Neural network formation control of a team of tractor–trailer systems

Robotica ◽  
2017 ◽  
Vol 36 (1) ◽  
pp. 39-56 ◽  
Author(s):  
Khoshnam Shojaei
Keyword(s):  
Neural Network ◽  
Formation Control ◽  
Network Formation ◽  
Direct Method ◽  
Dynamic Surface Control ◽  
Loop Formation ◽  
Dynamic Surface ◽  
Control Approach ◽  
On Line ◽  
Virtual Leader

SUMMARYThis paper addresses the formation tracking control of a group of tractor–trailer systems in the presence of model uncertainties. A virtual leader–follower formation technique is used to design a controller in order to force a team of tractor–trailer systems to construct a desired formation configuration. Since tractor–trailer systems have a nonlinear multi-input multi-output model with strong couplings, multi-layer neural networks are employed to overcome unknown nonlinearities and uncertain parameters by using on-line weight tuning algorithms. Neural network approximation errors and external disturbances are also compensated with adaptive robust signals. The dynamic surface control approach has been used to reduce the complexity of the proposed controller effectively. Lyapunov’s direct method proves that all signals in the closed-loop formation control system are bounded and tracking errors converge to a neighborhood of the origin whose size is adjustable. Finally, simulation results will be provided to illustrate the efficiency of the proposed controller.

Concise leader-follower formation control of underactuated unmanned surface vehicle with output error constraints

2021 ◽  
pp. 014233122110471
Author(s):  
Meijiao Zhao ◽  
Yan Peng ◽  
Yueying Wang ◽  
Dan Zhang ◽  
Jun Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Formation Control ◽  
Control Method ◽  
Dynamic Surface Control ◽  
Small Range ◽  
Dynamic Surface ◽  
Unmanned Surface Vehicle ◽  
Control Approach ◽  
Output Error ◽  
Ocean Environment

In this paper, a concise leader-follower formation control approach is presented for a group of underactuated unmanned surface vehicle with dynamic system uncertainties and external environment disturbances, where the output errors are required to be within constraints. To settle the output error constraints, a standard barrier Lyapunov function (BLF) is incorporated into the backstepping control method. Furthermore, the “differential explosion” problem of virtual control laws is avoided by introducing the dynamic surface control. To estimate the unknown dynamic terms, an adaptive neural network is designed and a nonlinear disturbance observer is adopted to compensate for the approximation errors of neural network and ocean environment disturbances. Under the constraint of output error, the presented controller based on standard BLF has simpler structure and better control performance than depended on tan-type BLF. The presented controller can ensure that the formation errors converge to a small range around zero, while the output error constraint requirements are met. All signals in the closed-loop system are bounded, and the numerical simulation further shows the effectiveness of the presented control scheme.

scholarly journals Actor Critic Neural Network-Based Adaptive Control for MEMS Gyroscopes Suffering from Multiresource Disturbances

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chao Jing ◽  
Gangzhu Qiao
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Method ◽  
Control Policy ◽  
Dynamic Surface Control ◽  
Order System ◽  
Dynamic Surface ◽  
Control Approach ◽  
Mems Gyroscopes ◽  
Critic Neural Network

In this paper, an actor critic neural network-based adaptive control scheme for micro-electro-mechanical system (MEMS) gyroscopes suffering from multiresource disturbances is proposed. Faced with multiresource interferences consisting of parametric uncertainties, strong couplings between axes, Coriolis forces, and variable external disturbances, an actor critic neural network is introduced, where the actor neural network is employed to estimate the packaged disturbances and the critic neural network is utilized to supervise the system performance. Hence, strong robustness against uncertainties and better tracking properties can be derived for MEMS gyroscopes. Aiming at handling the nonlinearities inherent in gyroscopes without analytically differentiating the virtual control signals, dynamic surface control (DSC) rather than backstepping control method is employed to divide the 2nd order system into two 1st order systems and design the actual control policy. Moreover, theoretical analyses along with simulation experiments are conducted with a view to validate the effectiveness of the proposed control approach.

An Adaptive Output Feedback Proportional-Integral-Derivative Controller for n-Link Type (m,s) Electrically Driven Mobile Manipulators

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Khoshnam Shojaei
Keyword(s):  
Direct Method ◽  
Dynamic Surface Control ◽  
Mobile Manipulators ◽  
Proportional Integral Derivative ◽  
Dynamic Surface ◽  
Control Approach ◽  
Proportional Integral ◽  
Link Type ◽  
Trajectory Tracking Controller ◽  
High Level

The design of a trajectory tracking controller for a general class of n-link type (m,s) electrically driven wheeled mobile manipulators has been addressed in this paper. In order to achieve a high level of the tracking performance, an adaptive robust proportional-integral-derivative (PID) controller is proposed which only requires position measurements by designing a velocity observer. Integral actions are incorporated into the design of both controller and observer to reduce the steady-state error as much as possible. The dynamic surface control approach is also applied to reduce the design complexity at the actuator level. Lyapunov's direct method is used to guarantee that tracking and observation errors are semiglobally uniformly ultimately bounded. Simulation results are presented to illustrate the effectiveness of the proposed controller for a group of mobile manipulators.

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