High-accuracy motion trajectory tracking control of a pneumatic cylinder driven by a proportional directional control valve was considered. A mathematical model of the system was developed firstly. Due to the time-var...High-accuracy motion trajectory tracking control of a pneumatic cylinder driven by a proportional directional control valve was considered. A mathematical model of the system was developed firstly. Due to the time-varying friction force in the cylinder, unmodeled dynamics, and unknown disturbances, there exist large extent of parametric uncertainties and rather severe uncertain nonlinearities in the pneumatic system. To deal with these uncertainties effectively, an adaptive robust controller was constructed in this work. The proposed controller employs on-line recursive least squares estimation(RLSE) to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodeled dynamics and disturbances. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology was applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping was used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Extensive experimental results were presented to illustrate the excellent achievable performance of the proposed controller and performance robustness to the load variation and sudden disturbance.展开更多
Aims The limitations of classical Lotka–Volterra models for analyzing and interpreting competitive interactions among plant species have become increasingly clear in recent years.Three of the problems that have been ...Aims The limitations of classical Lotka–Volterra models for analyzing and interpreting competitive interactions among plant species have become increasingly clear in recent years.Three of the problems that have been identified are(i)the absence of frequency-dependence,which is important for long-term coexistence of species,(ii)the need to take unmeasured(often unmeasurable)variables influencing individual performance into account(e.g.spatial variation in soil nutrients or pathogens)and(iii)the need to separate measurement error from biological variation.Methods We modified the classical Lotka–Volterra competition models to address these limitations.We fitted eight alternative models to pin-point cover data on Festuca ovina and Agrostis capillaris over 3 years in an herbaceous plant community in Denmark.A Bayesian modeling framework was used to ascertain whether the model amendments improve the performance of the models and increase their ability to predict community dynamics and to test hypotheses.Important Findings Inclusion of frequency-dependence and measurement error,but not unmeasured variables,improved model performance greatly.Our results emphasize the importance of comparing alternative models in quantitative studies of plant community dynamics.Only by considering possible alternative models can we identify the forces driving community assembly and change,and improve our ability to predict the behavior of plant communities.展开更多
基金Projects(50775200,50905156)supported by the National Natural Science Foundation of China
文摘High-accuracy motion trajectory tracking control of a pneumatic cylinder driven by a proportional directional control valve was considered. A mathematical model of the system was developed firstly. Due to the time-varying friction force in the cylinder, unmodeled dynamics, and unknown disturbances, there exist large extent of parametric uncertainties and rather severe uncertain nonlinearities in the pneumatic system. To deal with these uncertainties effectively, an adaptive robust controller was constructed in this work. The proposed controller employs on-line recursive least squares estimation(RLSE) to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodeled dynamics and disturbances. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology was applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping was used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Extensive experimental results were presented to illustrate the excellent achievable performance of the proposed controller and performance robustness to the load variation and sudden disturbance.
文摘Aims The limitations of classical Lotka–Volterra models for analyzing and interpreting competitive interactions among plant species have become increasingly clear in recent years.Three of the problems that have been identified are(i)the absence of frequency-dependence,which is important for long-term coexistence of species,(ii)the need to take unmeasured(often unmeasurable)variables influencing individual performance into account(e.g.spatial variation in soil nutrients or pathogens)and(iii)the need to separate measurement error from biological variation.Methods We modified the classical Lotka–Volterra competition models to address these limitations.We fitted eight alternative models to pin-point cover data on Festuca ovina and Agrostis capillaris over 3 years in an herbaceous plant community in Denmark.A Bayesian modeling framework was used to ascertain whether the model amendments improve the performance of the models and increase their ability to predict community dynamics and to test hypotheses.Important Findings Inclusion of frequency-dependence and measurement error,but not unmeasured variables,improved model performance greatly.Our results emphasize the importance of comparing alternative models in quantitative studies of plant community dynamics.Only by considering possible alternative models can we identify the forces driving community assembly and change,and improve our ability to predict the behavior of plant communities.
