Adaptive fuzzy sliding control for a three-link passive robotic manipulator

Robotica ◽  
2005 ◽  
Vol 23 (5) ◽  
pp. 635-644 ◽  
Author(s):  
Subashini Elangovan ◽  
Peng-Yung Woo
Keyword(s):  
Robotic Manipulator ◽  
Stability And Convergence ◽  
Sliding Control ◽  
Adaptive Fuzzy ◽  
Control Scheme ◽  
Simulation Results ◽  
The Stability

An adaptive fuzzy sliding control scheme is proposed to control a passive robotic manipulator. The motivation for the design of the adaptive fuzzy sliding controller is to eliminate the chattering and the requirement of pre-knowledge on bounds of error associated with the conventional sliding control. The stability and convergence of the adaptive fuzzy sliding controller is proven both theoretically and practically by simulations. A three-link passive manipulator model with two unactuated joints is derived to be used in the simulations. Simulation results demonstrate that the proposed system is robust against structured and unstructured uncertainties.

scholarly journals Stability and Convergence Analysis of Direct Adaptive Inverse Control

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Muhammad Shafiq ◽  
Muhammad A. Shafiq ◽  
Hassan A. Yousef
Keyword(s):  
Computer Simulation ◽  
Convergence Analysis ◽  
Indirect Method ◽  
Stability And Convergence ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Simulation Results ◽  
The Stability ◽  
Feed Forward Controller ◽  
Physical Plants

In adaptive inverse control (AIC), adaptive inverse of the plant is used as a feed-forward controller. Majority of AIC schemes estimate controller parameters using the indirect method. Direct adaptive inverse control (DAIC) alleviates the adhocism in adaptive loop. In this paper, we discuss the stability and convergence of DAIC algorithm. The computer simulation results are presented to demonstrate the performance of the DAIC. Laboratory scale experimental results are included in the paper to study the efficiency of DAIC for physical plants.

scholarly journals An Adaptive Control Scheme for Nonholonomic Mobile Robot with Parametric Uncertainty

10.5772/5801 ◽  
2005 ◽  
Vol 2 (1) ◽  
pp. 8 ◽  
Author(s):  
F. Mnif ◽  
F. Touati
Keyword(s):  
Mobile Robot ◽  
Parametric Uncertainty ◽  
Lyapunov Stability Theory ◽  
Original System ◽  
Stability And Convergence ◽  
Nonholonomic Mobile Robot ◽  
Control Scheme ◽  
The Stability ◽  
Invariant Control ◽  
Time Invariant

This paper addresses the problem of stabilizing the dynamic model of a nonholonomic mobile robot. A discontinuous adaptive state feedback controller is derived to achieve global stability and convergence of the trajectories of the of the closed loop system in the presence of parameter modeling uncertainty. This task is achieved by a non smooth transformation in the original system followed by the derivation of a smooth time invariant control in the new coordinates. The stability and convergence analysis is built on Lyapunov stability theory.

scholarly journals Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Minh-Canh Huynh ◽  
Cheng-Yuan Chang
Keyword(s):  
Active Noise Control ◽  
Neural System ◽  
Stability And Convergence ◽  
Least Mean Square ◽  
Neural Controller ◽  
Active Noise ◽  
Fxlms Algorithm ◽  
Simulation Results ◽  
The Stability ◽  
Narrowband Noise

Noise in a dynamic system is practically unavoidable. Today, such noise is commonly reduced using an active noise control (ANC) system with the filtered-x least mean square (FXLMS) algorithm. However, the performance of the ANC system with FXLMS algorithm is significantly impaired in nonlinear systems. Therefore, this paper develops an efficient nonlinear adaptive feedback neural controller (NAFNC) to eliminate narrowband noise for both linear and nonlinear ANC systems. The proposed controller is implemented to update its coefficients without prior offline training by neural network. Hence, the proposed method has rapid convergence rate as confirmed by simulation results. The proposed work also analyzes the stability and convergence of the proposed algorithm. Simulation results verify the effectiveness of the proposed method.

Position Synchronization of the Biaxial System with a PID Neural Networks Control

2013 ◽  
Vol 462-463 ◽  
pp. 766-770 ◽  
Author(s):  
Wang Yong He ◽  
Cong Qiu ◽  
Jia He
Keyword(s):  
Neural Networks ◽  
Control Algorithm ◽  
Learning Algorithm ◽  
Cross Coupling ◽  
Synchronization Error ◽  
Control Value ◽  
Control Scheme ◽  
Simulation Results ◽  
The Stability

A new control algorithm based on incorporating proportion integration differentiation (PID) neural networks into cross-coupling technology is developed for position synchronization of biaxial system. The PID neural networks controller is be used to adjust control value injecting two velocity loops of the biaxial system.The learning algorithm of PID neural networks is demonstrate in theory. It is tested by simulation that the proposed control scheme can guarantee the stability and the effectiveness of the biaxial control system.The simulation results show that synchronization error is reduced to 35um from 56um.

Control and synchronization for two Chua systems based on intuitionistic fuzzy control scheme: A comparative study

2021 ◽  
pp. 014233122098142
Author(s):  
Mohamed Hamdy ◽  
Mohamed Magdy ◽  
Salah Helmy
Keyword(s):  
Fuzzy Control ◽  
Comparative Study ◽  
Chaotic Systems ◽  
Initial Conditions ◽  
External Disturbances ◽  
Intuitionistic Fuzzy ◽  
Control Scheme ◽  
Simulation Results ◽  
The Stability ◽  
Control And Synchronization

This paper presents control and synchronization for two nonlinear chaotic systems in the presence of uncertainties and external disturbances based on an intuitionistic fuzzy control (IFC) scheme. Two classes of Chua and cubic Chua oscillators have been formulated as master and slave respectively. The master and slave systems have different initial conditions and parameters, which leads to the butterfly effect that rules the chaotic systems’ behaviour. IFC scheme is chosen as a different method that has not been used before to control and synchronize Chua and cubic Chua oscillators. The main objective of the IFC scheme is to collect more information about the system and provide flexibility for the controller that increases the robustness of the control system to uncertainties in the structure of the chaotic systems. The stability analysis of the overall system is guaranteed using Routh-Hurwitz and Lyapunov criteria. The simulation results accomplished to evaluate the effectiveness of the proposed control and to demonstrate its reliability to control Chua’s circuit system with a comparative study.

scholarly journals Finite-Time Adaptive Fuzzy Control for Nonlinear Systems with Unknown Backlash-Like Hysteresis

2021 ◽  
Author(s):  
Shuzhen Diao ◽  
Wei Sun ◽  
Le Wang ◽  
Jing Wu
Keyword(s):  
Fuzzy Control ◽  
Finite Time ◽  
Fuzzy Systems ◽  
Tracking Error ◽  
Adaptive Fuzzy Control ◽  
Finite Time Stability ◽  
Adaptive Fuzzy ◽  
Intermediate Variable ◽  
Control Scheme ◽  
Theoretical Results

AbstractThis study considers the tracking control problem of the nonstrict-feedback nonlinear system with unknown backlash-like hysteresis, and a finite-time adaptive fuzzy control scheme is developed to address this problem. More precisely, the fuzzy systems are employed to approximate the unknown nonlinearities, and the design difficulties caused by the nonlower triangular structure are also overcome by using the property of fuzzy systems. Besides, the effect of unknown hysteresis input is compensated by approximating an intermediate variable. With the aid of finite-time stability theory, the proposed control algorithm could guarantee that the tracking error converges to a smaller region. Finally, a simulation example is provided to further verify the above theoretical results.

Model development and control of an auto-refrigerated polystyrene polymerization reactor

2021 ◽  
pp. 014233122110251
Author(s):  
Reyhane Mokhtarname ◽  
Ali Akbar Safavi ◽  
Leonhard Urbas ◽  
Fabienne Salimi ◽  
Mohammad M Zerafat ◽  
...  
Keyword(s):  
Temperature Control ◽  
Model Development ◽  
Heat Removal ◽  
Superior Performance ◽  
Polymerization Process ◽  
Vacuum System ◽  
Control Scheme ◽  
Simulation Results ◽  
And Control ◽  
Operating Plant

Dynamic model development and control of an existing operating industrial continuous bulk free radical styrene polymerization process are carried out to evaluate the performance of auto-refrigerated CSTRs (continuous stirred tank reactors). One of the most difficult tasks in polymerization processes is to control the high viscosity reactor contents and heat removal. In this study, temperature control of an auto-refrigerated CSTR is carried out using an alternative control scheme which makes use of a vacuum system connected to the condenser and has not been addressed in the literature (i.e. to the best of our knowledge). The developed model is then verified using some experimental data of the real operating plant. To show the heat removal potential of this control scheme, a common control strategy used in some previous studies is also simulated. Simulation results show a faster dynamics and superior performance of the first control scheme which is already implemented in our operating plant. Besides, a nonlinear model predictive control (NMPC) is developed for the polymerization process under study to provide a better temperature control while satisfying the input/output and the heat exchanger capacity constraints on the heat removal. Then, a comparison has been also made with the conventional proportional-integral (PI) controller utilizing some common tuning rules. Some robustness and stability analyses of the control schemes investigated are also provided through some simulations. Simulation results clearly show the superiority of the NMPC strategy from all aspects.

scholarly journals Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Good Prediction ◽  
Foundation Settlement ◽  
Machine Method ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Simulation Results ◽  
Learning Machine ◽  
The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

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