A new robust fuzzy method for unmanned flying vehicle control

Abstract

A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles (UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. The proposed control system combined great advantages of generalized indirect adaptive sliding mode control (IASMC) and fuzzy control for the UFVs. An on-line adaptive tuning algorithm based on Lyapunov function and Barbalat lemma was designed, thus the stability of the system can be guaranteed. The chattering phenomenon in the sliding mode control was reduced and the steady error was also alleviated. The numerical results, for an underactuated quadcopter and a high speed underwater vehicle as case studies, indicate that the presented adaptive design of fuzzy sliding mode controller performs robustly in the presence of sensor noise and external disturbances. In addition, online unknown parameter estimation of the UFVs, such as ground effect and planing force especially in the cases with the Gaussian sensor noise with zero mean and standard deviation of 0.5 m and 0.1 rad and external disturbances with amplitude of 0.1 m/s 2 and frequency of 0.2 Hz, is one of the advantages of this method. These estimated parameters are then used in the controller to improve the trajectory tracking performance.

Advantages of Fuzzy Control While Dealing with Complex/ Unknown Model Dynamics: A Quadcopter Example

“…Some examples of the aforementioned directions can be found in works like [2] where the equivalence of a FLC with a PID controller together with a relay is explained. Self-regulated fuzzy controllers which incorporate adaptability are also found in the literature as in [4], and robustness enhancement has been achieved by merging fuzzy logic with sliding-mode control as presented in [16] and by Feng [10] who presents a survey on model-based FLC. However, FLC is known as a robust alternative on its own merits [5].…”

“…These tasks involve parameters which are not easily obtained or estimated, and can result in uncertain modeling ( [17,18]) which must be alleviated by dependable controllers. Altogether, quadcopters represent a challenge for control designers nowadays [16].…”

“…A short survey about nonintelligent control techniques applied to quadcopters was presented by Li and Song [5] specifically covering the PID, LQR, H, sliding-mode, feedback linearization, adaptive control, and backstepping techniques, presenting their individual benefits and drawbacks. An adaptable controller based on a real-time quadcopter model was tested by Achtelik et al [18] showing great performance even with complete parametric ignorance; Mirzaei et al [16] present a fuzzy indirect adaptive sliding-mode controller which provide robust performance toward noise and plant uncertainties.…”