Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays
Abstract
This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.
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Recent research has introduced several adaptive control approaches tailored for nonlinear systems with input delays.
“Recent research has introduced several adaptive control approaches tailored for nonlinear systems with input delays [29,30].”
From Fixed-Time Adaptive Control for Nonstrict-Feedback Nonlinear Systems with Input Delay and Unknown Backlash-Like Hysteresis (2025) · cites this paper as [30]
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- Authors
- Niu, B., & Li, L.
- Affiliations
- Dalian University of Technology (CN)
- ORCID
- Niu, Ben
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2018-06
- Volume
- 29
- Issue
- 6
- Pages
- 2638-2644
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2605565078
- Cited by
- 185
- References
- 43
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 18 · 2023: 12 · 2022: 14 · 2021: 27 · 2020: 24 · 2019: 35 · 2018: 33 · 2017: 4
- FWCI
- 16.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61673073, 61673072)
- Liaoning Provincial Natural Science Foundation, China (201602009)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Neural Networks Stability and Synchronization
- Keywords
- adaptive backstepping control, neural network control, output tracking control, MIMO nonlinear systems, switched systems, input delay, multiple Lyapunov functions, radial basis function neural network, semi-global uniform ultimate boundedness, switching signals, coordinate transformation, mass-spring-damper system
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