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Generalised predictive control with input constraints

机译:具有输入约束的广义预测控制

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Generalised predictive control (GPC), which has been shown to be effective for the self-tuning control of complex plants, is based on the minimisation of a long-range cost function. In previous work this has been achieved by an analytic solution which is not valid if there are constraints on the input control signals. A simple extension is proposed which caters for rate or amplitude limits, and simulations show the corresponding improvements in closed-loop performance that can be achieved. A common problem, in practice, is the effect of torque saturation in DC motor drives: results from a compliant link, where the control sample-rate is 60 Hz, show that the proposed method overcomes this and is also computationally acceptable. Bang-bang control, a limiting case of constrained inputs, is also treated within the GPC framework.
机译:通用预测控制(GPC)已被证明对复杂工厂的自调整控制有效,它基于最小化长期成本函数。在以前的工作中,这是通过解析解决方案实现的,如果输入控制信号存在约束,该解析解决方案将无效。提出了一个简单的扩展方案来满足速率或幅度限制,并且仿真显示了可以实现的闭环性能的相应改进。实际上,一个常见的问题是直流电动机驱动器中的扭矩饱和效应:来自顺应链接的结果(控制采样率为60 Hz)表明,所提出的方法克服了这一问题,并且在计算上也是可以接受的。 GPC框架中也处理了Bang-bang控制(一种受限输入的情况)。

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