Asymptotic Tracking Control for Uncertain MIMO Systems: A Biologically Inspired ESN Approach

Area of Science:

• Control Systems Engineering
• Computational Neuroscience
• Artificial Intelligence

Background:

• Existing neural network (NN) control methods often ensure only uniform ultimate boundedness for tracking errors.
• Uncertain multi-input multi-output (MIMO) systems present challenges due to modeling uncertainties and coupling nonlinearities.
• Biologically inspired computing offers novel approaches to complex control problems.

Purpose of the Study:

• To develop a biologically inspired echo state network (ESN)-based control method for asymptotic tracking in uncertain MIMO systems.
• To propose a diversified multiclustered echo state network (DMCESN) capable of handling system uncertainties and nonlinearities.
• To demonstrate that the proposed method achieves asymptotic convergence of tracking errors, surpassing existing NN-based approaches.

Main Methods:

• Establishing a biologically inspired echo state network (ESN) framework.
• Proposing a diversified multiclustered echo state network (DMCESN) by mimicking biological system characteristics.
• Applying the DMCESN to address modeling uncertainties and coupling nonlinearities in MIMO control systems.
• Conducting rigorous theoretical analysis to prove asymptotic convergence of tracking errors.

Main Results:

• The proposed DMCESN method enables asymptotic convergence of tracking errors.
• Numerical simulations confirm the effectiveness of the DMCESN.
• The DMCESN-based control scheme exhibits superior tracking performance when compared to multilayer feedforward network and traditional ESN-based control.

Conclusions:

• The developed biologically inspired DMCESN provides an effective solution for asymptotic tracking control in uncertain MIMO systems.
• The DMCESN outperforms existing NN-based control strategies in terms of tracking accuracy and convergence.
• This research highlights the potential of bio-inspired computing for advancing control system design.