为了实现超冗余机械臂动力学模型的精确辨识,提出了一种基于迭代优化和神经网络补偿的半参数动力学模型辨识方法。首先,介绍了超冗余机械臂的动力学模型和最小参数集,建立了关节非线性摩擦模型,使用遗传算法优化回归矩阵条件数生成激励...为了实现超冗余机械臂动力学模型的精确辨识,提出了一种基于迭代优化和神经网络补偿的半参数动力学模型辨识方法。首先,介绍了超冗余机械臂的动力学模型和最小参数集,建立了关节非线性摩擦模型,使用遗传算法优化回归矩阵条件数生成激励轨迹。然后建立了机械臂动力学模型物理可行性约束,基于迭代优化方法设计了两层循环网络对超冗余机械臂的惯性参数和关节摩擦模型进行辨识。最后,利用数据集训练BP神经网络,得到超冗余机械臂半参数动力学模型,并与多种算法进行了比较分析。实验结果表明:相较于传统的最小二乘算法和加权最小二乘算法,通过使用本文提出的辨识算法,关节辨识力矩残差均方根(Root Mean Square,RMS)之和分别提高了32.81%和23.76%,半参数动力学模型相比于全参数动力学模型力矩残差均方根之和提高了23.56%,辨识结果验证了辨识方法的有效性和优越性。展开更多
An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designe...An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter identification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.展开更多
In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied...In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.展开更多
Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution tech...Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the convergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model parameters for a complex mathematical model.展开更多
By taking the surface chemical reactions as the rate-controlling step, a possible reaction mechanism for ethylene epoxidation to synthesize ethylene oxide over the A-type silver catalyst was developed, while it was as...By taking the surface chemical reactions as the rate-controlling step, a possible reaction mechanism for ethylene epoxidation to synthesize ethylene oxide over the A-type silver catalyst was developed, while it was assumed that the epoxidation reaction would take place between ethylene and the un-dissociated adsorbed oxygen O2 a on the solo active sites, while the deep oxidation would occur between ethylene and the dissociated adsorbed oxygen Oa on the adjacent multi-active sites. In order to describe the effect of 1,2-C2H4Cl2(EDC) inhibitor on the ethylene epoxidation process, the reversible reactions between EDC and vinyl chloride(VC) on the active sites of silver catalyst was introduced. According to the assumed mechanism, the hyperbolic macro-kinetic model of ethylene epoxidation over the A-type silver catalyst was established, and the macrokinetic experiments were carried out in an internal-recycle gradientless reactor operating at a pressure of 2.1 MPa and a temperature in the range of 217.8—249.0 ℃, with the gas composition(molar fraction) consisting of 15.82%—34.65% C2H4, 2.55%—7.80% O2, 0.88%—6.15% CO2, 0.15—2.61 μmol/mol of 1,2-C2H4Cl2 and 0.14—1.28 μmol/mol of C2H3 Cl. By means of the Simplex Optimal Method, the parameters of the macrokinetic models were estimated. Statistical test showed that the macrokinetic models developed for the A-type silver catalyst agree well with the experimental results.展开更多
文摘为了实现超冗余机械臂动力学模型的精确辨识,提出了一种基于迭代优化和神经网络补偿的半参数动力学模型辨识方法。首先,介绍了超冗余机械臂的动力学模型和最小参数集,建立了关节非线性摩擦模型,使用遗传算法优化回归矩阵条件数生成激励轨迹。然后建立了机械臂动力学模型物理可行性约束,基于迭代优化方法设计了两层循环网络对超冗余机械臂的惯性参数和关节摩擦模型进行辨识。最后,利用数据集训练BP神经网络,得到超冗余机械臂半参数动力学模型,并与多种算法进行了比较分析。实验结果表明:相较于传统的最小二乘算法和加权最小二乘算法,通过使用本文提出的辨识算法,关节辨识力矩残差均方根(Root Mean Square,RMS)之和分别提高了32.81%和23.76%,半参数动力学模型相比于全参数动力学模型力矩残差均方根之和提高了23.56%,辨识结果验证了辨识方法的有效性和优越性。
基金Supported by the National Natural Science Foundation of China （20676013）
文摘An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter identification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.
基金Projects(61203020,61403190)supported by the National Natural Science Foundation of ChinaProject(BK20141461)supported by the Jiangsu Province Natural Science Foundation,China
文摘In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.
基金Supported by the National Natural Science Foundation of China （60804027, 61064003） and Fuzhou University Research Foundation （FZU-02335, 600338 and 600567）.
文摘Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the convergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model parameters for a complex mathematical model.
文摘By taking the surface chemical reactions as the rate-controlling step, a possible reaction mechanism for ethylene epoxidation to synthesize ethylene oxide over the A-type silver catalyst was developed, while it was assumed that the epoxidation reaction would take place between ethylene and the un-dissociated adsorbed oxygen O2 a on the solo active sites, while the deep oxidation would occur between ethylene and the dissociated adsorbed oxygen Oa on the adjacent multi-active sites. In order to describe the effect of 1,2-C2H4Cl2(EDC) inhibitor on the ethylene epoxidation process, the reversible reactions between EDC and vinyl chloride(VC) on the active sites of silver catalyst was introduced. According to the assumed mechanism, the hyperbolic macro-kinetic model of ethylene epoxidation over the A-type silver catalyst was established, and the macrokinetic experiments were carried out in an internal-recycle gradientless reactor operating at a pressure of 2.1 MPa and a temperature in the range of 217.8—249.0 ℃, with the gas composition(molar fraction) consisting of 15.82%—34.65% C2H4, 2.55%—7.80% O2, 0.88%—6.15% CO2, 0.15—2.61 μmol/mol of 1,2-C2H4Cl2 and 0.14—1.28 μmol/mol of C2H3 Cl. By means of the Simplex Optimal Method, the parameters of the macrokinetic models were estimated. Statistical test showed that the macrokinetic models developed for the A-type silver catalyst agree well with the experimental results.
