Quadrotor UAV Position and Altitude Tracking Using an Optimized Fuzzy-Sliding Mode Control

An optimal hybrid quadcopter control technique with MPC-based backstepping

“…Literature reviews such as [14][15][16][17] present a thorough evaluation of the essential state-of-the-art control techniques which could be efficiently employed to quadcopters, such as Backstepping control (BC), MPC, Sliding Mode Control (SMC), Linear-Quadratic Regulator (LQR), H-infinity, Proportional-Integral-Derivative (PID), Adaptive control, Fuzzy logic and Neural Network control, Feedback Linearization (FL) control. Since the quadcopter is a nonlinear system, a few nonlinear control methods have obtained good results in trajectory tracking difficulties such as sliding mode control, nonlinear model predictive control (NMPC), backstepping control design and state feedback linearization control as seen in [12,[18][19][20]. The term "model-based predictive control" (MPC) implies a class of sophisticated control methods that forecast the behavior of the system being controlled using a process model [21] and can even control systems that conventional feedback controllers are unable to control.…”

“…The term "model-based predictive control" (MPC) implies a class of sophisticated control methods that forecast the behavior of the system being controlled using a process model [21] and can even control systems that conventional feedback controllers are unable to control. MPC strategies for trajectory tracking of quadcopter have been presented in [2,19,22]. In [22] MPC was applied to mixed logical dynamical (MLD) model and solved using mixed integer quadratic programming (MIQP) optimization for a small-scale helicopter's obstacle avoidance as a hybrid system for the UAV to choose the best trajectory and avoid obstacles.…”

“…Linear and Nonlinear MPC approaches were compared to demonstrate the effectiveness of the NLMPC, and it was demonstrated that the NLMPC offers better results than Linear MPC (LMPC). In [19], the utilization of some approaches such as SMC, Fuzzy Logic, and GA have been examined to enhance the performance of the modelled Unmanned Aerial Vehicle (UAV) robot sample. A Ducted Fan UAV (DFUAV) trajectory tracking control problem is discussed in [24] using an offset-free MPC approach in the presence of various inconsistencies and external disruptions.…”

USDE-Based Continuous Sliding Mode Control for Quadrotor Attitude Regulation: Method and Application

“…Certainly, the core point behind abovementioned strategies is the selection of disturbance compensators. Up to now, abundant of disturbance estimators like extended state observer (ESO) [23], sliding mode observer (SMO) [25], function approximator-based techniques such as NN [26] and fuzzy logic systems [27] are exploited and incorporated with SMC design to circumvent the discontinuous control inputs. However, one should notice that for ESO [24] and SMO [25] designs requiring a series of calculations of derivative, a higher sensitivity to measurement noise is inevitably encountered.…”

“…Meanwhile, as far as NN is concerned, it is still a nontrivial task to adjust suitable parameters to make weight updating converge within a finite time, and a heavy calculational cost is unavoidably incurred. More importantly, as smaller switching gains are permitted, continuous control behavior is attained by sacrificing dynamic response to some extent, i.e., satisfactory transient profile and control inputs cannot be synchronously assured for the available disturbance compensation-based SMC strategies [25]- [27].…”