本文選題：區(qū)間特性 + 模型預(yù)測控制��； 參考：《燕山大學(xué)》2015年碩士論文

【摘要】：水泥回轉(zhuǎn)窯是水泥生產(chǎn)的核心設(shè)備,我們通過深入研究其機(jī)理,選取其有關(guān)變量。本課題正是以水泥回轉(zhuǎn)窯系統(tǒng)為研究對象,基于模型預(yù)測控制(Model Predictive Control,MPC)理論,通過結(jié)合線性區(qū)間MPC和非線性MPC,設(shè)計(jì)集成了MPC高級(jí)算法設(shè)計(jì)控制器。該控制器對實(shí)現(xiàn)復(fù)雜多變量系統(tǒng)的良好控制具有重要的理論和實(shí)際意義。具體研究工作如下:首先,從預(yù)熱分解、窯內(nèi)煅燒、窯尾廢氣處理和熟料冷卻四個(gè)方面研究了水泥回轉(zhuǎn)窯控制的煅燒工藝,并通過操作員經(jīng)驗(yàn)和理論研究選取了操作變量以及被控變量。其次,針對線性MPC算法,建立了一種基于區(qū)間特性和變量軟約束的MPC算法。利用預(yù)測輸出與區(qū)間的位置關(guān)系,模仿設(shè)定值偏差項(xiàng)的設(shè)計(jì)原理,構(gòu)造了區(qū)間跟蹤偏差項(xiàng);另外變量軟約束項(xiàng)是由經(jīng)典MPC算法的MV軟約束和CV的軟約束項(xiàng)構(gòu)成,這二者共同構(gòu)成了新的變量軟約束項(xiàng)。然后,為保證控制器在約束條件下始終有可行域,同時(shí)使CV盡可能的在輸出軟約束空間內(nèi),在每一步滾動(dòng)優(yōu)化求解之前,根據(jù)輸入空間和輸出軟約束空間的交集情況進(jìn)行區(qū)間約束的可行性判定,當(dāng)交集解域?yàn)榭諘r(shí),引入松弛變量,并給出松弛變量的求解方案,使優(yōu)化重新可行。最后,研究了一種非線性過程控制策略方法。當(dāng)非線性較弱時(shí),采用區(qū)間MPC算法,以線性代替非線性,簡化了算法。當(dāng)被控對象處于非線性較強(qiáng)的情況下,控制器切換為基于LSSVM模型的非線性MPC算法,通過多步迭代的方法代替了多步預(yù)測,省略了建立非線性預(yù)測模型的這一過程,只需要訓(xùn)練出被控對象的單步預(yù)測模型即可。
[Abstract]:Cement rotary kiln is the core equipment of cement production. Taking cement rotary kiln system as the research object, based on the model predictive control (MPC) theory, a MPC advanced algorithm design controller is designed and integrated by combining linear interval MPC and nonlinear MPCs. The controller has important theoretical and practical significance for the good control of complex multivariable systems. The specific research work is as follows: firstly, the calcination process controlled by cement rotary kiln is studied from four aspects: preheating decomposition, kiln calcinations, waste gas treatment and clinker cooling. Through operator experience and theoretical research, the operating variables and the controlled variables are selected. Secondly, for linear MPC algorithm, a MPC algorithm based on interval characteristic and variable soft constraint is established. Based on the position relation between the predicted output and the interval, the interval tracking deviation term is constructed by imitating the design principle of the set value deviation term, and the variable soft constraint term is composed of the MV soft constraint of the classical MPC algorithm and the soft constraint term of the CV. Both of them constitute a new variable soft constraint. Then, in order to ensure that the controller always has a feasible region under the constraint conditions, and make CV as far as possible in the output soft constraint space, before each step of rolling optimization solution, According to the intersection of the input space and the output soft constraint space, the feasibility of interval constraint is determined. When the solution field of the intersection is space, the relaxation variable is introduced, and the solution scheme of the relaxation variable is given to make the optimization feasible again. Finally, a nonlinear process control strategy is studied. When the nonlinearity is weak, the interval MPC algorithm is used to simplify the algorithm. When the controlled object is in strong nonlinearity, the controller is switched to a nonlinear MPC algorithm based on LSSVM model, and the multistep prediction is replaced by the multi-step iterative method, which omits the process of establishing the nonlinear prediction model. Only a single step prediction model of the controlled object needs to be trained.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TQ172.622;TP273

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本文編號(hào)：1935349