scholarly journals FLS-Based Nonuniform Trajectory Tracking AILC for Uncertain Nonlinear Systems with Nonsymmetric Dead-Zone Input and Initial State Error

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chunli Zhang ◽  
Xu Tian ◽  
Lei Yan
Keyword(s):  
Target Tracking ◽  
Dead Zone ◽  
Small Neighborhood ◽  
Convergent Series ◽  
Lyapunov Theory ◽  
Uncertain Nonlinear System ◽  
Initial State ◽  
Fuzzy Logic Systems ◽  
Mass Spring ◽  
State Error

This paper proposes an AILC method for uncertain nonlinear system to solve different target tracking problems. The method uses fuzzy logic systems (FLS) to approximate every uncertain term in systems. All closed-loop signals are bounded on 0 , T according to the Lyapunov theory. A time-varying boundary layer and a typical convergent series are introduced to handle initial state error, unknown bounds of errors, and nonuniform target tracking, respectively. The result is that the tracking error’s norm can converge to a small neighborhood along iteration increasing asymptotically. Finally, the simulation results of mass-spring mechanical system show the correctness of the theory and validity of the method.

scholarly journals A Robust Adaptive Trajectory Tracking Algorithm Using SMC and Machine Learning for FFSGRs with Actuator Dead Zones

2019 ◽  
Vol 9 (18) ◽  
pp. 3837 ◽  
Author(s):  
Lin Jia ◽  
Yaonan Wang ◽  
Changfan Zhang ◽  
Kaihui Zhao ◽  
Li Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Steady State ◽  
Trajectory Tracking ◽  
Dead Zone ◽  
Lyapunov Theory ◽  
Tracking Algorithm ◽  
Convergence Time ◽  
Model Uncertainties ◽  
Friction Forces ◽  
State Error

The actuator dead zone of free-form surface grinding robots (FFSGRs) is very common in the grinding process and has a great impact on the grinding quality of a workpiece. In this paper, an improved trajectory tracking algorithm for an FFSGR with an asymmetric actuator dead zone was proposed with consideration of friction forces, model uncertainties, and external disturbances. The presented control algorithm was based on the machine learning and sliding mode control (SMC) methods. The control compensator used neural networks to estimate the actuator’s dead zone and eliminate its effects. The robust SMC compensator acted as an auxiliary controller to guarantee the system’s stability and robustness under circumstances with model uncertainties, approximation errors, and friction forces. The stability of the closed-loop system and the asymptotic convergence of tracking errors were evaluated using Lyapunov theory. The simulation results showed that the dead zone’s non-linearity can be estimated correctly, and satisfactory trajectory tracking performance can be obtained in this way, since the influences of the actuator’s dead zone were eliminated. The convergence time of the system was reduced from 1.1 to 0.8 s, and the maximum steady-state error was reduced from 0.06 to 0.015 rad. In the grinding experiment, the joint steady-state error decreased by 21%, which proves the feasibility and effectiveness of the proposed control method.

scholarly journals RBFNN-Based Nonuniform Trajectory Tracking Adaptive Iterative Learning Control for Uncertain Nonlinear System with Continuous Nonlinearly Input

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chunli Zhang ◽  
Xu Tian ◽  
Lei Yan
Keyword(s):  
Nonlinear System ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Convergent Series ◽  
Iterative Learning ◽  
Time Interval ◽  
Uncertain Nonlinear System ◽  
Initial State ◽  
Adaptive Iterative Learning Control

This paper proposes an adaptive iterative learning control (AILC) method for uncertain nonlinear system with continuous nonlinearly input to solve different target tracking problem. The method uses the radial basis function neural network (RBFNN) to approximate every uncertain term in systems. A time-varying boundary layer, a typical convergent series are introduced to deal with initial state error and unknown bounds of errors, respectively. The conclusion is that the tracking error can converge to a very small area with the number of iterations increasing. All closed-loop signals are bounded on finite-time interval 0 , T . Finally, the simulation result of mass-spring mechanical system shows the correctness of the theory and validity of the method.

Adaptive control based on Barrier Lyapunov function for a class of full-state constrained stochastic nonlinear systems with dead-zone and unmodeled dynamics

2021 ◽  
pp. 014233122098563
Author(s):  
Fei Shen ◽  
Xinjun Wang ◽  
Xinghui Yin
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Lyapunov Function ◽  
Dead Zone ◽  
Small Neighborhood ◽  
Unmodeled Dynamics ◽  
Stochastic Nonlinear Systems ◽  
Dynamic Surface ◽  
Barrier Lyapunov Function ◽  
Full State

This paper investigates the problem of adaptive control based on Barrier Lyapunov function for a class of full-state constrained stochastic nonlinear systems with dead-zone and unmodeled dynamics. To stabilize such a system, a dynamic signal is introduced to dominate unmodeled dynamics and an assistant signal is constructed to compensate for the effect of the dead zone. Dynamic surface control is used to solve the “complexity explosion” problem in traditional backstepping design. Two cases of symmetric and asymmetric Barrier Lyapunov functions are discussed respectively in this paper. The proposed Barrier Lyapunov function based on backstepping method can ensure that the output tracking error converges in the small neighborhood of the origin. This control scheme can ensure that semi-globally uniformly ultimately boundedness of all signals in the closed-loop system. Two simulation cases are proposed to verify the effectiveness of the theoretical method.

scholarly journals Active Disturbance Rejection Position Synchronous Control of Dual-Hydraulic Actuators with Unknown Dead-Zones

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6124
Author(s):  
Lixin Wang ◽  
Dingxuan Zhao ◽  
Fucai Liu ◽  
Qian Liu ◽  
Zhuxin Zhang
Keyword(s):  
Disturbance Rejection ◽  
Position Control ◽  
Dead Zone ◽  
Lyapunov Theory ◽  
Least Square ◽  
Synchronization Control ◽  
Hydraulic Actuator ◽  
Dead Zones ◽  
Hydraulic Actuators ◽  
Active Disturbance Rejection

In this paper, an integrated control strategy of position synchronization control for dual-electro-hydraulic actuators with unknown dead-zones is proposed. The unified control scheme consists of two parts: One is adaptive dead-zone inverse controllers of each hydraulic actuator to offset the unknown dead-zones. The other is the linear active disturbance rejection controller (LADRC) for position synchronization error. First, the model of the electro-hydraulic proportional position control system (EPPS) was identified by the forgetting factor recursive least square (FFRLS) algorithm. Next, the model reference dead-zone inverse adaptive controller (MRDIAC) was developed to compensate for the delay of actuator response caused by unknown proportional valve dead-zones. Meanwhile, the validity of the adaptive law was proven by the Lyapunov theory. Therefore, the position control accuracy of each hydraulic actuator is guaranteed. Besides, to improve the precision of position synchronization control of dual-hydraulic actuators, a simple and elegant synchronous error-based LADRC was adopted, which applies the total disturbances design concept to eliminate and compensate for motion coupling rather than cross-coupling technology. The performance of the proposed control solution was investigated through extensive comparative experiments based on a hydraulic test platform. The experimental results successfully demonstrate the effectiveness and practicality of the proposed method.

Particle swarm optimization-tuned adaptive fuzzy output feedback motion tracking control of piezo-positioning mechanism with unknown hysteresis

2019 ◽  
Vol 41 (10) ◽  
pp. 2897-2908 ◽  
Author(s):  
Mohsen Hasanpour Naseriyeh ◽  
Adeleh Arabzadeh Jafari ◽  
Mehrnoosh Zaeifi ◽  
Seyed Mohammad Ali Mohammadi
Keyword(s):  
Particle Swarm Optimization ◽  
Output Feedback ◽  
Motion Tracking ◽  
Tracking Error ◽  
Particle Swarm ◽  
Small Neighborhood ◽  
Output Feedback Control ◽  
Fuzzy Logic Systems ◽  
Swarm Optimization ◽  
Adaptive Fuzzy

This paper considers the problem of observer-based adaptive fuzzy output feedback control for a piezo-positioning mechanism with unknown hysteresis. In this paper, fuzzy logic systems (FLSs) are used to estimate the unknown nonlinear functions, and also Nussbaum function is utilized to overcome the unknown direction hysteresis. Based on the Lyapunov method, the control scheme is constructed by using the backstepping and adaptive technique. In order to better control performance in reducing tracking error, the particle swarm optimization (PSO) algorithm is utilized for tuning the controller parameters. Proposed adaptive controller guarantees that all the closed-loop signals are semiglobally uniformly ultimately bounded (SGUUB) and the tracking error can converge to a small neighborhood of the origin. Finally, the simulation results are provided to demonstrate the effectiveness and robustness of the proposed approach.

scholarly journals Output-Feedback Controller Based Projective Lag-Synchronization of Uncertain Chaotic Systems in the Presence of Input Nonlinearities

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Abdesselem Boulkroune ◽  
Sarah Hamel ◽  
Farouk Zouari ◽  
Abdelkrim Boukabou ◽  
Asier Ibeas
Keyword(s):  
Output Feedback ◽  
Dead Zone ◽  
Output Feedback Control ◽  
Lyapunov Theory ◽  
Positive Real ◽  
Lag Synchronization ◽  
Uncertain Dynamics ◽  
Response Systems ◽  
Projective Lag Synchronization ◽  
Sector Nonlinearities

This paper solves the problem of projective lag-synchronization based on output-feedback control for chaotic drive-response systems with input dead-zone and sector nonlinearities. This class of the drive-response systems is assumed in Brunovsky form but with unavailable states and unknown dynamics. To effectively deal with both dead-zone and sector nonlinearities, the proposed controller is designed in a variable-structure framework. To online learn the uncertain dynamics, adaptive fuzzy systems are used. And to estimate the unavailable states, a simple synchronization error is constructed. To prove the stability of the overall closed-loop system (controller, observer, and drive-response system) and to design the adaptation laws, a Lyapunov theory and strictly positive real (SPR) approach are exploited. Finally, three academic examples are given to show the effectiveness of this proposed lag-synchronization scheme.

scholarly journals Adaptive Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guoqing Xia ◽  
Xingchao Shao ◽  
Ang Zhao ◽  
Huiyong Wu
Keyword(s):  
Neural Network ◽  
Explicit Knowledge ◽  
Dead Zone ◽  
Nonlinear Function ◽  
Lyapunov Theory ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Adaptive Neural Network Control ◽  
Fuzzy Logic Technique

This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship plant. Through backstepping and Lyapunov theory synthesis, an indirect adaptive network controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using fuzzy logic technique to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearity is well compensated. A set of simulations is carried out to verify the performance of the proposed controller.

scholarly journals Direct Adaptive Tracking Control for a Class of Pure-Feedback Stochastic Nonlinear Systems Based on Fuzzy-Approximation

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Huanqing Wang ◽  
Xiaoping Liu ◽  
Qi Zhou ◽  
Hamid Reza Karimi
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Controller Design ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Adaptive Controller ◽  
Stochastic Nonlinear Systems ◽  
Fuzzy Logic Systems ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking

The problem of fuzzy-based direct adaptive tracking control is considered for a class of pure-feedback stochastic nonlinear systems. During the controller design, fuzzy logic systems are used to approximate the packaged unknown nonlinearities, and then a novel direct adaptive controller is constructed via backstepping technique. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error eventually converges to a small neighborhood around the origin in the sense of mean quartic value. The main advantages lie in that the proposed controller structure is simpler and only one adaptive parameter needs to be updated online. Simulation results are used to illustrate the effectiveness of the proposed approach.

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