Abstract
A special class of nonlinear systems are studied in this paper in the context of fuel optimal control, which feature parametric uncertainties and confined control inputs. The control objective is to minimize the integrated control cost over the applicable time horizon. The conventional adaptive control schemes are difficult to apply. An innovative design approach is proposed to handle the uncertain parameters, physical limitations of control variables and fuel optimal control performance simultaneously. The proposed control design methodology makes an analysis of the fuel control problem for nominal cases, employs a hierarchical neural network structure, constructs the lower level neural networks to identify the switching manifolds, and utilizes the upper level neural network to coordinate the outputs of the lower level neural networks to achieve the control robustness in an approximately fuel-optimal control manner. Theoretical results are presented to justify the proposed design procedures for synthesizing adaptive, intelligent hierarchical neural controllers for uncertain nonlinear systems.
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Chen, D., Yang, J., Mohler, R.R. (2008). Neural Control of Uncertain Nonlinear Systems with Minimum Control Effort. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_34
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DOI: https://doi.org/10.1007/978-3-540-87732-5_34
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