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.
Highlights
The air vehicles can be divided into two types including propeller aircraft and jet aircraft according to the engine propulsion method used
Motivated by the aforementioned difficult points, we use feedback linearized approach to design the controller of quadrotor system with the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization, controllable convergence rate and globally exponential stability performances and take the place of traditional linear quadratic regulator (LQR) trial and error method and Jacobian locally linearized approach
The significant novelty of this article is to present optimal controller design for MIMO highly nonlinear quadrotor systems based on feedback linearized and linear quadratic regulator approaches using particle swarm optimization (PSO), and simultaneously achieves the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization, controllable convergence rate, improved suspension and globally exponential stability multiple-performances
Summary
The air vehicles can be divided into two types including propeller aircraft and jet aircraft according to the engine propulsion method used. Motivated by the aforementioned difficult points, we use feedback linearized approach to design the controller of quadrotor system with the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization, controllable convergence rate and globally exponential stability performances and take the place of traditional LQR trial and error method and Jacobian locally linearized approach. The significant novelty of this article is to present optimal controller design for MIMO highly nonlinear quadrotor systems based on feedback linearized and linear quadratic regulator approaches using PSO, and simultaneously achieves the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization, controllable convergence rate, improved suspension and globally exponential stability multiple-performances. (ii) The quadrotor system is firstly designed by applying the feedback linearized approach and linear quadratic regulator using PSO optimization approach that take the place of using traditional Jacobian linearization method with the almost disturbance decoupling performance. (vi)The generalities and implications of this approach are globally valid, whereas the locally Jacobian linearized approach is locally valid
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