本文選題：非線性 + 約束��； 參考：《華北電力大學(xué)》2014年博士論文

【摘要】：工業(yè)過程對象由于操作點(diǎn)在整個操作區(qū)域范圍內(nèi)頻繁變化,往往呈現(xiàn)非線性特性。實際工業(yè)過程中還存在大量的物理約束。因此,解約束非線性優(yōu)化控制問題是工業(yè)過程面臨的一個巨大挑戰(zhàn)。模型預(yù)測控制是一種基于模型的先進(jìn)優(yōu)化控制策略,它采用滾動時域方式,并且直接處理過程約束。傳統(tǒng)的模型預(yù)測控制策略往往只針對線性系統(tǒng)有效。一般情況下,非線性模型預(yù)測控制采用序列二次規(guī)劃、罰函數(shù)法、KKT條件等方法來在線解優(yōu)化問題。對大部分復(fù)雜系統(tǒng)來說,得到的非線性優(yōu)化問題往往是非凸的,且在線計算負(fù)擔(dān)很大。本文研究了非線性模型預(yù)測控制優(yōu)化方法和其在復(fù)雜系統(tǒng)中的應(yīng)用,主要工作概括如下：考慮一種仿射型非線性狀態(tài)空間模型,提出了一種基于輸入輸出反饋線性化的非線性模型預(yù)測控制策略。構(gòu)造了可以保證算法收斂性的迭代二次規(guī)劃方法來處理約束。該算法可以在整個預(yù)測時域上得到可行解。在連續(xù)系統(tǒng)數(shù)字例子和連續(xù)攪拌反應(yīng)器的例子中證明了其有效性。所構(gòu)造的基于輸入輸出反饋線性化的預(yù)測控制策略進(jìn)而推廣應(yīng)用到風(fēng)力發(fā)電系統(tǒng)的雙饋電機(jī)控制和永磁同步電機(jī)控制上。這兩種快過程都具有很強(qiáng)的非線性,也包含很多不確定性因素。雙饋電機(jī)的動態(tài)性能和風(fēng)速相關(guān),其轉(zhuǎn)矩是定子和轉(zhuǎn)子電流的非線性函數(shù)。永磁電機(jī)內(nèi)部具有非線性耦合,以及不可測的擾動,而且參數(shù)變化,負(fù)載也會頻繁變化。由于雙饋電機(jī)模型中總相對階小于狀態(tài)變量個數(shù),雙饋電機(jī)只能進(jìn)行輸入輸出反饋線性化。而永磁電機(jī)模型中總相對階等于狀態(tài)變量個數(shù),永磁電機(jī)可以進(jìn)行精確的狀態(tài)反饋線性化。和現(xiàn)有的控制方法相比,這種策略在保證算法收斂性的同時能夠降低在線計算負(fù)擔(dān),保證控制系統(tǒng)的實時性。超超臨界機(jī)組是一種高效、高燃料利用率、低排放的先進(jìn)發(fā)電設(shè)備。由于系統(tǒng)規(guī)模龐大、大范圍變工況下具有強(qiáng)非線性等特性,常規(guī)控制難以實現(xiàn)在負(fù)荷跟蹤和電網(wǎng)頻率擾動情況下的快速穩(wěn)定響應(yīng)的協(xié)調(diào)控制。本文構(gòu)造了包含經(jīng)濟(jì)指標(biāo)優(yōu)化層和跟蹤性能優(yōu)化層的多層控制結(jié)構(gòu),在此多層控制結(jié)構(gòu)基礎(chǔ)上構(gòu)造了超超臨界機(jī)組的分級非線性模型預(yù)測協(xié)調(diào)控制。在此結(jié)構(gòu)中,上層基于超超臨界機(jī)組的非線性模型,對包含經(jīng)濟(jì)指標(biāo)和環(huán)境指標(biāo)的目標(biāo)函數(shù)進(jìn)行優(yōu)化獲得下層機(jī)組運(yùn)行的給定值。上層的目標(biāo)是降低機(jī)組的運(yùn)行成本,抑制機(jī)組本身和電網(wǎng)產(chǎn)生的擾動。下層實現(xiàn)超超臨界機(jī)組對上層產(chǎn)生的優(yōu)化給定值的精確跟蹤。因為分級非線性模型預(yù)測協(xié)調(diào)控制的優(yōu)化問題是非凸問題,對超超臨界機(jī)組采用模糊神經(jīng)網(wǎng)絡(luò)建模使其轉(zhuǎn)為凸二次規(guī)劃。針對負(fù)荷跟蹤和電網(wǎng)頻率擾動問題進(jìn)行了詳細(xì)分析,仿真證明了所提出的分級非線性模型預(yù)測協(xié)調(diào)控制策略的有效性。
[Abstract]:Because the operating point of the industrial process object changes frequently in the whole operation area, it often presents nonlinear characteristics.There are still a lot of physical constraints in the actual industrial process.Therefore, solving constrained nonlinear optimal control problems is a great challenge for industrial processes.Model Predictive Control (MPC) is an advanced optimal control strategy based on model. It adopts rolling time domain and directly deals with process constraints.The traditional model predictive control strategy is only effective for linear systems.In general, nonlinear model predictive control uses sequential quadratic programming, penalty function method and KKT condition to solve on-line optimization problems.For most complex systems, the obtained nonlinear optimization problems are often non-convex and the online-computing burden is very large.In this paper, the nonlinear model predictive control optimization method and its application in complex systems are studied. The main work is summarized as follows: an affine nonlinear state space model is considered.A nonlinear model predictive control strategy based on input and output feedback linearization is proposed.An iterative quadratic programming method, which can guarantee the convergence of the algorithm, is constructed to deal with constraints.This algorithm can obtain feasible solution in the whole prediction time domain.The effectiveness of the method is proved by numerical examples of continuous systems and examples of continuous stirred reactors.The proposed predictive control strategy based on input and output feedback linearization is extended to wind power system doubly-fed motor control and permanent magnet synchronous motor control.Both of these fast processes have strong nonlinearity and many uncertainties.The dynamic performance of the doubly-fed machine is related to the wind speed, and its torque is a nonlinear function of stator and rotor current.The permanent magnet motor has nonlinear coupling and undetectable disturbance, and the load will change frequently when the parameters change.Because the total relative order in the model is less than the number of state variables, the doubly-fed machine can only linearize the input and output feedback.The total relative order in the permanent magnet motor model is equal to the number of state variables, and the permanent magnet motor can be linearized precisely by state feedback.Compared with the existing control methods, this strategy can not only guarantee the convergence of the algorithm, but also reduce the burden of on-line computation and ensure the real-time performance of the control system.Ultra-supercritical unit is an advanced generation equipment with high efficiency, high fuel efficiency and low emission.Due to the large scale of the system and the strong nonlinearity in a large range of variable operating conditions, it is difficult for the conventional control to realize the coordinated control of the fast and stable response under the condition of load tracking and frequency disturbance of the power network.In this paper, a multi-layer control structure including economic index optimization layer and tracking performance optimization layer is constructed. Based on the multi-layer control structure, the hierarchical nonlinear model predictive coordination control for ultra-supercritical units is constructed.In this structure, the upper layer optimizes the objective function including economic index and environment index based on the nonlinear model of ultra-supercritical unit to obtain the given operating value of the lower unit.The goal of the upper layer is to reduce the operating cost of the unit and restrain the disturbance caused by the unit itself and the power grid.The lower layer realizes the accurate tracking of the optimal given value generated by the upper layer of the ultra supercritical unit.Because the optimization problem of hierarchical nonlinear model predictive coordination control is a non-convex problem, fuzzy neural network is used to model the ultra-supercritical unit to transform it into convex quadratic programming.The load tracking and frequency disturbance problems are analyzed in detail. The simulation results show the effectiveness of the proposed hierarchical nonlinear model predictive coordinated control strategy.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TM614;O221.2

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 史宏宇;馮勇;;感應(yīng)電機(jī)高階終端滑模磁鏈觀測器的研究[J];自動化學(xué)報;2012年02期

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本文編號：1747086