Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances

Abstract

Design of tracking controller for quadrotor is an important issue for many engineering fields such as COVID-19 epidemic prevention, intelligent agriculture, military photography and rescue nowadays. This study applies the feedback linearized method and linear quadratic regulator (LQR) method using particle swarm optimization (PSO) to analysis and stabilize the highly nonlinear quadrotor system without applying any nonlinear function approximator that includes neural network approach and fuzzy approach. The article proposes a new method based on the firstly proposed convergence rate formula to achieve the optimal weighting matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally achieved via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This article firstly uses the convergence rate formula of the nonlinear system as the fitness function of LQR approach by using PSO to take the place of the trial and error method. The generalities and implications of proposed approach are globally valid, whereas the Jacobian linearized approach is locally valid due to the Taylor expansion theorem. In addition to these two major achievements, the significant innovation of the proposed method is to possess "simultaneously" additional performances including the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization and globally exponential stability performances. Comparative examples show that the convergence rate with our proposed optimal controller using the PSO algorithm is larger than the fuzzy method, and better than the singular perturbation method with high-gain feedback.INDEX TERMS Almost disturbance decoupling, COVID-19 epidemic prevention , feedback linearized approach, linear quadratic regulator, particle swarm optimization, quadrotor.

Adaptive State Feedback Control Method Based on Recursive Least Squares

“…The LQR technique that is preferred in many control applications is a method that is frequently used in modern optimal control theory and can compute the optimal feedback gain value for stable operation of state feedback systems [18].…”

On Active Vibration Absorption in Motion Control of a Quadrotor UAV

“…Moreover, nonlinear controllers based on sliding modes [32][33][34] and backstepping [35][36][37] approaches have been suitably introduced to deal with disturbances and uncertainties in quadrotor vehicles and, in some studies, further extended for fault tolerant controllers. In addition, important contributions based on theories such as robust H ∞ control [38], model predictive control [39], generalized proportional-integral control [40], energy-based control [41,42], optimal control [21,43], Lyapunov-based control [44], adaptive control [45,46], etc., have vastly improved the performance of quadrotors in regulation and tracking tasks. Nevertheless, to the best knowledge of the authors, there is no previous report taking advantage of the capabilities of virtual vibration absorbers for suppressing undesirable harmonic forces and torques in quadrotor motion trajectory tracking control design.…”

Four-Dimensional Gait Surfaces for a Tilt-Rotor—Two Color Map Theorem

“…In stabilizing a quadrotor, feedback linearization is favored for its unique character in linearizing the nonlinear system [1][2][3][4][5]; several degrees of freedom can be subsequently controlled independently by this method. These degrees of freedom can be selected as attitude and altitude [4,6,7], position and yaw angle [8][9][10][11], attitude only [12], etc.…”