Scheduling PID Attitude and Position Control Frequencies for Time-Optimal Quadrotor Waypoint Tracking under Unknown External Disturbances

Abstract

Recently, the use of quadrotors has increased in numerous applications, such as agriculture, rescue, transportation, inspection, and localization. Time-optimal quadrotor waypoint tracking is defined as controlling quadrotors to follow the given waypoints as quickly as possible. Although PID control is widely used for quadrotor control, it is not adaptable to environmental changes, such as various trajectories and dynamic external disturbances. In this work, we discover that adjusting PID control frequencies is necessary for adapting to environmental changes by showing that the optimal control frequencies can be different for different environments. Therefore, we suggest a method to schedule the PID position and attitude control frequencies for time-optimal quadrotor waypoint tracking. The method includes (1) a Control Frequency Agent (CFA) that finds the best control frequencies in various environments, (2) a Quadrotor Future Predictor (QFP) that predicts the next state of a quadrotor, and (3) combining the CFA and QFP for time-optimal quadrotor waypoint tracking under unknown external disturbances. The experimental results prove the effectiveness of the proposed method by showing that it reduces the travel time of a quadrotor for waypoint tracking.

Cascaded Control System Design for Quadrotor UAV through Relay with Embedded Integrator-Based Automatic Tuning Approach

“…The frequency sampling filter also provides the fundamental frequency that is determined as ω 1 = 3 67 radians. K p and d are then obtained through (21) and (22). Accordingly, with the help of inner loop frequency response and fundamental frequency information, these are calculated as K p = 0 669 and d = 0 134.…”

“…Several control strategies have been designed for the multirotor UAVs over the years, such as [17][18][19][20][21]. PID controllers are supposed to be the most vastly utilized controllers in the quadrotor control system because of their simplified structure and ease of practical implementation [22,23]. Due to these reasons, presently the majority of the mainstream commercial autopilot flight controllers are using PID controllers as their main control algorithm, i.e., micropilot [24], ardupilot [25], paparazzi autopilot [26], AfroFlight Naze32, and Multiwii SE V2.0.…”

Design and Real-Time Implementation of a Cascaded Model Predictive Control Architecture for Unmanned Aerial Vehicles

“…To meet these requirements, some control strategies have been developed [2][3][4][5][6][7]. First, the well-known PID controller, which can handle both linear and nonlinear systems, can achieve zero steady-state errors with a simple-to-understand tuning principle [2].…”

Metaheuristics-Based Optimization of a Robust GAPID Adaptive Control Applied to a DC Motor-Driven Rotating Beam with Variable Load

“…In this sense, intelligent algorithms have been successfully employed to solve multimodal optimization tasks from various real-world problems. For example, in [21], the authors proposed the use of an improved version of the Salp Swarm Algorithm to deal with feature selection problems; variations of the Moth-flame algorithm, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bees Life Algorithm (BLA) were addressed for cyber-physical system applications in real fog computing; a hybrid proposition among Firefly Algorithm and PSO is proposed in [22] for optimal cluster head selection in wireless sensor networks with the goal of increasing the energy efficiency of the system; notwithstanding, nonlinear control is the target of many studies involving bio-inspired optimization, such as [23][24][25][26][27][28].…”