Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer

Abstract

The research of this paper works out the attitude and position control of the flapping wing micro aerial vehicle (FWMAV). Neural network control with full state and output feedback are designed to deal with uncertainties in this complex nonlinear FWMAV dynamic system and enhance the system robustness. Meanwhile, we design disturbance observers which are exerted into the FWMAV system via feedforward loops to counteract the bad influence of disturbances. Then, a Lyapunov function is proposed to prove the closed-loop system stability and the semi-global uniform ultimate boundedness of all state variables. Finally, a series of simulation results indicate that proposed controllers can track desired trajectories well via selecting appropriate control gains. And the designed controllers possess potential applications in FWMAVs.

Time-Varying Formation Control for Time-Delayed Multi-Agent Systems with General Linear Dynamics and Switching Topologies

“…Its research field includes consensus control [1][2][3][4][5][6][7][8], flocking control [9], containment control [10], formation control [11][12][13], rendezvous control [14], etc. As one of the most important research topics, formation control, which aims to propel the states or outputs of all agents toward a desired shape, has broad range of practical applications in various areas, such as unmanned aerial vehicles [15][16][17][18], autonomous underwater vehicles [19], mobile robots [20], etc.…”

Full‐Order Sliding‐Mode Control of Rigid Robotic Manipulators

“…Many tracking control methods have been proposed, such as PID control [1], computed-torque control [2], decentralized control [3], feedback linearization (otherwise known as inverse dynamics control) [4], adaptive control [5], intelligent control (fuzzy control [6], neural-network control [7]), optimal control [8], H ∞ control, [9] iterative learning control [10], model predictive control [11], passive-based control [12], adaptive neural network control [13,14], robust control [15], and sliding-mode control (SMC) [16][17][18][19][20][21][22][23][24][25][26][27]. Among the methods, SMC has attracted significant interest due to its computational simplicity for implementation, high robustness to external disturbances, low sensitivity to parameter variations, and fast dynamic response.…”

“…Among the methods, SMC has attracted significant interest due to its computational simplicity for implementation, high robustness to external disturbances, low sensitivity to parameter variations, and fast dynamic response. Different from the control compensating uncertainties using the neural network in [13,14], SMC utilizes the switching function to deal with uncertainties.…”

Modeling and Control for a Multi-Rope Parallel Suspension Lifting System under Spatial Distributed Tensions and Multiple Constraints

“…The model of MPSLS is relatively complicated when the coupling among the sinking platform, the suspension rope, the lifting rope, Given that the flexible suspension rope is a distributed parameter system, the model of the MPSLS derived from Hamilton's principles is couple PDEs with infinite dimensions, which make it more difficult to control [5]. The general control methods, such as variable structure sliding-mode control [6], modal control [7], neural network control [8,9], etc. [10,11], can be used to suppress the vibration and improve the lifespan of the system, when the PDEs are discretized into ODEs by using Galerkin approximation method or finite element method [12].…”