以某起重船为控制对象,以SRI-VC2110DP动力定位控制系统为基础,设计一种基于数字信号处理器(Digital Signal Processor,DSP)的动力定位控制器。针对动力定位控制器的实际需求,围绕核心处理器进行双CAN总线接口、双以太网接口和I/O接口...以某起重船为控制对象,以SRI-VC2110DP动力定位控制系统为基础,设计一种基于数字信号处理器(Digital Signal Processor,DSP)的动力定位控制器。针对动力定位控制器的实际需求,围绕核心处理器进行双CAN总线接口、双以太网接口和I/O接口等硬件电路的开发,同时完成硬件设备的测试。为检验所研发的控制器的功能和性能,在SYS/BIOS实时操作系统上完成动力定位应用程序的开发,搭建仿真平台验证该控制器的实时性和可靠性。展开更多
In order to deal with the dynamic positioning system control problems of dredgers working under strong dredging reaction or harsh environments,an adaptive backstepping method is proposed.Disturbances are estimated and...In order to deal with the dynamic positioning system control problems of dredgers working under strong dredging reaction or harsh environments,an adaptive backstepping method is proposed.Disturbances are estimated and compensated for by the adaptive method without extra sensors on dredging equipment,and the control mechanism is simplified.Adaptive control is used to compensate for the reaction and environmental disturbances on the dredger,so the dredger can maintain the desired position with a minimum error and shock.The proposed adaptive robust controller guarantees the global asymptotic stability of the closed-loop system and rapid position tracking of the dredger.The simulation results show that the proposed controller has superior performance in position tracking and robustness to large disturbances.展开更多
This paper researches how to apply the advanced control technology of model predictive control （MPC） to the design of the dynamic positioning system （DPS） of a semi-submersible platform. First, a linear low-freque...This paper researches how to apply the advanced control technology of model predictive control （MPC） to the design of the dynamic positioning system （DPS） of a semi-submersible platform. First, a linear low-frequency motion model with three degrees of freedom was established in the context of a semi-submersible platform. Second, a model predictive controller was designed based on a model which took the constraints of the system into account. Third, simulation was carried out to demonstrate the feasibility of the controller. The results show that the model predictive controller has good performance and good at dealing with the constraints or the system.展开更多
文摘以某起重船为控制对象,以SRI-VC2110DP动力定位控制系统为基础,设计一种基于数字信号处理器(Digital Signal Processor,DSP)的动力定位控制器。针对动力定位控制器的实际需求,围绕核心处理器进行双CAN总线接口、双以太网接口和I/O接口等硬件电路的开发,同时完成硬件设备的测试。为检验所研发的控制器的功能和性能,在SYS/BIOS实时操作系统上完成动力定位应用程序的开发,搭建仿真平台验证该控制器的实时性和可靠性。
基金The National Basic Research Program of China (973 Program) (No. 2005CB221505)Open Fund of Provincial Open Laboratory for Control Engineering Key Disciplines (No. KG2009-02)
文摘In order to deal with the dynamic positioning system control problems of dredgers working under strong dredging reaction or harsh environments,an adaptive backstepping method is proposed.Disturbances are estimated and compensated for by the adaptive method without extra sensors on dredging equipment,and the control mechanism is simplified.Adaptive control is used to compensate for the reaction and environmental disturbances on the dredger,so the dredger can maintain the desired position with a minimum error and shock.The proposed adaptive robust controller guarantees the global asymptotic stability of the closed-loop system and rapid position tracking of the dredger.The simulation results show that the proposed controller has superior performance in position tracking and robustness to large disturbances.
基金Supported by the Basic Research Foundation of Central University(HEUCFZ1003)
文摘This paper researches how to apply the advanced control technology of model predictive control （MPC） to the design of the dynamic positioning system （DPS） of a semi-submersible platform. First, a linear low-frequency motion model with three degrees of freedom was established in the context of a semi-submersible platform. Second, a model predictive controller was designed based on a model which took the constraints of the system into account. Third, simulation was carried out to demonstrate the feasibility of the controller. The results show that the model predictive controller has good performance and good at dealing with the constraints or the system.
