PID, LQR and LQR-PID on a quadcopter platform

Model Order Reduction and Linear Quadratic Regulator Controller Design on A Large Scale Linear Time Invariant System

“…A few of the recent methods available in literature are the pole clustering and pade approximation method [2] by Vishwakarma, the factor division method [3] and order reduction on multi variable large scale LTI systems [4] by S K Tiwari, eigen permutation and pade approximation [5] by Jay Singh, redefined time moments and markov parameters by G Saraswathi [6], [13][14][15] and various optimization methods [7][8]. Similarly, the design strategies employed and available in literature are the PID, LQR and LQR-PID Controller design [9] by Argentim, the comparisons of PID and LQR by Jose [10] and Nasir [11], the robust LQR controller design [12] by Kumar et al etc.…”

Rapid Control Prototyping and PIL Co-Simulation of a Quadrotor UAV Based on NI myRIO-1900 Board

“…Control laws must be designed to work in a real-world scenario and over different flight conditions. Among many control approaches proposed for the dynamics stabilization and path tracking of Quadrotors, we note mainly the PID and Linear Quadratic [7]- [10], Sliding Mode (SMC) [11]- [13], backstepping [14], neural network [15] and Model Predictive (MPC) [16]- [19] control methods.…”

X-Morf: A crash-separable quadrotor that morfs its X-geometry in flight

“…However, tuning each gain separately would be particularly tricky, and this would not ensure that the controllers are robust enough in the case of a large variation in the X geometry. Linear Quadratic Regulator (LQR) control procedures (see [2]) could be used to tackle this issue, or adaptive control methods such as L1 adaptive control or Model Reference Adaptive Control (MRAC) methods (see [1]). The MRAC process is quite straight forward to implement and yields consistent results.…”