Nonlinear PID controller design for a 6-DOF UAV quadrotor system

Abstract

A Nonlinear PID (NLPID) controller is proposed to stabilize the translational and rotational motion of a 6-DOF UAV quadrotor system and enforce it to track a given trajectory with minimum energy and error. The complete nonlinear model of the 6-DOF quadrotor system are obtained using Euler-Newton formalism and used in the design process, taking into account the velocity and acceleration vectors resulting in a more accurate 6-DOF quadrotor model and closer to the actual system. Six NLPID controllers are designed, each for Roll, Pitch, Yaw, Altitude, and the Position subsystems, where their parameters are tuned using GA to minimize a multi-objective Output Performance Index (OPI). The stability of the 6-DOF UAV subsystems has been analyzed in the sense of Hurwitz stability theorem under certain conditions on the gains of the NLPID controllers. The simulations have been accomplished under MATLAB/SIMULINK environment and included three different trajectories, i.e., circular, helical, and square. The proposed NLPID controller for each of the six subsystems of the 6-DOF UAV quadrotor system has been compared with the Linear PID (LPID) one and the simulations showed the effectiveness of the proposed NLPID controller in terms of speed, control energy, and steady-state error.

Quadrotors in the Present Era: A Review

“…UAVs are controlled remotely having varied levels of autonomy and control based on its use in real-life applications [1]. This is a boon in areas inaccessible to human beings and during natural calamities [2]. With the ubiquitous use of UAVs, its market value is expected to increase manifolds.…”

Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System

“…As mentioned in our previous works, the mathematical model of the quadrotor system is derived in Appendix A based on Newton–Euler equation and can be represented in Equation (1), while Newton’s law is used to obtain Equation (2), which is helpful to design a controller for the under-actuated quadrotor system using the acceleration in 3 dimensions [ 23 , 24 ]; all the parameters used are described in Table 1 . …”

Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment

“…Many linear and nonlinear control techniques are available for the design of the motion control layer. [37][38][39][40][41][42][43] Motivation Some of the limitations of previous works are, the previous studies try to adopt an objective function like the shortest and/ or smoothest path in a static or dynamic environment without considering robot size into account. Moreover, grid-based environment modeling suffers from inflexibility in dynamic environments and space wastage.…”