Identification and adaptive PID Control of a hexacopter UAV based on neural networks

Abstract

In this paper, a novel adaptive PID controller for trajectory-tracking tasks is proposed. It is implemented in discrete time over a hexacopter, and it takes into consideration the unmanned aerial vehicles (UAVs) nonlinear model. The PID controller is developed following an adaptive neural technique, and its stability is verified by the Lyapunov discrete theory. Besides, the neural identification of the dynamic model of the UAV is presented to backpropagate output errors to adjust PID gains with the purpose of reducing the control errors. The validation of the proposed algorithm is performed through experimental results with a hexacopter. KEYWORDSadaptive PID, discrete stability analysis, hexacopter, identification, neural networks 74 ;33:74-91. ROSALES ET AL. 75steady-state errors, and the derivative gain is used to modify any system overshoot. However, since quadrotor is a nonlinear underactuated systems, 8 it is not always possible to use PID control directly for the quadrotor system. Consequently, some authors proposed controllers designed based only on static PID for the UAV control such as the controllers presented in other works. [9][10][11] Other studies proposed the UAV control based on PID controllers with artificial intelligence and dynamic properties, such as the controllers presented in the work of Yang et al, 12 where authors defined a single-neuron PID, where the adjusting of the weights is a function of the control errors. Besides, simulations results were displayed to validate the proposal but the stability analysis is omitted. In addition, Xu and Zhou 13 use self-tuning PID controller based on adaptive pole placement to a quadrotor by using a linearized version of the dynamic model. Another class of PID controllers had the capabilities to backpropagate control errors for a better adjust of the controller gains, some of those are described in this work. Tsakalis and Dash 14 proposed an algorithm for  ∞ for online tuning of PID gains. The estimator is very complex to implement, although is robust with respect to the excitation spectrum. The algorithm is validated by using simulations results for different transfer functions. In the work of Liu et al, 15 the design of a Gaussian potential function network PID (GPFN-PID) controller based on GPFN NN was described. The proposed controller had the functions of online training, self-training, and self-adjusting, which made control of the UAV longitudinal channel more effective. However, the results obtained were based on simulations. Babu et al 16 proposed a gradient descent-based methodology for adjusting online the gains of a PID controller based on a cost function defined to penalize errors. The controller is designed only for positioning task and a commercial quadrotor is used to experiment the controller in position task. In the work of Wang et al, 17 an intelligent PID controller based on radial basis functions (RBFs) NN was designed for longitudinal attitude control of a small UAV using a nonlinear model. An RBF NN was utilized for online updatin...

Adaptive finite‐time tracking control for output‐constrained nonlinear systems with non‐strict‐feedback structure

“…Therefore, the system structure is more general than the previous results. In addition, different from these works [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][35][36][37][38][39][40][41][42][43][44][45][46][47][48] this article presents another challenge: how to handle the phenomenon of output constraints.…”

“…Therefore, due to there are terms containing (k 2 b1 − z 2 1 ) in the denominator, the virtual control signal 1 will not become unbounded. By combining (6), (11), and (12), one has:…”

“…Over the past few decades, the design of backstepping control has become the basic method of nonlinear cascade system control, and remarkable achievements have been made . In addition, neural networks and fuzzy logic systems, as two universal approximators, have been widely used for the modeling of unknown nonlinear functions, and adaptive controllers were designed in References by combining adaptive technique with backstepping. Although some great developments have been made in References , the problem of output constraints is not considered.…”

Robust and Adaptive Backstepping Control for Hexacopter UAVs

“…Therefore, combining with (21) and (28), the linear extended state observer based backstepping controller can be obtained as (30) 3) STABILITY OF THE ROBUST CONTROL SYSTEM Lemma 1 (Petros and Jing [69]): In view of V : [0, ∞) ∈ , the solution of the inequalityV ≤ −αV + f , ∀t ≥ t 0 ≥ 0 is as follows:…”

A Neural Network Based Landing Method for an Unmanned Aerial Vehicle with Soft Landing Gears

“…The input delays to the system were further discussed in [25]. In [26][27][28], radial basis function neural network (RBFNN)-based PID controllers were proposed to control quadrotor flying robots without separating the inner loop and the outer loop. However, the multiple non-linear uncertainties and disturbances were not considered in the design approach, which has a sub-optimal impact on the performance of UAVs.…”