Hybrid intelligent adaptive controller for tiltrotor UAV

Abstract

PurposeIn this paper, an innovative hybrid intelligent position control method for vertical take-off and landing (VTOL) tiltrotor unmanned aerial vehicle (UAV) is proposed. So the more accurate the reference position signals tracking, the proposed control system will be better.Design/methodology/approachIn the proposed method, for the vertical flight mode, first the model reference adaptive controller (MRAC) operates and for the horizontal flight, the model predictive control (MPC) will operate. Since the linear model is used for both of these controllers and naturally has an error compared to the real nonlinear model, a neural network is used to compensate for them. So the main novelties of this paper are a new hybrid control design (MRAC & MPC) and a neural network-based compensator for tiltrotor UAV.FindingsThe proper performance of the proposed control method in the simulation results is clear. Also the results showed that the role of compensator is very important and necessary, especially in extreme speed wind conditions and uncertain parameters.Originality/valueNovel hybrid control method. 10;-New method to use neural network as compensator in an UAV.

Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction

“…Figure 13 shows the predicted solar power in Ilam. [46]). Simulations verify that the presented NCPRT2FS has high performances in function approximation and system identification.…”

“…The increasing number of rules represents the increasing number of neurons [39]. Some recent works on T2F neural networks can be seen in many applications such as 2DOF robot control [40], 3 parallel robots control [41], PMSM control [42], water temperature control [43,44], environmental temperature control [45] and UAV control [46]. Tavoosi and Badamchizadeh [47] proposed a T2S with linear "then part" for dynamic modeling.…”

Modeling of Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) for Power Prediction

“…Table 4. presents the comparison of our proposed method with another method (method of [46]). Simulation results show that the proposed NCPRT2FS has high performances in function approximation and system identification.…”

“…Table 4 shows that the number of rules of the proposed NCPRT2FS is almost less than method of [53], accuracy of identification is better than [53], but the training time that achieves by average of 10 times run the program (computer processor: Dual CPU T3200 @ 2.00 GHz 2.00 GHz, RAM: 2.00 GB and MATLAB 2011a), is more than [53]. The references [23,46] presented two different T2F neural structures. They have also been used and evaluated only to identify some theory systems.…”

“…The more number of rules is equal with the more number of neurons [39]. Some recently work on T2F neural networks can be seen in many applications such as 2DOF robot control [40], 3 parallel robot control [41], PMSM control [42], water temperature control [43,44], environmental temperature control [45] and UAV control [46]. Tavoosi and Badamchizadeh [47] proposed a T2S with linear "then part" for dynamic modeling.…”