scholarly journals Adaptive Neural Backstepping Control of Variable Stiffness Actuator

2021 ◽  
Author(s):  
Yu Xia ◽  
Yankui Song ◽  
Jiaxu Wang ◽  
Junyang Li ◽  
Yanfeng Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Tracking Error ◽  
Variable Stiffness ◽  
Backstepping Control ◽  
Performance Bound ◽  
Prescribed Performance ◽  
Chebyshev Neural Network ◽  
Control Scheme ◽  
Output Constraints ◽  
Variable Stiffness Actuator

Abstract In this paper, we provide a novel adaptive neural network backstepping control scheme for a special variable stiffness actuator (VSA) based on lever mechanisms with saturation inputs, output constraints and disturbances is presented here. In the controller designing, the prescribed performance-tangent barrier Lyapunov function (PP-TBLF) is introduced to ensure that both the prescribed performance bound of tracking error and the output constraints are not violated. In specific steps of backstepping control scheme, the Chebyshev neural network and the Nussbaum-type function are used to solve the unknown nonlinearities and unknown gain sign. Meanwhile, the inverse hyperbolic sine function tracking differentiator is exploited to solve the “explosion of complexity” caused by the differentiation of virtual inputs and also approximate the complex partial derivative caused by the auxiliary control signals. Finally, the stability of the whole scheme is proved by Lyapunov criterion and the simulation results are presented to illustrate the feasibility of the raised control strategy.

scholarly journals Multiscale Chebyshev Neural Network Identification and Adaptive Control for Backlash-Like Hysteresis System

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xuehui Gao ◽  
Ruiguo Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Tracking Error ◽  
Closed Loop System ◽  
Chebyshev Neural Network ◽  
Hysteresis Nonlinearity ◽  
Control Scheme ◽  
Network Identification ◽  
Identification Approach ◽  
Adaptive Control Strategy

An adaptive control based on a new Multiscale Chebyshev Neural Network (MSCNN) identification is proposed for the backlash-like hysteresis nonlinearity system in this paper. Firstly, a MSCNN is introduced to approximate the backlash-like nonlinearity of the system, and then, the Lyapunov theorem assures the identification approach is effective. Afterward, to simplify the control design, tracking error is transformed into a scalar error with Laplace transformation. Therefore, an adaptive control strategy based on the transformed scalar error is proposed, and all the signals of the closed-loop system are uniformly ultimately bounded (UUB). Finally, simulation results have demonstrated the performance of the proposed control scheme.

Recurrent Higher Order Neural Network Control for Output Trajectory Tracking with Neural Observers and Constrained Inputs

2010 ◽  
pp. 286-311 ◽  
Author(s):  
Luis J. Ricalde ◽  
Edgar N. Sanchez ◽  
Alma Y. Alanis
Keyword(s):  
Neural Network ◽  
Tracking Error ◽  
Network Control ◽  
Design Parameters ◽  
The Neural Network ◽  
Control Scheme ◽  
Output Trajectory ◽  
Error Dynamics ◽  
Adaptation Law ◽  
Constrained Inputs

This Chapter presents the design of an adaptive recurrent neural observer-controller scheme for nonlinear systems whose model is assumed to be unknown and with constrained inputs. The control scheme is composed of a neural observer based on Recurrent High Order Neural Networks which builds the state vector of the unknown plant dynamics and a learning adaptation law for the neural network weights for both the observer and identifier. These laws are obtained via control Lyapunov functions. Then, a control law, which stabilizes the tracking error dynamics is developed using the Lyapunov and the inverse optimal control methodologies . Tracking error boundedness is established as a function of design parameters.

scholarly journals Adaptive Dynamic Surface Control for Generator Excitation Control System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhang Xiu-yu ◽  
Liu Cui-ping ◽  
Wang Jian-guo ◽  
Lin Yan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Tracking Error ◽  
Dynamic Surface Control ◽  
Excitation Control ◽  
Dynamic Surface ◽  
Adaptive Dynamic ◽  
Surface Control ◽  
Control Scheme ◽  
Generator Excitation Control

For the generator excitation control system which is equipped with static var compensator (SVC) and unknown parameters, a novel adaptive dynamic surface control scheme is proposed based on neural network and tracking error transformed function with the following features: (1) the transformation of the excitation generator model to the linear systems is omitted; (2) the prespecified performance of the tracking error can be guaranteed by combining with the tracking error transformed function; (3) the computational burden is greatly reduced by estimating the norm of the weighted vector of neural network instead of the weighted vector itself; therefore, it is more suitable for the real time control; and (4) the explosion of complicity problem inherent in the backstepping control can be eliminated. It is proved that the new scheme can make the system semiglobally uniformly ultimately bounded. Simulation results show the effectiveness of this control scheme.

scholarly journals Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141668270 ◽  
Author(s):  
Zhonghua Wu ◽  
Jingchao Lu ◽  
Jingping Shi ◽  
Qing Zhou ◽  
Xiaobo Qu
Keyword(s):  
Neural Control ◽  
Tracking Error ◽  
Flight Vehicle ◽  
Model Uncertainties ◽  
Adaptive Neural Control ◽  
Prescribed Performance ◽  
Hypersonic Flight Vehicle ◽  
Hypersonic Flight ◽  
Control Scheme ◽  
Robust Adaptive

A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique–based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.

scholarly journals Backstepping control for a 3DOF model helicopter with input and output constraints

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141668713
Author(s):  
Rong Mei ◽  
Qingliang Cui
Keyword(s):  
Motion Control ◽  
Degrees Of Freedom ◽  
External Disturbance ◽  
Input Saturation ◽  
Backstepping Control ◽  
Input And Output ◽  
Control Scheme ◽  
Output Constraints ◽  
Modelling Uncertainties ◽  
Model Helicopter

In this article, a backstepping control scheme is developed for the motion control of a Three degrees of freedom (3DOF) model helicopter with unknown external disturbance, modelling uncertainties and input and output constraints. In the developed robust control scheme, augmented state observers are applied to estimate the unknown states, unknown external disturbance and modelling uncertainties. Auxiliary systems are designed to deal with input saturation. A barrier Lyapunov function is employed to handle the output saturation. The stability of closed-loop system is proved by the Lyapunov method. Simulation results show that the designed control scheme is effective at dealing with the motion control of a 3DOF model helicopter in the presence of unknown external disturbance and modelling uncertainties, and input and output saturation.

scholarly journals Adaptive Finite-time Dynamic Surface Neural Network Control of an Uncertain Robot with Output Constraint and Input Saturation

2021 ◽  
Author(s):  
Zhao Zhang ◽  
Lingxi Peng ◽  
Zhijia Zhao
Keyword(s):  
Neural Network ◽  
Finite Time ◽  
Control Method ◽  
Tracking Error ◽  
Input Saturation ◽  
Network Control ◽  
Neural Network Control ◽  
Dynamic Surface ◽  
Time Dynamic ◽  
Output Constraints

Abstract In this study, a finite-time dynamic surface neural network control is developed for an uncertain n-link robot subject to input saturation and output constraints. First, a barrier Lyapunov function and a hyperbolic tangent function are applied to solve the system constraints using a dynamic surface control. Subsequently, a radial basis function neural network is utilized to handle system uncertainties. Then, a finite-time filter is employed in the design to achieve the fast convergence and a Nussbaum function is employed to optimize the design process. Finally, the simulation results show that the dynamic tracking error is proved to converging to zero, and the proposed control method is effective and never violates the constraints.

scholarly journals Backstepping Control Based on Constrained Command Filter for Hypersonic Flight Vehicles with AOA and Actuator Constraints

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Junbao Wei ◽  
Haiyan Li ◽  
Ming Guo ◽  
Jing Li ◽  
Huang Huang
Keyword(s):  
Tracking Error ◽  
Lyapunov Stability Theory ◽  
Backstepping Control ◽  
Control Input ◽  
Tracking Errors ◽  
Hypersonic Flight ◽  
Flight Vehicles ◽  
Control Scheme ◽  
Actuator Constraints ◽  
Command Filter

An antisaturation backstepping control scheme based on constrained command filter for hypersonic flight vehicle (HFV) is proposed with the consideration of angle of attack (AOA) constraint and actuator constraints of amplitude and rate. Firstly, the HFV system model is divided into velocity subsystem and height subsystem. Secondly, to handle AOA constraint, a constrained command filter is constructed to limit the amplitude of the AOA command and retain its differentiability. And the constraint range is set in advance via a prescribed performance method to guarantee that the tracking error of the AOA meets the constraint conditions and transient and steady performance. Thirdly, the proposed constrained command filter is combined with the auxiliary system for actuator constraints, which ensures that the control input meets the limited requirements of amplitude and rate, and the system is stable. In addition, the tracking errors of the system are proved to be ultimately uniformly bounded based on the Lyapunov stability theory. Finally, the effectiveness of the proposed method is verified by simulation.

scholarly journals Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Jinzhu Peng ◽  
Zeqi Yang ◽  
Tianlei Ma
Keyword(s):  
Neural Network ◽  
Impedance Control ◽  
Tracking Error ◽  
Tracking Performance ◽  
Robotic Systems ◽  
Position Tracking ◽  
External Disturbances ◽  
Outer Loop ◽  
Control Scheme ◽  
Force Tracking

In this paper, an adaptive Jacobian and neural network based position/force tracking impedance control scheme is proposed for controlling robotic systems with uncertainties and external disturbances. To achieve precise force control performance indirectly by using the position tracking, the control scheme is divided into two parts: the outer-loop force impedance control and the inner-loop position tracking control. In the outer-loop, an improved impedance controller, which combines the traditional impedance relationship with the PID-like scheme, is designed to eliminate the force tracking error quickly and to reduce the force overshoot effectively. In this way, the satisfied force tracking performance can be achieved when the manipulator contacts with environment. In the inner-loop, an adaptive Jacobian method is proposed to estimate the velocities and interaction torques of the end-effector due to the system kinematical uncertainties, and the system dynamical uncertainties and the uncertain term of adaptive Jacobian are compensated by an adaptive radial basis function neural network (RBFNN). Then, a robust term is designed to compensate the external disturbances and the approximation errors of RBFNN. In this way, the command position trajectories generated from the outer-loop force impedance controller can be then tracked so that the contact force tracking performance can be achieved indirectly in the forced direction. Based on the Lyapunov stability theorem, it is proved that all the signals in closed-loop system are bounded and the position and velocity errors are asymptotic convergence to zero. Finally, the validity of the control scheme is shown by computer simulation on a two-link robotic manipulator.

P-CMAC-Based Control Method and its Application in Filament Tension Control

2010 ◽  
Vol 33 ◽  
pp. 101-104
Author(s):  
D.X. Chen ◽  
G. Wang
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Inference ◽  
Control Method ◽  
Hybrid Control ◽  
Tracking Error ◽  
Tension Control ◽  
Approximation Accuracy ◽  
Control Scheme ◽  
Hybrid Control Scheme

Cerebella Model Articulation Controller (CMAC) is considered as local association and generalization neural network. Parametric CMAC (P-CMAC) is a modification to the original CMAC. The introduction of continuous activation function applied in the input space can overcome the binary behavior of the original CMAC. Takagi-Sugeno(TS) type fuzzy inference is embedded in the internal mapping to improve the approximation accuracy. Hybrid control scheme, which combines P-CMAC neural network and traditional PID controller, is proposed in the paper. The output of P-CMAC network dominates the overall control signal applied to the plant, while the traditional PID controller serves as compensator for reducing tracking error. The application of hybrid control scheme to filament tension control is illustrated. The experimental results have shown the effectiveness and accuracy improvement of the control scheme.

Feedforward Input Generation Based on Neural Network Prediction in Multi-Joint Robots1

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Jonathan Asensio ◽  
Wenjie Chen ◽  
Masayoshi Tomizuka
Keyword(s):  
Neural Network ◽  
Feedforward Control ◽  
Industrial Robot ◽  
Tracking Error ◽  
Industrial Applications ◽  
Superior Performance ◽  
Control Scheme ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Learning Data

Learning feedforward control based on the available dynamic/kinematic system model and sensor information is generally effective for reducing the tracking error for a learned trajectory. For new trajectories, however, the system cannot benefit from previous learning data and it has to go through the learning process again to regain its performance. In industrial applications, this means production line has to stop for learning, and the overall productivity of the process is compromised. To solve this problem, this paper proposes a feedforward input generation scheme based on neural network (NN) prediction. Learning/training is performed for the NNs for a set of trajectories in advance. Then the feedforward torque input for any trajectory in the predefined workspace can be calculated according to the predicted error from multiple NNs managed with expert logic. Experimental study on a 6-DOF industrial robot has shown the superior performance of the proposed NN based feedforward control scheme in the position tracking as well as the residual vibration reduction, without any further learning or end-effector sensors during operation.

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