摘要
随着现代工业的快速发展,工业的生产规模越来越大,生产工艺以及生产流程变得越来越复杂,这对传统的机理建模与控制策略提出了重大的挑战,基于数据驱动的方法越来越受到工业界和学术界的重视。基于PLS的数据驱动控制方法由于其自身的优点已经广泛的应用到各个领域中,然而在实际的工业过程中仍存在其他不足,例如PLS非线性动态特性的拟合、PLS控制方法中隐变量约束耦合等问题。本文针对复杂化工过程中存在的强耦合、约束、非线性等问题,结合PLS去噪、降维、消除共线性、自解耦等特点,提出了基于动态PLS建模方法的多回路预测控制策略。在技术路线上,从线性无约束的控制器到有约束的控制器设计,随后进一步将算法推广到非线性系统建模及控制方法的研究中,最后对非线性系统在线建模与控制问题进行研究。主要的研究内容包括：
(1)针对大滞后多变量系统的控制问题,本文第二章结合Kaspar等学者提出的PLS控制框架,提出了隐变量的动态PLS建模方法及PID控制框架。针对铝合金MIG焊接过程中存在的强耦合、难建模等特点,本文利用动态PLS框架的解耦特性,将其用于焊接过程中系统的建模以及控制器的设计中,通过该算法将原多变量系统的控制器设计问题转化成为多个单变量控制回路的控制器设计问题,在各个隐变量控制回路中进行独立PID控制器设计,通过仿真验证该方法的有效性。
(2)过程受到装置、环境等外在条件和内部特性引起的过程变量和控制变量的约束,对基于数据驱动的控制器的设计提出了较高的控制要求。本文第三章,提出了一种将空间投影方法(DyPLS)与迭代方法相结合的约束处理方法；并采用约束模型预测控制策略,对建立的模型进行控制器设计仿真验证。结果表明这种约束迭代算法在约束存在和极点漂移的情况下都能达到理想的控制效果。
(3)非线性特性是困扰工业过程建模与控制的一个难题,同时非线性特性却具有普遍性。对多变量系统的非线性特性的建模就变得更加困难,模糊模型特别是TS模型为解决非线性问题提供了一种有效途径,通过将模糊方法与PLS方法相结合,将多变量的动态非线性建模问题转变为多个单输入单输出的动态非线性建模问题,从而建立描述过程非线性动态特性的动态模糊PLS模型。在此基础上,进一步研究解决约束工况下的控制问题,引入了模糊预测函数控制方法。在pH中和滴定过程中,验证了模糊预测函数控制方法的控制性能。
(4)过程受到工况变化、设备、催化剂等变化而使原始模型与当前对象产生较大偏差,这对基于模型的控制器的性能产生较大影响,甚至引起整个系统的不稳定。所以,本文在第五章中,提出了一种将WPLS方法与递推TS建模方法相结合的非线性递推模糊PLS模型更新方法。这种自适应算法在增益变化的情况下能较好的更新模型参数,实现模型与过程匹配。在此基础上,引入预测函数控制器,并在pH中和滴定中验证了该算法。
With the rapid development of modern industry, the scale of industrial production is increasing. The production process and production flow have become more and more complex, which is a major challenge to the traditional mechanism modeling and control strategy. On this condition, data-driven application and theory attract more and more researchers'attention in industry and academia. Due to its advantages, PLS-based data-driven control methods have been widely applied to various fields. However, there are still some specific problems in the industrial process, for example, the nonlinear dynamic characteristics fitting of the PLS, latent variables constraints coupled in PLS control etc. This paper is mainly focusing on strong coupling, constraint processing, nonlinear problems in complex chemical process. Combined with the characteristics of PLS denoising, dimensionality reduction, eliminating collinearity, self decoupling and so on, this paper proposes a multi-loop predictive control strategy based on dynamic PLS modeling method. Technically, we first design linear unconstrained controller and constrainted controller and then extend the algorithm to nonlinear system modeling and control methods and further research on non-linear system online modeling and control problems.
The main contents include:
(1) Aiming at conventional control strategies in big lag multivariate system, combined with PLS control framework by Kaspar and Ray, Chapter II of this paper proposed a implicit variable dynamic PLS modeling methods and PID control framework. Against the characteristics of strong coupling and difficult modeling of aluminum alloy MIG welding process, the dynamic PLS framework is used in the welding process system modeling and controller design according to its decoupling characteristics. This method transfers the multivariable controller design problem in original space into multi-loop single-variable controller design in latent space and then design PID controller independently in each latent variables control loop. Finally, demonstrate the effectiveness of this method by simulation.
(2) Aiming at the constraint of process variables and control variables result from external conditions of process such as pressure, device, environment, and internal characteristics, a higher control requirement is needed for the data-driven based controller design. Consequently, in the Chapter III of the paper, a space projection method (DyPLS) is proposed to deal with constraint, which is combined with iterations method and spatial transform. Constrained model predictive control method is used for model controller design. The simulation shows that this constrained iterative algorithm can achieve the desired result in the case of constraints and pole shift. On this basis, process control is achieved and method of this article is verified on the test model.
(3) The nonlinear characteristic is one of the biggest problems in the modeling and control of industrial process. Unfortunately, non-linear characteristic is universal, which is more difficult for nonlinear characteristic modeling of the multi-variable system. Fuzzy model especially the TS model provides an effective way to solve the nonlinear problems. Combined with PLS method with fuzzy method, the multivariable dynamic nonlinear modeling is transferred into the multiple SISO nonlinear dynamic modeling problems. And dynamic fuzzy PLS model is established to describe the process dynamic nonlinear characteristics. Combined with the dynamic fuzzy PLS modeling method, based on PLS control framework proposed by Kaspar and Ray and characteristics of TS fuzzy model, constraint conditions control problem is solved. Meanwhile, fuzzy PFC control is introduced to verify the control performance of fuzzy PFC in pH titration process
(4) The process is affected by changes in working conditions, equipment, catalyst, which results in the deviation between original model and current object, having a great impact on the control performance of the model-based controller. What is worse, it leads to instability of the whole system. To cope with this, in Chapter V nonlinear recursive fuzzy PLS model update method, which combines WPLS method with recursive TS modeling, is proposed. The results show that this adaptive algorithm can do better in update the model parameters to achieve the matching between model and process in the case of gain change. On this basis, prediction function controller is introduced to realize the process control. And an experiment of pH titration is simulated to validate this method.
引文
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