Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 32, Issue 11, Pages 4890-4900Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2020.3026078
Keywords
Nonlinear systems; Artificial neural networks; Control design; Adaptive systems; Backstepping; Learning systems; Asymmetric output constraint; neural adaptive control; nonlinear systems; universal barrier function
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Funding
- Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China [ICT20099]
- National Natural Science Foundation of China [61860206008, 61773081, 61933012, 61833013, 61991403, 61803053]
- National Key Research and Development Program of China [2019YFB1703600]
- Science and Technology Development Fund, Macau [079/2017/A2, 0119/2018/A3, 196/2017/A3]
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A low-cost neuroadaptive tracking control solution is proposed for pure-feedback nonlinear systems under asymmetric output constraint. The solution is characterized by a novel output-dependent universal barrier function and a single parameter estimator, which ensure system stability and output constraint satisfaction.
For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal barrier function (ODUBF) is constructed such that not only the restrictive condition on constraining boundaries/functions is removed but also both constrained and unconstrained cases can be handled uniformly without the need for changing the control structure. In the second step, to reduce the computational burden caused by the neural network (NN)-based approximators, a single parameter estimator is developed so that the number of adaptive law is independent of the system order and the dimension of system parameters, making the control design inexpensive in computation. Furthermore, it is shown that all signals in the closed-loop system are semiglobally uniformly ultimately bounded, the tracking error converges to an adjustable neighborhood of the origin, and the violation of output constraint is prevented. The effectiveness of the proposed method can be validated via numerical simulation.
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