In this paper, we investigated trajectory tracking control for differential-drive mobile robots under unknown disturbances and limited communication resources. To address the singularity problem and the trade-off between high-precision finite-time tracking and communication resource conservation, we proposed a novel event-triggered sliding mode control scheme. The scheme achieved three major results: First, a specially designed sliding surface eliminated the singularity problem and ensured high convergence accuracy. Second, an adaptive disturbance observer accurately estimated unknown disturbances without requiring prior information. Third, an event-triggered mechanism significantly reduced communication demands while maintaining control performance. Importantly, the proposed controller avoided the high-gain issues present in methods. Comparative MATLAB simulations demonstrated faster convergence, smaller steady-state errors, and a favorable balance between tracking precision and communication efficiency.
Citation: Zhiqiang Chen, Duzhesheng Liao, Wanghui Yang. Event-triggered sliding mode control for differential-drive mobile robots[J]. AIMS Mathematics, 2026, 11(6): 16199-16234. doi: 10.3934/math.2026666
In this paper, we investigated trajectory tracking control for differential-drive mobile robots under unknown disturbances and limited communication resources. To address the singularity problem and the trade-off between high-precision finite-time tracking and communication resource conservation, we proposed a novel event-triggered sliding mode control scheme. The scheme achieved three major results: First, a specially designed sliding surface eliminated the singularity problem and ensured high convergence accuracy. Second, an adaptive disturbance observer accurately estimated unknown disturbances without requiring prior information. Third, an event-triggered mechanism significantly reduced communication demands while maintaining control performance. Importantly, the proposed controller avoided the high-gain issues present in methods. Comparative MATLAB simulations demonstrated faster convergence, smaller steady-state errors, and a favorable balance between tracking precision and communication efficiency.
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