基于迭代ADP算法的污水处理过程最优控制
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摘要
针对污水处理过程(wastewater treatment process,WWTP)中溶解氧质量浓度和硝态氮质量浓度的最优控制问题,提出了一种基于迭代自适应动态规划(adaptive dynamic programming,ADP)算法的最优控制策略.该策略无须知道污水处理过程的非线性动力学模型,只需污水处理系统的输入输出观测信息,设计基于ADP强化学习原理的控制体系结构,并利用神经网络辨识特性,通过在线迭代来逼近性能评价指标和最优控制策略.实验结果表明:该控制器相对于传统的PID控制策略,提高了污水处理过程的控制精度,系统鲁棒性也明显增强,控制性能更优.
In order to improve the control performance of the dissolved oxygen(DO) concentration and nitrate nitrogen(S_(NO)) concentration in a wastewater treatment plant(WWTP),an optimal control strategy based on iterative adaptive dynamic programming(ADP) algorithm was proposed.It only need the input and output of the non-linear dynamics model of the control system,and approximate the performance evaluation index and optimal control strategy by networks identifying the control system.The learning algorithm of controler was implemented on line in the benchmark simulation model(BSM1).Finally,the experiments validate the effectiveness of the proposed control strategy in improving the control precision and system robustness compared to the traditional PID control strategy.
In order to improve the control performance of the dissolved oxygen(DO) concentration and nitrate nitrogen(S_(NO)) concentration in a wastewater treatment plant(WWTP),an optimal control strategy based on iterative adaptive dynamic programming(ADP) algorithm was proposed.It only need the input and output of the non-linear dynamics model of the control system,and approximate the performance evaluation index and optimal control strategy by networks identifying the control system.The learning algorithm of controler was implemented on line in the benchmark simulation model(BSM1).Finally,the experiments validate the effectiveness of the proposed control strategy in improving the control precision and system robustness compared to the traditional PID control strategy.
引文
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[2]ALEX J,BENEDETTI L,COPP J,et al.Benchmark simulation model no.1(BSM1)[D].Lund:Lund University,2008.
[3]BRDYS M A,GROCHOWSKI M,GMINSKI T,et al.Hierarchical predictive control of integrated wastewater treatment systems[J].Control Engineering Practice,2008,16(6):751-767.
[4]CARLSSON B,LINDBERG C F,HASSELBLAD S,et al.On-line estimation of the respiration rate and the oxygen transfer rate at Kungsangen wastewater treatment plant in Uppsala[J].Acta Mathematica Sinica,1994,27(11):2169-2176.
[5]CARLOS-HERNANDEZ S.A new adaptive PI for bicarbonate regulation in an anaerobic digestion process[J].Asian Journal of Control,2015,13(6):785-796.
[6]HA I N,GILBERT E.Robust tracking in nonlinear systems[J].IEEE Transactions on Automatic Control,1987,32(9):763-771.
[7]SHEN W,CHEN X,PONS M N,et al.Model predictive control for wastewater treatment process with feedforward compensation[J].Chemical Engineering Journal,2009,155(1):161-174.
[8]WERBOS P J.Advanced forecasting methods for global crisis warning and models of intelligence[J].General Systems Yearbook,1977,22(12):25-38.
[9]ZHANG X,ZHANG H,SUN Q,et al.Adaptive dynamic programming-based optimal control of unknown nonaffine nonlinear discrete-time systems with proof of convergence[J].Neurocomputing,2012,91:48-55.
[10]SARANGAPANI J.Neural network control of nonlinear discrete-time systems[M].Roca Roton:CRC press,2006.
[11]WERBOS P J.Foreword-ADP:the key direction for future research in intelligent control and understanding brain intelligence[J].IEEE Transactions on Systems,Man,and Cybernetics Part B,2008,38(4):898-900.
[12]WANG F Y,JIN N,LIU D,et al.Adaptive dynamic programming for finite-horizon optimal control of discretetime nonlinear systems with-error bound[J].IEEE Transactions on Neural Networks,2011,22(1):24-36.
[13]WANG D,LIU D,WEI Q.Finite-horizon neuro-optimal tracking control for a class of discrete-time nonlinear systems using adaptive dynamic programming approach[J].Neurocomputing,2012,78(1):14-22.
[14]MURRAY J J,COX C J,LENDARIS G G,et al.Adaptive dynamic programming[J].IEEE Trans Syst Man Cybernetics Part C Application&Reviews,2002,32(2):140-153.
[15]AL-TAMIMI A,LEWIS F L,ABU-KHALAF M.Discrete-time nonlinear HJB solution using approximate dynamic programming:convergence proof[J].IEEE Transactions on Systems,Man,and Cybernetics:Part B(Cybernetics),2008,38(4):943-949.
[16]QIAO J F,BO Y C,CHAI W,et al.Adaptive optimal control for a wastewater treatment plant based on a datadriven method[J].Water Science and Technology,2013,67(10):2314-2320.
[17]王澄,刘德荣,魏庆来,等.带有储能设备的智能电网电能迭代自适应动态规划最优控制[J].自动化学报,2014,40(9):1984-1990.WANG C,LIU D R,WEI Q L,et al.Iterative adaptive dynamic programming approach to power optimal control for smart grid with energy storage devices[J].Acta Automatica Sinica,2013,40(9):1984-1990.(in Chinese)