investigated and a sliding mode control (SMC) design is presented. The method is proposed for uncertain systems with model uncertainties, nonlinear dynamics and external disturbances. Using nominal system and related bounds of uncertainties, a chattering alleviating scheme is also proposed, which can ensure the robust SMC law. The performance and the significance of the controlled system are investigated under variation in system parameters and also in presence of an external disturbance. The simulation results indicate that performance of the proposed controller is effective compared to the existing controllers.

Sign in / Sign up

Export Citation Format

Share Document