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Real-time implementation of adaptive feedback and feedforward generalized predictive control algorithm

机译:自适应反馈和前馈广义预测控制算法的实时实现

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The adaptive generalized predictive control (GPC), which combines the process of system identification using recursive least-squares (RLS) algorithm and the process of generalized predictive feedback control design, has been presented and successfully implemented on testbeds. In this paper, the adaptive GPC algorithm is extended when the disturbance measurement signal is available for feedforward control. First, the adaptive feedback and feedforward GPC algorithm is presented when the disturbance is stochastic or random. Second, the adaptive algorithm is further extended when the disturbance is deterministic or periodic. In the second case, measured disturbance signals are used to estimate future disturbance values, which are used in the control design. The proposed adaptive GPC design algorithm with/without the future disturbance estimation is implemented in real time and demonstrated for the application of acoustic noise control, structural vibration control, and optical jitter control. (c) 2005 Elsevier Ltd. All rights reserved.
机译:提出了自适应广义预测控制(GPC),该系统结合了使用递归最小二乘(RLS)算法进行系统识别的过程和广义预测反馈控制设计的过程,并已在测试平台上成功实现。当干扰测量信号可用于前馈控制时,本文扩展了自适应GPC算法。首先,提出了当干扰为随机或随机时的自适应反馈和前馈GPC算法。其次,当干扰是确定性的或周期性的时,自适应算法将进一步扩展。在第二种情况下,将测得的干扰信号用于估计将来的干扰值,这些值将用于控制设计中。所提出的带有或不带有未来干扰估计的自适应GPC设计算法可以实时实现,并在声噪声控制,结构振动控制和光学抖动控制的应用中得到了证明。 (c)2005 Elsevier Ltd.保留所有权利。

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