A novel approximation of underwater robotic vehicle controller exploiting multi-point matching

Area of Science:

• Robotics
• Control Systems Engineering
• Optimization Algorithms

Background:

• Underwater robotic vehicle (URV) performance is significantly impacted by complex internal and external dynamics.
• Effective control is crucial for achieving desired momentum and operational efficiency in URVs.
• Approximating higher-order controllers to lower-order models offers potential for more economical and efficient control solutions.

Purpose of the Study:

• To develop an efficient, effective, and economical lower-order (LO) controller for higher-order (HO) underwater robotic vehicle (URV) systems.
• To approximate HO URV controllers to LO URV controllers using a multi-point matching technique.
• To optimize the controller approximation using the grey wolf optimization algorithm (GWOA).

Main Methods:

• Approximation of HO URV controller to a LO URV controller using expansion parameters.
• Minimization of errors between HO and LO controller expansion parameters via multi-point matching, formulated as an objective function (OF).
• Optimization of the OF using GWOA, subject to steady-state matching and Hurwitz stability criteria as constraints.

Main Results:

• The proposed multi-point matching approach successfully generated a LO URV model.
• Validation through comparison with existing LO URV models demonstrated the effectiveness of the proposed method.
• Performance evaluation showed the applicability of the derived LO controller, supported by response analysis and statistical data on performance error values (PEVs).

Conclusions:

• The presented method effectively approximates higher-order URV controllers to lower-order models.
• The integration of multi-point matching and GWOA provides an efficient and stable control strategy for URVs.
• The developed LO controller offers a practical and economical solution for URV applications.