Electro-hydraulic control systems are nonlinear in nature and their mathematic models have unknown parameters. Existing research of modeling and identification of the electro-hydraulic control system is mainly based o...Electro-hydraulic control systems are nonlinear in nature and their mathematic models have unknown parameters. Existing research of modeling and identification of the electro-hydraulic control system is mainly based on theoretical state space model, and the parameters identification is hard due to its demand on internal states measurement. Moreover, there are also some hard-to-model nonlinearities in theoretical model, which needs to be overcome. Modeling and identification of the electro-hydraulic control system of an excavator arm based on block-oriented nonlinear（BONL） models is investigated. The nonlinear state space model of the system is built first, and field tests are carried out to reveal the nonlinear characteristics of the system. Based on the physic insight into the system, three BONL models are adopted to describe the highly nonlinear system. The Hammerstein model is composed of a two-segment polynomial nonlinearity followed by a linear dynamic subsystem. The Hammerstein-Wiener（H-W） model is represented by the Hammerstein model in cascade with another single polynomial nonlinearity. A novel Pseudo-Hammerstein-Wiener（P-H-W） model is developed by replacing the single polynomial of the H-W model by a non-smooth backlash function. The key term separation principle is applied to simplify the BONL models into linear-in-parameters struc^tres. Then, a modified recursive least square algorithm（MRLSA） with iterative estimation of internal variables is developed to identify the all the parameters simultaneously. The identification results demonstrate that the BONL models with two-segment polynomial nonlinearities are able to capture the system behavior, and the P-H-W model has the best prediction accuracy. Comparison experiments show that the velocity prediction error of the P-H-W model is reduced by 14%, 30% and 75% to the H-W model, Hammerstein model, and extended auto-regressive （ARX） model, respectively. This research is helpful in controller design, system monitoring and diagnosis.展开更多
传统的振动控制技术将初始辨识的系统频率响应函数贯穿使用于振动控制的过程中;针对液压振动台系统的时变特性,提出使用基于最小均方误差(least mean square,简称LMS)的自适应算法在线辨识系统的频响函数。平滑周期图功率谱估计法相对...传统的振动控制技术将初始辨识的系统频率响应函数贯穿使用于振动控制的过程中;针对液压振动台系统的时变特性,提出使用基于最小均方误差(least mean square,简称LMS)的自适应算法在线辨识系统的频响函数。平滑周期图功率谱估计法相对现代谱估计法分辨率较低,提出自回归(auto-regressive,简称AR)模型法对振动系统响应信号进行功率谱估计,利用尤利-沃克(Yule-Walker)方程求解AR模型参数,并给出AR模型阶次确定的方法。利用自行开发的基于DSP和ARM多处理器信号处理系统对功率谱复现进行软硬件仿真。结果表明,此方法对振动台功率谱进行复现,复现精度优于传统功率谱复现算法。展开更多
基金supported by National Natural Science Foundation of China(Grant No.51175511)
文摘Electro-hydraulic control systems are nonlinear in nature and their mathematic models have unknown parameters. Existing research of modeling and identification of the electro-hydraulic control system is mainly based on theoretical state space model, and the parameters identification is hard due to its demand on internal states measurement. Moreover, there are also some hard-to-model nonlinearities in theoretical model, which needs to be overcome. Modeling and identification of the electro-hydraulic control system of an excavator arm based on block-oriented nonlinear（BONL） models is investigated. The nonlinear state space model of the system is built first, and field tests are carried out to reveal the nonlinear characteristics of the system. Based on the physic insight into the system, three BONL models are adopted to describe the highly nonlinear system. The Hammerstein model is composed of a two-segment polynomial nonlinearity followed by a linear dynamic subsystem. The Hammerstein-Wiener（H-W） model is represented by the Hammerstein model in cascade with another single polynomial nonlinearity. A novel Pseudo-Hammerstein-Wiener（P-H-W） model is developed by replacing the single polynomial of the H-W model by a non-smooth backlash function. The key term separation principle is applied to simplify the BONL models into linear-in-parameters struc^tres. Then, a modified recursive least square algorithm（MRLSA） with iterative estimation of internal variables is developed to identify the all the parameters simultaneously. The identification results demonstrate that the BONL models with two-segment polynomial nonlinearities are able to capture the system behavior, and the P-H-W model has the best prediction accuracy. Comparison experiments show that the velocity prediction error of the P-H-W model is reduced by 14%, 30% and 75% to the H-W model, Hammerstein model, and extended auto-regressive （ARX） model, respectively. This research is helpful in controller design, system monitoring and diagnosis.
文摘传统的振动控制技术将初始辨识的系统频率响应函数贯穿使用于振动控制的过程中;针对液压振动台系统的时变特性,提出使用基于最小均方误差(least mean square,简称LMS)的自适应算法在线辨识系统的频响函数。平滑周期图功率谱估计法相对现代谱估计法分辨率较低,提出自回归(auto-regressive,简称AR)模型法对振动系统响应信号进行功率谱估计,利用尤利-沃克(Yule-Walker)方程求解AR模型参数,并给出AR模型阶次确定的方法。利用自行开发的基于DSP和ARM多处理器信号处理系统对功率谱复现进行软硬件仿真。结果表明,此方法对振动台功率谱进行复现,复现精度优于传统功率谱复现算法。
