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The IMC Structure of Multi-rate Multivariable Predictive Control Systems and An Improved Algorithm 被引量:4
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作者 周立芳 钱积新 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2001年第3期273-279,共7页
Multirate multivariable predictive control system with the sampling mechanism that adjusts the plant inputs only once but detects the plant outputs several times during a period is examined. The IMC structure of the s... Multirate multivariable predictive control system with the sampling mechanism that adjusts the plant inputs only once but detects the plant outputs several times during a period is examined. The IMC structure of the system is derived, and its robust stability and zero steady state error characteristics are analyzed. A new control algorithm is developed by adding the variation of the outputs to the index performance. The simulation results show that the method is effective and has zeros steady-state error. 展开更多
关键词 MULTIRATE predictive control internal model control (imc)
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Analysis of Robustness of PID-GPC Based on IMC Structure 被引量:1
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作者 陈增强 毛宗星 +2 位作者 杜升之 孙青林 袁著祉 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2003年第1期55-61,共7页
Proportion integral differential generalized predictive control(PID-GPC), a new type of generalized predictive control(GPC) is introduced, and its quality is analyzed with internal model control (IMC). A very importan... Proportion integral differential generalized predictive control(PID-GPC), a new type of generalized predictive control(GPC) is introduced, and its quality is analyzed with internal model control (IMC). A very important characteristic, which distinguishes GPC from ordinary IMC, and the robust effect are found. At the same time, a robust region is obtained according to the control laws, so that the defect that the robust analysis could be carried out only with stable models is overcome. It is verified that the robustness of PID-GPC is stronger than general GPC. 展开更多
关键词 process control internal model control predictive control
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内模型神经网络在有源噪声控制中的应用
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作者 熊斌 张奇志 +1 位作者 周雅莉 吕小明 《电声技术》 2005年第11期58-60,共3页
采用IMC结构与人工神经网络方法解决了无法得到参考信号和系统非线性的问题,设计内模型神经网络控制器对噪声进行控制。通过仿真和实验证明,该控制结构能有效消除噪声中所含的周期噪声。非线性系统的仿真实例表明,内模型神经网络控制方... 采用IMC结构与人工神经网络方法解决了无法得到参考信号和系统非线性的问题,设计内模型神经网络控制器对噪声进行控制。通过仿真和实验证明,该控制结构能有效消除噪声中所含的周期噪声。非线性系统的仿真实例表明,内模型神经网络控制方法明显优于线性滤波X-LMS算法。 展开更多
关键词 有源噪声控制 imc结构 人工神经网络 非线性系统
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