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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.

This paper presents a 433 MHz low-power receiver utilizing an N-path filter technique and a self-frequency tracking mechanism. Without the front-end amplifier, the mixer-first architecture can reduce power consumption significantly. A self-adjusted frequency tracking loop (SA-FTL) adjusts local oscillator (LO) frequency to approach input RF frequency automatically, thereby enhancing conversion gain and lowering return loss. The receiver, implemented in a $0.18~\mu \text{m}$ CMOS process, achieves a sensitivity of −80 dBm at a bit error rate (BER) of 10−3 and a data rate of 10 kb/s, while consuming $152~\mu \text{W}$ from a 0.8V voltage supply.

In this paper, based on simple quadratic matrix algebra, feedback linearization and neural network methods, the kinematics and almost perturbation decoupling and tracking performances of nonlinear spacecraft attitude control represented by improved Rodriguez parameters are studied. A nonlinear robust attitude controller is proposed to ensure the global stability and the almost disturbance decoupling performance without any learning or adaptation rules used in neural network approach and without having to solve the complex Hamilton-Jacobi equation of H-infinity control approach. This study presents an example that cannot be solved by the first paper on the problem of almost disturbance decoupling, to illustrate the point that this method can easily solve the tracking and almost disturbance decoupling performance. In addition, we propose a novel algorithm and two theorems that design a controller with almost disturbance decoupling and tracking performances. On the basis of meeting the standards we proposed, we successfully carried out some simulations on the spacecraft system to solve the effects of external disturbance torques.