Robust neural network control of MEMS gyroscope using adaptive sliding mode compensator

Robotica ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 497-512 ◽  
Author(s):  
Juntao Fei ◽  
Yuzheng Yang
Keyword(s):  
Neural Network ◽  
Upper Bound ◽  
Tracking Control ◽  
Sliding Mode ◽  
Tracking Error ◽  
Modeling Error ◽  
External Disturbances ◽  
Mems Gyroscope ◽  
Control Scheme ◽  
The Impact

SUMMARYA new robust neural sliding mode (RNSM) tracking control scheme using radial basis function (RBF) neural network (NN) is presented for MEMS z-axis gyroscope to achieve robustness and asymptotic tracking error convergence. An adaptive RBF NN controller is developed to approximate and compensate the large uncertain system dynamics, and a robust compensator is designed to eliminate the impact of NN modeling error and external disturbances for guaranteeing the asymptotic stability property. Moreover, another RBF NN is employed to learn the upper bound of NN modeling error and external disturbances, so the prior knowledge of the upper bound of system uncertainties is not required. All the adaptive laws in the RNSM control system are derived in the same Lyapunov framework, which can guarantee the stability of the closed loop system. Comparative numerical simulations for an MEMS gyroscope are investigated to verify the effectiveness of the proposed RNSM tracking control scheme.

Robust Guidance Control for Nonholonomic AGV Based on First Order Dynamic Sliding Mode Techniques

2013 ◽  
Vol 709 ◽  
pp. 583-588
Author(s):  
Jin Hua Ye ◽  
Di Li ◽  
Shi Yong Wang ◽  
Feng Ye
Keyword(s):  
High Performance ◽  
Sliding Mode ◽  
Path Following ◽  
Lyapunov Theory ◽  
External Disturbances ◽  
Guidance Control ◽  
First Order ◽  
Control Scheme ◽  
The Impact ◽  
Dynamic Sliding Mode

This paper develops a high performance guidance controller for automated guided vehicle (AGV) with nonholonomic constraint. In this controller, the path following method in the Serret-Frenet frame is used for driving the AGV onto a predefined path at a constant forward speed. Moreover, a first order dynamic sliding mode controller is proposed, not only to overcome the impact of unknown model uncertainties and external disturbances of the system, but also to weaken the chattering in the standard sliding mode control. The global asymptotic stability and robustness of the system is proven by the Lyapunov theory and LaSalles invariance principle. Simulation results show the validity of the proposed guidance control scheme.

scholarly journals Neural Network-Based Adaptive Control of Robotic Manipulator: Application to a Three Links Cylindrical Robot

2017 ◽  
Vol 13 (1) ◽  
pp. 114-122
Author(s):  
Abdul-Basset AL-Hussein
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Robotic Manipulator ◽  
Lyapunov Theorem ◽  
External Disturbances ◽  
The Stability ◽  
Learning Law

A composite PD and sliding mode neural network (NN)-based adaptive controller, for robotic manipulator trajectory tracking, is presented in this paper. The designed neural networks are exploited to approximate the robotics dynamics nonlinearities, and compensate its effect and this will enhance the performance of the filtered error based PD and sliding mode controller. Lyapunov theorem has been used to prove the stability of the system and the tracking error boundedness. The augmented Lyapunov function is used to derive the NN weights learning law. To reduce the effect of breaching the NN learning law excitation condition due to external disturbances and measurement noise; a modified learning law is suggested based on e-modification algorithm. The controller effectiveness is demonstrated through computer simulation of cylindrical robot manipulator.

scholarly journals Fixed-Time Trajectory Tracking Control of Autonomous Surface Vehicle with Model Uncertainties and Disturbances

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jiawen Cui ◽  
Haibin Sun
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Fixed Time ◽  
Convergence Time ◽  
Model Uncertainties ◽  
External Disturbances ◽  
Trajectory Tracking Control ◽  
Integral Sliding Mode ◽  
Control Scheme

The issue of fixed-time trajectory tracking control for the autonomous surface vehicles (ASVs) system with model uncertainties and external disturbances is investigated in this paper. Particularly, convergence time does not depend on initial conditions. The major contributions include the following: (1) An integral sliding mode controller (ISMC) via integral sliding mode surface is first proposed, which can ensure that the system states can follow the desired trajectory within a fixed time. (2) Unknown external disturbances are absolutely estimated by means of designing a fixed-time disturbance observer (FTDO). By combining the FTDO and ISMC techniques, a new control scheme (FTDO-ISMC) is developed, which can achieve both disturbance compensation and chattering-free condition. (3) Aiming at reconstructing the unknown nonlinear dynamics and external disturbances, a fixed-time unknown observer (FTUO) is proposed, thus providing the FTUO-ISMC scheme that finally achieves trajectory tracking of ASVs with unknown parameters. Finally, simulation tests and detailed comparisons indicate the effectiveness of the proposed control scheme.

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.

scholarly journals Adaptive Fixed-Time Sliding Mode Control for Uncertain Twin-Rotor System with Experimental Validation

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Linwu Shen ◽  
Qiang Chen ◽  
Meiling Tao ◽  
Xiongxiong He
Keyword(s):  
Sliding Mode ◽  
Setting Time ◽  
Tracking Error ◽  
Fixed Time ◽  
External Disturbances ◽  
Rotor Systems ◽  
Time Control ◽  
Control Scheme ◽  
Comparative Results ◽  
Twin Rotor

This paper proposes an adaptive fixed-time control scheme for twin-rotor systems subject to the inertia uncertainties and external disturbances. First of all, a fixed-time sliding mode surface is constructed and the corresponding controller is developed such that the fixed-time uniform ultimate boundedness of the sliding variable and tracking error could be guaranteed simultaneously, and the setting time is independent of the initial values. The adaptive update laws are developed to estimate the upper bounds of the lumped uncertainties and external disturbances such that no prior knowledge on the system uncertainties and disturbances is required. Finally, a twin-rotor platform is constructed to verify the effectiveness of proposed scheme. Comparative results show better position tracking performance of the proposed control scheme.

scholarly journals Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yuzheng Yang ◽  
Juntao Fei
Keyword(s):  
Neural Network ◽  
Neural Control ◽  
Microelectromechanical Systems ◽  
Sliding Mode ◽  
Tracking Error ◽  
Approximation Error ◽  
Adaptive Sliding Mode Control ◽  
Feedback Gain ◽  
Mems Gyroscope ◽  
Unknown System

An adaptive sliding controller using radial basis function (RBF) network to approximate the unknown system dynamics microelectromechanical systems (MEMS) gyroscope sensor is proposed. Neural controller is proposed to approximate the unknown system model and sliding controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. Online neural network (NN) weight tuning algorithms, including correction terms, are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. The tracking error bound can be made arbitrarily small by increasing a certain feedback gain. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness.

scholarly journals Neuro-Sliding Control for Underwater ROV’s Subject to Unknown Disturbances

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2943 ◽  
Author(s):  
Luis Govinda García-Valdovinos ◽  
Fernando Fonseca-Navarro ◽  
Joanes Aizpuru-Zinkunegi ◽  
Tomas Salgado-Jiménez ◽  
Alfonso Gómez-Espinosa ◽  
...  
Keyword(s):  
Sliding Mode ◽  
Tracking Error ◽  
Fast Response ◽  
External Disturbances ◽  
Sliding Control ◽  
Model Free ◽  
Control Scheme ◽  
Parameter Variations ◽  
Unknown Disturbances ◽  
Chattering Free

Proposed in this paper is a model-free and chattering-free second order sliding mode control (2nd-SMC) in combination with a backpropagation neural network (BP-NN) control scheme for underwater vehicles to deal with external disturbances (i.e., ocean currents) and parameter variations caused, for instance, by the continuous interchange of tools. The compound controller, here called the neuro-sliding control (NSC), takes advantage of the 2nd-SMC robustness and fast response to drive the position tracking error to zero. Simultaneously, the BP-NN contributes with its capability to estimate and to compensate online the hydrodynamic variations of the vehicle. When a change in the vehicle’s hydrodynamics occurs, the 2nd-SMC may no longer be able to compensate for the variations since its feedback gains are tuned for a different condition; thus, in order to preserve the desired performance, it is necessary to re-tune the feedback gains, which a cumbersome and time consuming task. To solve this, a viable choice is to implement a BP-NN control scheme along with the 2nd-SMC that adds or removes energy from the system according to the current condition it is in, in order to keep, or even improve, its performance. The effectiveness of the proposed compound controller was supported by experiments carried out on a mini-ROV.

Adaptive sliding mode tracking control of hydraulic servosystems with unknown non-linear friction and modelling error

2000 ◽  
Vol 214 (4) ◽  
pp. 247-257 ◽  
Author(s):  
S H Cho ◽  
K A Edge
Keyword(s):  
Boundary Layer ◽  
Tracking Control ◽  
Sliding Mode ◽  
Tracking Error ◽  
Control Scheme ◽  
Non Linear ◽  
Linear Friction ◽  
Frictional Forces ◽  
Mode Tracking ◽  
Modelling Error

This paper deals with the use of adaptive discrete-time sliding mode tracking control in order to assure good tracking performance as well as to guarantee robustness against non-linear frictional forces and modelling error. The control scheme ensures that the absolute value of the sliding function decreases when it is outside the sliding boundary layer and the steady state value of the sliding function is bounded by the sliding boundary layer. Application of the scheme to a hydraulic servosystem has shown that adaptively estimated frictional forces compare favourably with those obtained from direct measurement. A significant reduction in tracking error is achieved through the use of non-linear friction compensation.

scholarly journals Robust Tracking Control of the Euler–Lagrange System Based on Barrier Lyapunov Function and Self-Structuring Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yi Wang ◽  
He Ma ◽  
Weidong Wu
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Lyapunov Function ◽  
Tracking Control ◽  
Tracking Error ◽  
Robust Tracking ◽  
Robust Tracking Control ◽  
Barrier Lyapunov Function ◽  
Control Scheme ◽  
Unknown Disturbances

This article studies the robust tracking control problems of Euler–Lagrange (EL) systems with uncertainties. To enhance the robustness of the control systems, an asymmetric tan-type barrier Lyapunov function (ATBLF) is used to dynamic constraint position tracking errors. To deal with the problems of the system uncertainties, the self-structuring neural network (SSNN) is developed to estimate the unknown dynamics model and avoid the calculation burden. The robust compensator is designed to estimate and compensate neural network (NN) approximation errors and unknown disturbances. In addition, a relative threshold event-triggered strategy is introduced, which greatly saves communication resources. Under the proposed robust control scheme, tracking behavior can be implemented with disturbance and unknown dynamics of the EL systems. All signals in the closed-loop system are proved to be bounded by stability analysis, and the tracking error can converge to the neighborhood near the origin. The numerical simulation results show the effectiveness and the validity of the proposed robust control scheme.

scholarly journals Integral Sliding Mode Based Finite-Time Tracking Control for Underactuated Surface Vessels with External Disturbances

2021 ◽  
Vol 9 (11) ◽  
pp. 1204
Author(s):  
Yunfei Xiao ◽  
Yuan Feng ◽  
Tao Liu ◽  
Xiuping Yu ◽  
Xianfeng Wang
Keyword(s):  
Sliding Mode Control ◽  
Finite Time ◽  
Tracking Control ◽  
Sliding Mode ◽  
Tracking Error ◽  
External Disturbances ◽  
Mode Control ◽  
Surface Vessels ◽  
Attitude Tracking ◽  
Underactuated Surface Vessels

This study focuses on the problem of finite-time tracking control for underactuated surface vessels (USVs) through sliding-mode control algorithms with external disturbances. Considering the nonexistence of relative degree caused by the underactuated property, the initial tracking error system is firstly transformed to a high order system for the possibility of applying a sliding-mode control algorithm. Subsequently, a finite-time controller based on an integral sliding surface (ISMS) is designed to achieve trajectory tracking. With the aid of this controller, the tracking errors converge to a steady state in a finite time. In contrast to the backstepping-based approach, the proposed method makes it possible to integrate controller design of position tracking and attitude tracking in one step, thus ensuring simplicity for implementation. Finally, theoretical analysis and numerical simulations are conducted to confirm the effectiveness of the proposed method.

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