Document Type : Research Article
Authors
1 Department of Electrical Engineering, University of Bojnord, Bojnord, Iran
2 Department of Electrical Engineering, Sab.C., Islamic Azad University, Sabzevar, Iran
Abstract
This paper proposes a novel observer-based adaptive neural command filter (CF) output-feedback tracking control scheme for uncertain nonlinear multiple-input multiple-output (MIMO) stochastic systems subject to constrained partial tracking errors (PTEs). The key contributions are threefold. First, a linear Luenberger-type state observer is designed to handle unmeasured states, and radial basis function neural networks (RBFNNs) are employed to approximate unknown nonlinear functions. Second, a dynamic surface control (DSC) strategy augmented with compensating signals simultaneously eliminates the "explosion of complexity" inherent in conventional backstepping and rectifies filter-output errors in the DSC framework. Third, a minimal learning parameter (MLP) technique — based on Young's inequality — reduces the number of online-tunable parameters to a single scalar per subsystem, yielding a computationally efficient adaptive law. The closed-loop system is rigorously proven to be semi-globally uniformly ultimately bounded (SGUUB) in probability via Lyapunov-based stochastic stability analysis, and all PTEs remain within prescribed performance bounds throughout transient and steady-state operation. Comparative simulations on a second-order MIMO stochastic benchmark demonstrate that the proposed approach achieves significantly lower root mean square and integral absolute tracking errors than existing methods, while maintaining bounded and smooth control effort. Current limitations, including the restriction to strict-feedback topologies and the assumption of bounded external disturbances, motivate future extensions to multi-agent and event-triggered control frameworks.
Highlights
- A novel observer-based adaptive neural command filter output-feedback control is developed for uncertain MIMO stochastic systems.
- A compensated dynamic surface control (DSC) strategy is proposed to simultaneously eliminate the "explosion of complexity" and filter-output errors.
- The minimal learning parameter (MLP) technique, based on Young’s inequality, reduces online tuning to a single scalar per subsystem.
- Prescribed performance bounds are strictly enforced for both transient and steady-state tracking error constraints.
- Stochastic stability is rigorously proven, ensuring all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB) in probability.
Keywords
- Adaptive neural control
- Output-feedback control
- Nonlinear stochastic systems
- Prescribed performance control
- Command filtered backstepping
- Dynamic surface control
Main Subjects
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