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Low-complexity prescribed performance tracking control for uncertain high-order nonlinear systems considering input quantization
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DOI：10.1007/s11071-023-08886-w.png)
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Low-complexity prescribed performance control (PPC) is a control methodology whose strongest feature is its structural simplicity, namely, uncertainty can be handled without unknown parameter estimators or approximation structures, e.g., fuzzy logic or neural networks, considered in the design of the control scheme. This work shows that low-complexity PPC can tackle uncertain high-order odd-rational power nonlinear dynamics, i.e., an integrator chain where the power is the ratio of odd integers. State of the art has shown the feasibility of PPC for strict-feedback nonlinear dynamics (an integrator chain with a power equal to one) or high-order odd-integer-power nonlinear dynamics. In this paper, we show that the same structural simplicity of PPC can be retained for the more general class of high-order odd-rational-power nonlinear systems. Note that all the methods proposed in the literature for handling high-order nonlinearities cannot be used with rational powers (unless restricting the system's nonlinearity growth). Therefore, a new lemma is proposed to handle the odd-rational power scenario without restricting the nonlinearities' growth. Interestingly, the developed scheme generalizes the most advanced PPC and effectively handles the input quantization, and unknown power problems studied recently.
Keyword:
High-order odd-rational power nonlinear systems
Low-complexity control
Prescribed performance control
Growth conditions
期刊
IF:
6
论文数:
1.4W
被引数:
4.1W
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