| In chemical processes,such as pulp and paper production,the presence of strong coupling,strong interference,and time-delay have always affected the control performance of chemical process control systems,leading to reduced product quality and production efficiency.Intelligent control has proven to be an effective means of improving the control performance of the chemical process control system,it is plagued by low search efficiency and a tendency to get trapped in local optima.However,the behavior of strange nonchaotic dynamics(SND)has the characteristics of randomness and ergodicity,which can improve the search efficiency of intelligent control methods and the ability to escape from local optima.Therefore,theoretical research is conducted on intelligent control methods based on SND supported by the National Natural Science Foundation of China(number:62073206),with the aim of resolving control problems arising from strong coupling,strong interference,and time-delay characteristics in chemical process control and ultimately elevating product quality and production efficiency.The main objectives and contributions can be summarized in four aspects.I.System identification method with particle swarm optimization based on SNDA particle swarm optimization(PSO)parameter identification method based on SND is proposed to identify system parameters in chemical process caused by large time-delay and strong disturbance.Random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions;the linearly decaying weight is replaced by strange nonchaotic sequences to further enhance the optimization capability of the algorithm;time-varying acceleration coefficients and mutation rules with strange nonchaotic characteristics are used to further enhance the global search ability of the algorithm;the improved algorithm is applied to parameter identification of time-delay systems.Simulation results show that the strange nonchaotic particle swarm optimization algorithm is more suitable for parameter identification of time-delay systems,and the identification rate of this algorithm for low order models is up to 90%,which is higher than the classical particle swarm optimization algorithm,the time-varying acceleration coefficient particle swarm optimization algorithm and the improved particle swarm optimization with time-varying acceleration and mutation rules;For two types of higher-order models,the average convergence performance of the strange nonchaotic particle swarm optimization algorithm is improved by 6%and 24%compared with the other algorithms.II.Decoupling method with extreme learning machine based on SNDA decoupling method based on SND for extreme learning machine(ELM)is proposed to address the strong coupling problem of multivariable systems.Strange nonchaotic sequences are used to optimize the weights and thresholds randomly generated between the input layer and the hidden layer of the ELM,which overcomes the shortcomings of extreme learning machine optimization;the improved extreme learning machine is applied to the decoupling of multivariable systems.Simulation results show that the strange nonchaotic optimization extreme learning machine decoupling method is an effective method to solve the strong coupling problem of multivariable systems,and the decoupling accuracy of this method can reach 3.15*10-8,while the decoupling accuracy of the classical extreme learning machine decoupling method,whale optimization extreme learning machine decoupling method and particle swarm optimization extreme learning machine decoupling method is only 3.32*10-5,5.47*10-8,3.26*10-8.In addition,it was verified that the decoupling effect of the decentralized decoupler is better than that of the centralized decoupler,and the decoupling accuracy of the strange nonchaotic optimization extreme learning machine decoupling method has been improved from 3.15*10-8 to 1.581*10-13.III.Parameter tuning method of internal model control based on SNDA parameter tuning method for internal model controllers based on SND is proposed to address the problem of difficult parameter tuning for process controllers with large time-delay.The specific approach is as follows:strange nonchaotic sequences with randomness and ergodicity are used for global search to determine the current optimal target parameters in the search space;an appropriate scaling step size is selected and strange nonchaotic sequences are used to continue traversing around the current optimal target parameters to improve the ability of algorithm to escape local optima;the method is applied to the controller parameter tuning of two-degree-of-freedom internal model control(TDF-IMC).Simulation results show that during the parameter tuning process of the internal model controller,the convergence speed of the strange nonchaotic optimization algorithm is 3.5 times that of the chaotic optimization algorithm.In addition,by analyzing the distribution of strange nonchaotic solutions in the target space,an improved strange nonchaotic optimization algorithm is proposed for tuning the controller parameters of internal model control.Simulation results show that when tuning the internal model controllers of first-order and second-order systems using this method,the proportion of the statistical objective function values in the limited interval is as high as 75%.However,when using the strange nonchaotic optimization algorithm to tune the internal model controllers of two types of systems,the proportion of the statistical objective function values in the limited interval is only 5%and 35%.IV.Design of Cross-directional basis weight intelligent control scheme based on SNDBased on the application background of paper cross-directional basis weight control,a cross-directional basis weight intelligent control scheme based on SND is proposed,which lays the foundation for the application of the above research results in cross-directional basis weight control.The scheme uses Step7 and WinCC as the software development platform,and is designed using Siemens S7400 and industrial Ethernet.The cross-directional control system adopts a dualring network dual-redundant server architecture,which can improve the stability and reliability of the system.Intelligent control algorithms based on strange nonchaotic dynamics are implemented to use the powerful matrix calculation capabilities of Matlab;OPC communication technology is used to achieve data communication between Matlab and the upper-level network;Siemens controller S7-400 as the hardware main body of the system is used to exchange information between the lower-level and upper-level networks and implement crossdirectional basis weight control.In conclusion,aiming at the intelligent control method based on SND and its application design in paper CD basis weight control,theoretical research work is mainly carried out in three aspects:particle swarm optimization system identification method based on SND,extreme learning machine decoupling method based on SND,and controller parameter tuning method of internal model control based on SND,and a paper CD basis weight control scheme based on the above research results was proposed.The research results indicate that the proposed methods can improve the performance of intelligent control methods,enhance the control effectiveness of light chemical process control systems,and lay the foundation for the application of intelligent control methods in paper CD basis weight control. |
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