In this article, we develop a deterministic learning control approach using an adaptive neural network (NN) for a two-degree-of-freedom helicopter nonlinear system subject to unknown backlash and model uncertainty. Fi...
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In this article, we develop a deterministic learning control approach using an adaptive neural network (NN) for a two-degree-of-freedom helicopter nonlinear system subject to unknown backlash and model uncertainty. First, by combining the backstepping and direct Lyapunov approaches, a novel adaptive NN control scheme with an inverse compensation method is proposed to address the input backlash nonlinearity, track specified trajectories, and stabilize the closed-loop system. Simultaneously, uncertain system dynamics are accurately identified and stored as learned knowledge in constant radial basis function NN weights, while satisfying partial persistent excitation. Subsequently, by extracting the learned knowledge, a learning-based controller is constructed to operate the same control tasks to achieve a superior control performance, less backlash nonlinearity, and minimal computational burden. Finally, the validity and efficacy of the proposed scheme are demonstrated through numerical examples and experiments.
This paper presents a newclustering-based fuzzy learningcontroller for a passive torque simulator (PTS) system in the presence of nonlinear friction and disturbance. An adaptive network-based fuzzy inference system i...
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This paper presents a newclustering-based fuzzy learningcontroller for a passive torque simulator (PTS) system in the presence of nonlinear friction and disturbance. An adaptive network-based fuzzy inference system is integrated with clustering algorithm to deal with unknown terms. Besides, a state-augmented technique is also employed in the framework of the backstepping method to improve the performance of system. The simplicity of design, fast learning speed and robust behavior are the main properties of the proposed controller for PTS system. In addition, the online computational burden is also alleviated due to employing the clustering algorithm. The stability of the closed-loop system is confirmed by the Lyapunov theorem. Furthermore, different simulation results are provided to validate the potential of the proposed control system in comparison with previous related research.
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