scholarly journals Robust Adaptive Full-Order TSM Control Based on Neural Network

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 726 ◽  
Author(s):  
Qianlei Cao ◽  
Chongzhen Cao ◽  
Fengqin Wang ◽  
Dan Liu ◽  
Hui Sun
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
A Priori ◽  
System Model ◽  
A Priori Knowledge ◽  
Control Methods ◽  
Terminal Sliding Mode ◽  
Finite Time Convergence ◽  
Model Free ◽  
Robust Adaptive

Existing full-order terminal sliding mode (FOTSM) control methods often require a priori knowledge of the system model. To tackle this problem, two novel neural-network-based FOTSM control methods were proposed. The first one was model based but did not require knowledge of the uncertainties’ bounds. The second one was model free and did not require knowledge of the system model. Finite-time convergence of the two schemes was verified by theoretical analysis and simulation cases. Meanwhile, the designed methods avoided singularity as well as chattering.

scholarly journals Model-Free High Order Sliding Mode Control with Finite-Time Tracking for Unmanned Underwater Vehicles

2021 ◽  
Vol 11 (4) ◽  
pp. 1836
Author(s):  
Josué González-García ◽  
Néstor Alejandro Narcizo-Nuci ◽  
Luis Govinda García-Valdovinos ◽  
Tomás Salgado-Jiménez ◽  
Alfonso Gómez-Espinosa ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Finite Time ◽  
Sliding Mode ◽  
Underwater Vehicles ◽  
High Order ◽  
Time Base ◽  
Unmanned Underwater Vehicles ◽  
Finite Time Convergence ◽  
Model Free

Several strategies to deal with the trajectory tracking problem of Unmanned Underwater Vehicles are encountered, from traditional controllers such as Proportional Integral Derivative (PID) or Lyapunov-based, to backstepping, sliding mode, and neural network approaches. However, most of them are model-based controllers where it is imperative to have an accurate knowledge of the vehicle hydrodynamic parameters. Despite some sliding mode and neural network-based controllers are reported as model-free, just a few of them consider a solution with finite-time convergence, which brings strong robustness and fast convergence compared with asymptotic or exponential solutions and it can also help to reduce the power consumption of the vehicle thrusters. This work aims to implement a model-free high-order sliding-mode controller and synthesize it with a time-base generator to achieve finite-time convergence. The time-base was included by parametrizing the control gain at the sliding surface. Numerical simulations validated the finite-time convergence of the controller for different time-bases even in the presence of high ocean currents. The performance of the obtained solution was also evaluated by the Root Mean Square (RMS) value of the control coefficients computed for the thrusters, as a parameter to measure the power consumption of the vehicle when following a trajectory. Computational results showed a reduction of up to 50% in the power consumption from the thrusters when compared with other solutions.

Robust adaptive anti-windup wheel slip tracking control for intelligent vehicle with fast terminal sliding mode observer

2020 ◽  
Vol 234 (14) ◽  
pp. 3373-3384
Author(s):  
Jiaxu Zhang ◽  
Shiying Zhou
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Sliding Mode ◽  
Sliding Mode Observer ◽  
Intelligent Vehicle ◽  
Control Law ◽  
Wheel Slip ◽  
Terminal Sliding Mode ◽  
Fast Terminal Sliding Mode ◽  
Robust Adaptive

Aiming at the requirement of the intelligent vehicle for the fast and stable tracking control of the wheel slip, a novel robust adaptive anti-windup wheel slip tracking control method with fast terminal sliding mode observer is proposed. First, a fast terminal sliding mode observer based on equivalent control on the sliding surface is proposed to estimate the states of the wheel slip dynamic system to lay the foundation for the full state feedback control law design. Second, a robust adaptive anti-windup wheel slip tracking control law with lumped uncertainty observer and additional anti-windup dynamics is derived based on Lyapunov-based method. The lumped uncertainty observer utilizes the nonlinear mapping ability of the radius basis function neural network to estimate and compensate the lumped uncertainty of the system, and the unknown optimal weight vector of the radius basis function neural network is updated by adaptive law. The additional anti-windup dynamics is used to suppress the effect of the input saturation on the stability of the system. Finally, the performance of the proposed method is verified through simulations of various maneuvers on vehicle dynamics simulation software.

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