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工程中的混合方法

ISSN 打印: 1099-2391
ISSN 在线: 2641-7359

Archives: Volume 1, 1999 to Volume 4, 2002

工程中的混合方法

DOI: 10.1615/HybMethEng.v2.i1.50
37 pages

ROBUST ADAPTIVE CONTROL OF SISO DYNAMIC HYBRID SYSTEMS

M. de la Sen
Departamento de Electricidad y Electrónica, Facultad de Ciencias, Universidad del Pais Vasco, Leioa (Bizkaia), Bilbao, Spain

ABSTRACT

This article deals with the problem of synthesizing a robust adaptive controller for a class of single-input, single-output (SISO), time-invariant hybrid plant that can operate under bounded disturbances and/or unmodeled dynamics. The hybrid plant dealt with is composed of two coupled subsystems, one being of continuous-time type while the other is digital. The estimation algorithm is of a continuous-time nature, because the plant parameter estimates are updated for all times. The adaptive scheme is pole-placement-based and of indirect type, because the controller parameters are reupdated at all times based on the calculated plant parameter estimates. An input-output model is first derived which involves filtered signals for the hybrid plant from an initial state-space description. Such a model is simultaneously driven by the standard continuous-time input plus an extra signal. The extra input is composed for all times of a signal that involves the contribution of the input and output over a finite number of previous sampling instants plus a signal which involves the contribution of the weighted integral of the continuous-time input on a set of preceding sampling intervals. The last driving signal is due to the existing couplings between the continuous-time and digital substates of the hybrid plant. A relative adaptation dead zone is used in the parameter estimation scheme whose role is robust adaptive stabilization in the presence of uncertainties. The hybrid nature of the system becomes apparent, because the plant is simultaneously driven by the continuous time input plus its samples at sampling instants. As a result, its input-output differential equation has forcing terms generated by the system description at sampling instants.


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