Model and robust gain‐scheduled PID control of a bio‐inspired morphing UAV based on LPV method

Abstract

In this paper, a linear parameter-varying (LPV)-based model and robust gainscheduled structural proportion integral and derivative (PID) control design solution are proposed and applied on a bio-inspired morphing wing unmanned aerial vehicle (UAV) for the morphing process. In the LPV model method, the authors propose an improved modeling method for LPV systems. The method combines partial linearization and function substitution. Using the proposed method, we can choose the varying parameters simply, thus creating a model that is more flexible and applicable. Then, a robust gain-scheduled structural PID control design method is given by introducing a structural matrix to design a structural PID controller, which is more consistent with the structure of the PID controller used in practice and has a simpler structure than representative ones in the existing literature. The simulation results show that the developed LPV morphing UAV model is able to catch the response of the original nonlinear model with a smaller error than the existing Jacobian linearization method and the designed controller can maintain stable flights in practice with satisfactory robustness and performance.

Data‐driven generalized predictive control for car‐like mobile robots using interval type‐2 T‐S fuzzy neural network

“…The first two methods need full or partial system dynamics to determine the structure and parameters of the system. In the literature [1][2][3][4][5][6][7], they discussed the problem of modeling with the first two modeling methods for CLMR and established the kinematics model of CLMR. However, due to the inherent uncertainty and nonlinearity, it is difficult to establish an accurate mathematical model.…”

“…Also, to overcome the uncertain information such as identification error, parameter perturbation, and internal and external interference, the dynamic model is an interesting research topic in recent years. Therefore, many effective control schemes have been introduced, including nonlinear model predictive control [1], robust control [2,3], Lyapunov method [4,5], and adaptive control [6,7]. However, these control methods inevitably need debugging parameters, need a very suitable model, or weak anti-interference ability.…”

“…So far, although some control models have been designed for CLMR, they are difficult to include all nonlinear factors[1][2][3][4][5][6][7]. The IT2TSFNN proposed in this paper can learn and identify the CLMR model based on historical data.…”

Study of anti‐windup pi controllers with different coupling–decoupling tuning gains in motor speed

“…The proportional‐integral‐derivative (PID) family is still a commonly used controller in the industry due to its simplicity in implementation and ability to provide satisfying results. This includes its application in the solar steam turbines [1], hydro turbine governing system [2], the bio‐inspired morphing wing of the unmanned aerial vehicle (UAV) [3] and robotics [4]. The Proportional‐Integral (PI) controller is able to eliminate errors or disturbance in a system but could be subjected to oscillation in their response, large overshoot, and prolonged settling time due to the coupled tuning gains, where the gain affects each other and oppose the response characteristics carried by individual tuning gain.…”

Novel robust gain scheduled PID controller design usingDRregions

“…Gain‐scheduling PID controller design based on quadratic stability and special technique to simplify the corresponding BMI solution is presented in [26]. In [27], an improved modeling method which combines partial linearization and function substitution is proposed to build an LPV model for a nonlinear unmanned aerial vehicle. Then a robust gain‐scheduling PID controller is designed based on bounded real lemma.…”