化工过程中的若干预测控制算法与应用研究
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摘要
随着全球化市场的竞争日趋激烈、不可再生资源的日益减少和环境压力的不断加大,如今的生产企业对过程控制的性能和效益提出了更高的要求。预测控制技术以其对模型质量要求不高、控制性能好、易于处理约束和经济效益可观等特点,越来越受到工业控制界的关注。本文在前人基础上,从实际出发,对预测控制的若干问题进行了较为深入的研究,包括:
(1)针对DMC算法对扰动估计能力弱的缺点,提出了一种扰动自适应DMC算法。采用时间序列(ARMA)模型描述不可测扰动的动态特性。考虑到扰动往往具有时变性,采用递推算法在线辨识ARMA模型,并对扰动的未来行为作出预估,提高了系统的模型预测精度,改善了DMC抑制扰动的能力。
(2)提出一种基于多次迭代思想的递推伪线性回归(MIPLR)算法。递推辨识ARMA(X)模型,算法的准则函数不是参数向量的二次函数形式,所以相应的解析解不存在,传统的在线递推算法辨识精度不理想。MIPLR采用多次迭代思想,在每一次递推过程中通过多次迭代使结果更加逼近准则函数极小点。与原伪线性回归算法相比,在辨识ARMA(X)模型上MIPLR具有更好的辨识效果。
(3)将扰动自适应的思想扩展到状态空间框架下的预测控制中,克服了基于输入-输出模型的DMC算法的不足。同时,考虑到数据与辨识模型的不确定性,改用Min-Max形式描述MPC算法的控制作用优化命题,并将在线辨识过程中的误差数据引入Min-Max命题,使在线辨识与控制作用鲁棒优化求解紧密结合起来,提高了算法的鲁棒性。进一步地,将此Min-Max问题转换为一个等效的非线性Min问题,并采用多步线性化方法实现快速求解,解决了传统Min-Max方法在线计算负荷高的问题。
(4)将经典控制理论中的反馈机制和闭环控制系统的概念引入粒子群算法,提出了一种闭环粒子群(CLPSO)算法。在CLPSO中,将每个粒子视作一个被控对象,对其构建一个闭环控制系统。迭代过程中将粒子的适应值作为被控变量,反馈给闭环回路,通过PID控制器调整更新惯性权重,然后再进行粒子的速度和位置的更新。CLPSO很好的满足了每个粒子的自身需求,极大的保证了种群中粒子的多样性,提高了PSO的搜索能力。
(5)考虑到模型不确定性,用Min-Max优化命题描述工业多变量PID控制器和预测控制器参数整定问题,并给出了面向工程应用的性能指标。利用CLPSO求解该命题。仿真结果显示,所提方法具有良好的控制效果和鲁棒性。并且,运用该方法对化工厂PTA装置控制回路进行优化整定,得到了满意的控制效果。现场应用进一步证明了该方法的有效性。
最后对全文进行了总结,并指出若干有待于今后进一步研究的内容。
With the increasing competiton in global market, decrease of non-renewable resources and high pressure in environment, process control is required to be more efficient and profitable. Model preditive control technique, due to its advantages of control performance, robustness and constraints handling, has become an important issue in the control engineering fields. Some problems of predictive control are researched in this dissertation, and the main research works are as follows:
(1) A modified DMC algorithm that uses an adaptive disturbances model is proposed. The dynamics of unmeasured distubances are estimated by an ARMA process. An on-line recursive method is adopted to estimate the coefficients of ARMA process considering the time-varing feature of disturbances. With the adaptive disturbance model, the accuracy of model prediction in DMC is improved, and consequently, a better performance in disturbance rejection is abtained.
(2) A novel multi-iteration pseudo-linear regression (MIPLR) method is proposed. In estimating ARMA(X) processes, the cost function of recursive identification algorithms is not a quadratic form of the model coefficients vector, thus no analytical solution is available and the performance of traditional methods is not satisfactory. This can be improved by introducing the concept of multi-iteration into recursive methods. MIPLR uses each data sample in multiple iterations, improving the model accuracy and convergence rate.
(3) A robust model predictive control (RAMPC) technique with an adaptive disturbance model is developed. The dynamics of unmeasured disturbances are modeled by ARMA processes. In addition, the optimization in MPC is formulated as a Min-Max problem which takes into account data uncertainties. For lower computational burden, the Min-Max problem is reduced to a nonlinear Min one, and is solved by multi-step linearization method. Numerical simulations demonstrate the effectiveness of the proposed methods.
(4) A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional-integral-derivative (PID) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity.
(5) Tuning of general multivariable process controllers is formulated as a Min-Max optimization problem, considering model uncertainty. A novel engineering oriented performance index is proposed and CLPSO is employed to solve the problem. Simulation results demonstrate the superiority of CLPSO over other methods, and the applications to PTA equipment control loops prove the effectiveness of the proposed method.
At the end of this dissertation, the author also gives some suggestions to the further research in these fields.
(1)针对DMC算法对扰动估计能力弱的缺点,提出了一种扰动自适应DMC算法。采用时间序列(ARMA)模型描述不可测扰动的动态特性。考虑到扰动往往具有时变性,采用递推算法在线辨识ARMA模型,并对扰动的未来行为作出预估,提高了系统的模型预测精度,改善了DMC抑制扰动的能力。
(2)提出一种基于多次迭代思想的递推伪线性回归(MIPLR)算法。递推辨识ARMA(X)模型,算法的准则函数不是参数向量的二次函数形式,所以相应的解析解不存在,传统的在线递推算法辨识精度不理想。MIPLR采用多次迭代思想,在每一次递推过程中通过多次迭代使结果更加逼近准则函数极小点。与原伪线性回归算法相比,在辨识ARMA(X)模型上MIPLR具有更好的辨识效果。
(3)将扰动自适应的思想扩展到状态空间框架下的预测控制中,克服了基于输入-输出模型的DMC算法的不足。同时,考虑到数据与辨识模型的不确定性,改用Min-Max形式描述MPC算法的控制作用优化命题,并将在线辨识过程中的误差数据引入Min-Max命题,使在线辨识与控制作用鲁棒优化求解紧密结合起来,提高了算法的鲁棒性。进一步地,将此Min-Max问题转换为一个等效的非线性Min问题,并采用多步线性化方法实现快速求解,解决了传统Min-Max方法在线计算负荷高的问题。
(4)将经典控制理论中的反馈机制和闭环控制系统的概念引入粒子群算法,提出了一种闭环粒子群(CLPSO)算法。在CLPSO中,将每个粒子视作一个被控对象,对其构建一个闭环控制系统。迭代过程中将粒子的适应值作为被控变量,反馈给闭环回路,通过PID控制器调整更新惯性权重,然后再进行粒子的速度和位置的更新。CLPSO很好的满足了每个粒子的自身需求,极大的保证了种群中粒子的多样性,提高了PSO的搜索能力。
(5)考虑到模型不确定性,用Min-Max优化命题描述工业多变量PID控制器和预测控制器参数整定问题,并给出了面向工程应用的性能指标。利用CLPSO求解该命题。仿真结果显示,所提方法具有良好的控制效果和鲁棒性。并且,运用该方法对化工厂PTA装置控制回路进行优化整定,得到了满意的控制效果。现场应用进一步证明了该方法的有效性。
最后对全文进行了总结,并指出若干有待于今后进一步研究的内容。
With the increasing competiton in global market, decrease of non-renewable resources and high pressure in environment, process control is required to be more efficient and profitable. Model preditive control technique, due to its advantages of control performance, robustness and constraints handling, has become an important issue in the control engineering fields. Some problems of predictive control are researched in this dissertation, and the main research works are as follows:
(1) A modified DMC algorithm that uses an adaptive disturbances model is proposed. The dynamics of unmeasured distubances are estimated by an ARMA process. An on-line recursive method is adopted to estimate the coefficients of ARMA process considering the time-varing feature of disturbances. With the adaptive disturbance model, the accuracy of model prediction in DMC is improved, and consequently, a better performance in disturbance rejection is abtained.
(2) A novel multi-iteration pseudo-linear regression (MIPLR) method is proposed. In estimating ARMA(X) processes, the cost function of recursive identification algorithms is not a quadratic form of the model coefficients vector, thus no analytical solution is available and the performance of traditional methods is not satisfactory. This can be improved by introducing the concept of multi-iteration into recursive methods. MIPLR uses each data sample in multiple iterations, improving the model accuracy and convergence rate.
(3) A robust model predictive control (RAMPC) technique with an adaptive disturbance model is developed. The dynamics of unmeasured disturbances are modeled by ARMA processes. In addition, the optimization in MPC is formulated as a Min-Max problem which takes into account data uncertainties. For lower computational burden, the Min-Max problem is reduced to a nonlinear Min one, and is solved by multi-step linearization method. Numerical simulations demonstrate the effectiveness of the proposed methods.
(4) A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional-integral-derivative (PID) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity.
(5) Tuning of general multivariable process controllers is formulated as a Min-Max optimization problem, considering model uncertainty. A novel engineering oriented performance index is proposed and CLPSO is employed to solve the problem. Simulation results demonstrate the superiority of CLPSO over other methods, and the applications to PTA equipment control loops prove the effectiveness of the proposed method.
At the end of this dissertation, the author also gives some suggestions to the further research in these fields.
引文
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