【摘要】：從控制工程的角度講,系統(tǒng)的最終控制性能既和被控對(duì)象有關(guān),又受到控制回路中如執(zhí)行器和傳感器等各種物理器件的性能以及通訊信道性能的影響。一方面,被控對(duì)象由于受到系統(tǒng)建模誤差和工作環(huán)境等因素的影響通常具有本質(zhì)非線性和不確定性。另一方面,執(zhí)行器和傳感器往往存在非光滑、非線性約束特性。當(dāng)控制信號(hào)和輸出信號(hào)經(jīng)過(guò)這些約束環(huán)節(jié)時(shí),就會(huì)引起系統(tǒng)性能惡化甚至出現(xiàn)系統(tǒng)失穩(wěn)現(xiàn)象。此外,通訊信道受到網(wǎng)絡(luò)帶寬的限制,控制信號(hào)在傳輸之前進(jìn)行量化在所難免,由此產(chǎn)生的量化誤差同樣會(huì)對(duì)系統(tǒng)控制性能造成極大的負(fù)面影響。鑒于此,本文以backstepping技術(shù)為框架,以模糊邏輯系統(tǒng)作為函數(shù)逼近器,系統(tǒng)地研究具有輸入輸出約束特性的非線性系統(tǒng)的自適應(yīng)控制問(wèn)題。本文共分六章。第一章概述具有輸入輸出約束的非線性系統(tǒng)研究現(xiàn)狀。從第二章開始,主要研究?jī)?nèi)容從五個(gè)部分展開,每一部分作為一章。第二章針對(duì)具有未建模動(dòng)態(tài)和動(dòng)態(tài)干擾的非線性系統(tǒng),提出了一種直接自適應(yīng)模糊輸出反饋控制方案。在設(shè)計(jì)過(guò)程中,引入一個(gè)線性狀態(tài)觀測(cè)器來(lái)估計(jì)系統(tǒng)狀態(tài),采用模糊邏輯系統(tǒng)(Fuzzy Logic Systems, FLS)逼近未知的虛擬控制信號(hào),結(jié)合反步遞推方法設(shè)計(jì)了一種自適應(yīng)模糊控制器,借助小增益定理證明了閉環(huán)系統(tǒng)的輸入-狀態(tài)穩(wěn)定性。該控制方案放寬了以往文獻(xiàn)中對(duì)動(dòng)態(tài)干擾項(xiàng)的強(qiáng)假設(shè)條件,并通過(guò)在線估計(jì)模糊邏輯系統(tǒng)權(quán)向量的范數(shù),減少了在線調(diào)節(jié)的自適應(yīng)參數(shù)個(gè)數(shù),從而加快了自適應(yīng)控制算法的在線運(yùn)行效率。第三章充分考量復(fù)雜工作環(huán)境下執(zhí)行器死區(qū)的不確定性和攝動(dòng)特性,創(chuàng)新性地提出了一種模糊死區(qū)模型,以研究具有不確定死區(qū)輸入的非線性系統(tǒng)的跟蹤控制問(wèn)題。結(jié)合模糊集理論和集成控制思想,首先,針對(duì)具有不可測(cè)量狀態(tài)和模糊死區(qū)輸入的嚴(yán)格反饋非線性系統(tǒng),提出了一種自適應(yīng)綜合控制設(shè)計(jì)方案。該方案保證了閉環(huán)系統(tǒng)的穩(wěn)定性和跟蹤性能。接著,研究了具有模糊死區(qū)輸入的未建模動(dòng)態(tài)非線性系統(tǒng)的跟蹤控制問(wèn)題,利用輔助動(dòng)態(tài)信號(hào)控制未建模動(dòng)態(tài),結(jié)合集成控制思想和動(dòng)態(tài)表面(DSC)技術(shù),設(shè)計(jì)了新穎的自適應(yīng)控制器。第四章針對(duì)實(shí)際控制系統(tǒng)中執(zhí)行器磁滯方向容易發(fā)生跳變的情況,提出了變方向的Bouc-wen磁滯模型�；谠摯艤Ｐ�,研究了具有磁滯輸入的隨機(jī)純反饋非線性系統(tǒng)的自適應(yīng)跟蹤控制問(wèn)題。在引理5.1的基礎(chǔ)上,通過(guò)引入一個(gè)輔助虛擬控制器并利用Nussbaum函數(shù)的性質(zhì),在隨機(jī)非線性系統(tǒng)中解決了磁滯輸入變方向的難題,結(jié)合backstepping技術(shù),提出了一種新穎的自適應(yīng)模糊控制設(shè)計(jì)方案。與已有的磁滯輸入問(wèn)題的研究工作相比,本章所考慮的系統(tǒng)更具一般性,從而擴(kuò)展了磁滯輸入問(wèn)題的應(yīng)用范圍。第五章為抵消輸出傳動(dòng)裝置中的非線性環(huán)節(jié)對(duì)系統(tǒng)性能的負(fù)面影響,研究了具有未知輸出死區(qū)的嚴(yán)格反饋非線性系統(tǒng)的跟蹤控制問(wèn)題。一方面,現(xiàn)有的輸出非線性研究工作都集中在線性系統(tǒng)或者滿足匹配條件的非線性系統(tǒng)的鎮(zhèn)定問(wèn)題上,其方法難以控制比較復(fù)雜的非線性系統(tǒng)(如嚴(yán)格反饋非線性系統(tǒng))的跟蹤控制問(wèn)題。另一方面,實(shí)際系統(tǒng)中的狀態(tài)變量常常難以獲得,這導(dǎo)致以往包含了部分或全部狀態(tài)變量的backstepping設(shè)計(jì)方案也不能直接用來(lái)控制該類系統(tǒng)。本文通過(guò)建立狀態(tài)的非線性函數(shù)與輸出之間的關(guān)系,引入一個(gè)Nussbaum函數(shù)和輔助虛擬控制器,提出了一種全新的控制器設(shè)計(jì)方法,解決了這類復(fù)雜系統(tǒng)的跟蹤控制問(wèn)題。第六章考慮到量化反饋控制在數(shù)字控制、網(wǎng)絡(luò)化控制系統(tǒng)等領(lǐng)域中的廣泛應(yīng)用,研究了具有輸入量化約束的隨機(jī)非線性系統(tǒng)的性能控制問(wèn)題。首先,利用磁滯類量化器的扇形有界性質(zhì)提出了量化器輸出的一種新的非線性分解策略,該策略克服了以往線性分解策略中擾動(dòng)項(xiàng)的界不好確定的問(wèn)題。接著,運(yùn)用這種非線性分解策略,提出了一種新的自適應(yīng)模糊控制方案,解決了具有輸入量化的隨機(jī)嚴(yán)格反饋非線性系統(tǒng)的跟蹤控制問(wèn)題。該方案通過(guò)在線學(xué)習(xí)機(jī)制補(bǔ)償了量化誤差,不需要系統(tǒng)和量化器參數(shù)滿足強(qiáng)的假設(shè)條件,從而在有限通訊頻率下仍能保證系統(tǒng)的跟蹤性能。然后,充分考量未建模動(dòng)態(tài)對(duì)量化反饋非線性系統(tǒng)的負(fù)面影響,研究了具有量化輸入約束的未建模動(dòng)態(tài)隨機(jī)非線性系統(tǒng)的鎮(zhèn)定問(wèn)題。結(jié)合反步遞推技術(shù)和小增益方法,提出了全新的自適應(yīng)模糊控制方案,保證了閉環(huán)系統(tǒng)是依概率輸入-狀態(tài)穩(wěn)定的。
[Abstract]:From the angle of control engineering, the final control performance of the system is not only related to the controlled object but also the performance of various physical devices such as the actuator and the sensor in the control loop, as well as the effect of the communication channel performance. On the one hand, the controlled object is generally nonlinear and uncertain due to the influence of factors such as system modeling error and working environment. on the other hand, the actuators and sensors often have non-smooth, non-linear constraint characteristics. When the control signal and the output signal pass through these restriction links, the system performance degradation and even the system instability can be caused. In addition, the communication channel is limited by the network bandwidth, the control signal is quantified before transmission, and the resulting quantization error also has a great negative effect on the system control performance. In view of this, the adaptive control of nonlinear systems with input and output constraint characteristics is systematically studied by using the backstepping technique as a frame and using the fuzzy logic system as a function approximation. This article is divided into six chapters. The first chapter provides an overview of the research status of nonlinear systems with input and output constraints. From the second chapter, the main research contents are expanded from five parts, each part as a chapter. In the second chapter, a direct adaptive fuzzy output feedback control scheme is proposed for nonlinear systems with unmodeled dynamic and dynamic interference. In the design process, a linear state observer is introduced to estimate the state of the system. The fuzzy logic system (FLS) is used to approximate the unknown virtual control signal, and a self-adaptive fuzzy controller is designed in combination with the reverse-step recursion method. The input-state stability of closed-loop system is proved by means of small gain theorem. The control scheme has the advantages that the strong hypothesis condition of the dynamic interference term in the prior art is relaxed, the norm of the weight vector of the fuzzy logic system is estimated on-line, the number of the self-adaptive parameters of the on-line adjustment is reduced, and the on-line running efficiency of the adaptive control algorithm is accelerated. In the third chapter, the uncertainty and the perturbation characteristic of the dead zone of the actuator in the complex working environment are fully considered, and a fuzzy dead zone model is proposed to study the tracking control problem of the nonlinear system with uncertain dead zone input. Combined with the theory of fuzzy set and the idea of integrated control, a self-adaptive comprehensive control scheme is proposed for the strict feedback nonlinear system with non-measurable state and fuzzy dead zone input. The scheme guarantees the stability and tracking performance of the closed-loop system. Then, the tracking control problem of the unmodeled dynamic nonlinear system with fuzzy dead zone input is studied, and the new adaptive controller is designed by using the auxiliary dynamic signal to control the unmodeled dynamics, combining the integrated control idea and the dynamic surface (DSC) technology. In the fourth chapter, the change of the hysteresis of the actuator in the actual control system is easy to jump, and the Bouc-wen hysteresis model of the variable direction is put forward. Based on the hysteresis model, the self-adaptive tracking control problem of a stochastic pure-feedback nonlinear system with hysteresis input is studied. On the basis of lemma, a novel adaptive fuzzy control scheme is proposed by introducing an auxiliary virtual controller and using the properties of the Nusculum function. In the random nonlinear system, a novel adaptive fuzzy control design scheme is proposed. Compared with the existing research work of the hysteresis input problem, the system considered in this chapter is more general, thus extending the application range of the hysteresis input problem. The fifth chapter is to cancel the negative influence of the non-linear link in the output transmission device on the system performance, and to study the tracking control problem of the strict feedback nonlinear system with unknown output dead zone. On the one hand, the existing output non-linear research work is focused on the stabilization problem of a linear system or a non-linear system satisfying the matching condition, and the method is difficult to control the tracking control problem of a complex nonlinear system (such as a strict feedback nonlinear system). On the other hand, the state variables in the actual system are often difficult to obtain, which results in a backstepping design that has previously included some or all of the state variables and cannot be used directly to control such systems. In this paper, a new controller design method is proposed to solve the tracking control problem of this kind of complex system by establishing the relation between the non-linear function and the output of the state, introducing a Nusculum function and an auxiliary virtual controller. The sixth chapter, taking into account the wide application of the quantitative feedback control in the fields of digital control and networked control system, has studied the performance control problem of the stochastic nonlinear system with input quantization constraint. First, a new non-linear decomposition strategy for the output of a quantizer is proposed by using the sector-specific property of the hysteresis class quantizer, which overcomes the problem that the boundary of the perturbation term in the prior linear decomposition strategy is not well defined. Then, using this non-linear decomposition strategy, a new adaptive fuzzy control scheme is proposed to solve the problem of tracking control with input quantization and random strict feedback nonlinear system. The scheme can compensate the quantization error through the on-line learning mechanism, and does not need the system and the quantizer parameter to meet the strong hypothesis condition, so that the tracking performance of the system can be ensured under the limited communication frequency. Then, the negative influence of the unmodeled dynamics on the quantitative feedback nonlinear system is fully considered, and the stabilization problem of the unmodeled dynamic random nonlinear system with the quantized input constraint is studied. In this paper, a new self-adaptive fuzzy control scheme is proposed in combination with the reverse-step recursive technique and the small-gain method, which ensures that the closed-loop system is stable according to the probability input-state.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP273.4

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