Abstract
This paper presents a novel iterative learning sliding mode controller (ILSMC) that can be applied to the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) subject to model uncertainties and external disturbances. Here, the proposed ILSMC is integrated in the outer loop of a controlled system. The control development, conducted in the discrete-time domain, does not require a priori information of the disturbance bound as with conventional SMC techniques. It only involves an equivalent control term for the desired dynamics in the closed loop and an iterative learning term to drive the system state toward the sliding surface to maintain robust performance. By learning from previous iterations, the ILSMC can yield very accurate tracking performance when a sliding mode is induced without control chattering. The design is then applied to the attitude control of a 3DR Solo UAV with a built-in PID controller. The simulation results and experimental validation with real-time data demonstrate the advantages of the proposed control scheme over existing techniques.
Highlights
In this paper, integrating the learning capacity of Iterative learning control (ILC) with the strong robustness of Sliding mode control (SMC), we propose a novel iterative learning sliding mode controller (ILSMC) to achieve high-accuracy trajectory tracking for unmanned aerial vehicles (UAVs) while retaining strong robustness as well as alleviating control chattering
We proposed an effective control technique called ILSMC to address the tracking control problem experienced by quadcopters when subject to disturbances and uncertainties
The control signal consists of an equivalent term to control the system states within the desired sliding surface, and an iterative learning term to drive the system states toward the sliding surface and remain in the sliding surface despite the presence of uncertainties and disturbances
Summary
This paper presents a novel iterative learning sliding mode controller (ILSMC) that can be applied to the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) subject to model uncertainties and external disturbances. The control development, conducted in the discrete-time domain, does not require a priori information of the disturbance bound as with conventional SMC techniques. It only involves an equivalent control term for the desired dynamics in the closed loop and an iterative learning term to drive the system state toward the sliding surface to maintain robust performance. Feedback linearization (FL) has been widely used [7,8].
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