長行程高精度雙級執(zhí)行器建模與控制方法研究
發(fā)布時間：2018-12-17 04:34
【摘要】：長行程和高精度是當(dāng)前驅(qū)動進(jìn)給系統(tǒng)面臨的需求和挑戰(zhàn)。以傳統(tǒng)電機(jī)作為宏執(zhí)行器,雖然能提供較大的行程,但是響應(yīng)速度慢和精度低的缺陷,使其難以在精密定位系統(tǒng)中發(fā)揮作用。而基于智能材料的微執(zhí)行器響應(yīng)速度快、定位精度高,但是行程有限。因此需要合理地設(shè)計宏執(zhí)行器和微執(zhí)行器的控制方案,結(jié)合宏執(zhí)行器和微執(zhí)行器的優(yōu)點(diǎn),使得整個雙級執(zhí)行器實現(xiàn)長行程高精度的定位。主要研究工作和成果如下:(1)考慮一類含有非匹配不確定性的非線性系統(tǒng),Bouc-Wen模型用于描述非線性系統(tǒng)中的遲滯非線性,采用徑向基神經(jīng)網(wǎng)絡(luò)(RBFNN),對非平滑、多映射的遲滯非線性進(jìn)行在線逼近,將時變的遲滯轉(zhuǎn)化為權(quán)值矩陣的學(xué)習(xí),通過李雅普諾夫穩(wěn)定性理論得到權(quán)值矩陣的更新律。設(shè)計的多層滑模自適應(yīng)控制器,能夠有效地解決系統(tǒng)中含有非匹配不確定性的問題,并可將系統(tǒng)跟蹤誤差收斂到預(yù)設(shè)的邊界層內(nèi)。(2)針對一類含有backlash-like遲滯特性的狀態(tài)受限非線性系統(tǒng),基于障礙型李雅普諾夫函數(shù)(BLF)和徑向基神經(jīng)網(wǎng)絡(luò)(RBFNN)設(shè)計了一種輸出反饋控制器。首先,在系統(tǒng)狀態(tài)不可測的情況下,通過BLF解決系統(tǒng)狀態(tài)受限問題。其次引入RBF神經(jīng)網(wǎng)絡(luò)近似逼近backlash-like遲滯中的類擾動項,在類擾動項界未知的情況下,削弱遲滯效應(yīng)對系統(tǒng)的影響。最后通過李雅普諾夫穩(wěn)定性定理設(shè)計控制器,證明了閉環(huán)系統(tǒng)的穩(wěn)定性,仿真結(jié)果表明了該控制方案的可行性。(3)針對質(zhì)量-彈簧-阻尼結(jié)構(gòu)描述的雙級執(zhí)行器,采用解耦控制方式,宏執(zhí)行器跟蹤參考信號,而微執(zhí)行器補(bǔ)償宏執(zhí)行器的跟蹤誤差。只要保證宏執(zhí)行器的跟蹤誤差在微執(zhí)行器的執(zhí)行范圍以內(nèi),就能使整個雙級執(zhí)行器的輸出穩(wěn)定在目標(biāo)值上。為宏執(zhí)行器設(shè)計了近似時間最優(yōu)控制器,能實現(xiàn)快速定位;微執(zhí)行器采用基于預(yù)測控制的離散滑�？刂破�,對系統(tǒng)中匹配的或非匹配的不確定性都有很強(qiáng)的魯棒性,且能有效地削弱滑�？刂菩盘柕亩墩瘛７抡娼Y(jié)果表明了該控制方案的可行性。
[Abstract]:Long stroke and high precision are the requirements and challenges of current drive feed system. Using traditional motor as macro actuator, although it can provide a large stroke, it is difficult to play a role in precision positioning system due to the shortcomings of slow response speed and low precision. The micro actuators based on smart materials have fast response speed and high positioning accuracy, but the stroke is limited. Therefore, it is necessary to design the control scheme of macro actuator and micro actuator reasonably, and combine the advantages of macro actuator and micro actuator to realize the long stroke and high precision positioning of the whole double stage actuator. The main work and results are as follows: (1) considering a class of nonlinear systems with mismatched uncertainties, the Bouc-Wen model is used to describe the hysteresis nonlinearity of nonlinear systems, and the radial basis function neural network (RBFNN),) is used to describe the nonsmoothness. The hysteresis nonlinearity of multiple mappings is approximated on line, and the time-varying hysteresis is transformed into the learning of weight matrix, and the updating law of weight matrix is obtained by Lyapunov stability theory. The multi-layer sliding mode adaptive controller designed can effectively solve the problem of mismatch uncertainty in the system. The tracking error of the system can be converged to the preset boundary layer. (2) for a class of state-constrained nonlinear systems with backlash-like hysteresis, An output feedback controller is designed based on the barrier Lyapunov function (BLF) and the radial basis function neural network (RBFNN). Firstly, under the condition that the system state is unmeasurable, the problem of system state restriction is solved by BLF. Secondly, the RBF neural network is introduced to approximate the perturbation term in backlash-like hysteresis, which weakens the effect of hysteresis on the system when the bound of the perturbation term is unknown. Finally, the stability of the closed-loop system is proved by Lyapunov stability theorem. The simulation results show the feasibility of the control scheme. (3) the two-stage actuator described by mass-spring-damping structure. Using decoupling control, the macro actuator tracks the reference signal, while the micro actuator compensates for the tracking error of the macro actuator. As long as the tracking error of the macro actuator is within the execution range of the microactuator, the output of the whole two-stage actuator can be stabilized at the target value. The approximate time optimal controller is designed for the macro actuator, which can realize fast positioning. The micro actuator adopts a discrete sliding mode controller based on predictive control, which is robust to both matching and mismatching uncertainties, and can effectively reduce the chattering of sliding mode control signal. The simulation results show the feasibility of the control scheme.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP273
本文編號：2383662
[Abstract]:Long stroke and high precision are the requirements and challenges of current drive feed system. Using traditional motor as macro actuator, although it can provide a large stroke, it is difficult to play a role in precision positioning system due to the shortcomings of slow response speed and low precision. The micro actuators based on smart materials have fast response speed and high positioning accuracy, but the stroke is limited. Therefore, it is necessary to design the control scheme of macro actuator and micro actuator reasonably, and combine the advantages of macro actuator and micro actuator to realize the long stroke and high precision positioning of the whole double stage actuator. The main work and results are as follows: (1) considering a class of nonlinear systems with mismatched uncertainties, the Bouc-Wen model is used to describe the hysteresis nonlinearity of nonlinear systems, and the radial basis function neural network (RBFNN),) is used to describe the nonsmoothness. The hysteresis nonlinearity of multiple mappings is approximated on line, and the time-varying hysteresis is transformed into the learning of weight matrix, and the updating law of weight matrix is obtained by Lyapunov stability theory. The multi-layer sliding mode adaptive controller designed can effectively solve the problem of mismatch uncertainty in the system. The tracking error of the system can be converged to the preset boundary layer. (2) for a class of state-constrained nonlinear systems with backlash-like hysteresis, An output feedback controller is designed based on the barrier Lyapunov function (BLF) and the radial basis function neural network (RBFNN). Firstly, under the condition that the system state is unmeasurable, the problem of system state restriction is solved by BLF. Secondly, the RBF neural network is introduced to approximate the perturbation term in backlash-like hysteresis, which weakens the effect of hysteresis on the system when the bound of the perturbation term is unknown. Finally, the stability of the closed-loop system is proved by Lyapunov stability theorem. The simulation results show the feasibility of the control scheme. (3) the two-stage actuator described by mass-spring-damping structure. Using decoupling control, the macro actuator tracks the reference signal, while the micro actuator compensates for the tracking error of the macro actuator. As long as the tracking error of the macro actuator is within the execution range of the microactuator, the output of the whole two-stage actuator can be stabilized at the target value. The approximate time optimal controller is designed for the macro actuator, which can realize fast positioning. The micro actuator adopts a discrete sliding mode controller based on predictive control, which is robust to both matching and mismatching uncertainties, and can effectively reduce the chattering of sliding mode control signal. The simulation results show the feasibility of the control scheme.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP273
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