本文選題：粒子群算法 + ADS-33�。� 參考：《南昌航空大學(xué)》2015年碩士論文

【摘要】：直升機(jī)存在振動大、操縱難、穩(wěn)定性差等問題,為安全有效地控制直升機(jī),必須設(shè)計性能優(yōu)良的控制系統(tǒng)。傳統(tǒng)的控制方法在控制參數(shù)的選取中,過于依賴于設(shè)計人員的經(jīng)驗,文中采用粒子群算法對控制參數(shù)進(jìn)行智能化的尋優(yōu)設(shè)計。為了提高粒子群算法的搜索效率,文中提出一種改進(jìn)的粒子群算法,采用該改進(jìn)的粒子群算法設(shè)計直升機(jī)顯模型跟蹤控制器和線性二次型控制器,開展了如下研究工作:1、分析直升機(jī)的受力和力矩,依據(jù)牛頓運(yùn)動定律得出了直升機(jī)的六自由度運(yùn)動方程,利用在平衡點(diǎn)附近增加小擾動的方法對六自由度方程進(jìn)行線性化處理,得到直升機(jī)9階線性化模型;2、考慮基本粒子群算法易陷入局部極小值的不足,研究基本粒子群算法中的慣性權(quán)重和學(xué)習(xí)因子的選取后,提出一種自適應(yīng)變換慣性權(quán)重和學(xué)習(xí)因子的改進(jìn)方法,運(yùn)用Schwefel等函數(shù)驗證,該方法具有良好的收斂速度和搜索精度;3、在直升機(jī)顯模型跟蹤控制系統(tǒng)設(shè)計中,首先根據(jù)ADS-33品質(zhì)中懸停和低速飛行狀態(tài)下對直升機(jī)性能指標(biāo)的要求選擇直升機(jī)4個通道的顯模型,然后以跟蹤誤差最小為目標(biāo),采用粒子群算法對顯模型跟蹤控制系統(tǒng)中的積分矩陣和前向增益矩陣進(jìn)行優(yōu)化。仿真結(jié)果表明,系統(tǒng)的跟蹤性、解耦性和魯棒性均達(dá)到了理想的效果;4、在直升機(jī)線性二次型控制中,先針對二次型控制要求對直升機(jī)進(jìn)行能控性分析,設(shè)計出LQR控制器,然后以二次型最優(yōu)指標(biāo)為目標(biāo),采用粒子群算法對Q和R矩陣進(jìn)行優(yōu)化,仿真結(jié)果表明,系統(tǒng)的誤差與魯棒性均達(dá)到了理想的效果。通過粒子群算法對直升機(jī)控制器進(jìn)行優(yōu)化設(shè)計,相比傳統(tǒng)的控制器設(shè)計方法,該方法提高了設(shè)計的效率,同時又能保證系統(tǒng)的性能要求,為控制器的設(shè)計開辟了一條新的途徑。
[Abstract]:In order to control the helicopter safely and effectively, it is necessary to design a control system with good performance. The traditional control method is too dependent on the designer's experience in the selection of control parameters. In this paper, the particle swarm optimization algorithm is used to design the control parameters intelligently. In order to improve the searching efficiency of particle swarm optimization algorithm, an improved particle swarm optimization algorithm is proposed in this paper. The improved particle swarm optimization algorithm is used to design helicopter explicit model tracking controller and linear quadratic controller. The following research work is carried out: 1, the force and torque of the helicopter are analyzed, according to Newton's law of motion, the motion equation of the helicopter with six degrees of freedom is obtained, and the equation of six degrees of freedom is linearized by adding a small disturbance near the equilibrium point. The 9 th order linearization model of helicopter is obtained. Considering the deficiency of elementary particle swarm optimization algorithm which is easy to fall into local minimum, the selection of inertia weight and learning factor in basic particle swarm optimization algorithm is studied. An improved method for adaptive transformation of inertia weight and learning factor is proposed. The method is verified by Schwefel and other functions. The method has good convergence speed and searching precision. It is used in the design of helicopter model tracking control system. Firstly, according to the requirement of helicopter performance index in hover and low speed flight state of ADS-33, the explicit model of four channels of helicopter is selected, and then the minimum tracking error is taken as the target. Particle swarm optimization (PSO) is used to optimize the integral matrix and the forward gain matrix in the explicit model tracking control system. The simulation results show that the tracking, decoupling and robustness of the system are satisfactory. In the linear quadratic control of the helicopter, the controllability of the helicopter is analyzed according to the requirements of the quadratic control, and the LQR controller is designed. Then the quadratic optimal index is used to optimize the Q and R matrices. The simulation results show that the error and robustness of the system are satisfactory. The particle swarm optimization algorithm is used to optimize the design of helicopter controller. Compared with the traditional controller design method, this method can improve the efficiency of the design and at the same time ensure the performance of the system. It opens a new way for the design of the controller.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：V249.12;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 于雪晶;麻肖妃;夏斌;;動態(tài)粒子群優(yōu)化算法[J];計算機(jī)工程;2010年04期

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本文編號：2103091