Hybrid spiral-dynamic bacteria-chemotaxis algorithm with application to control two-wheeled machines

Area of Science:

• Robotics and Control Systems
• Computational Intelligence
• Optimization Algorithms

Background:

• Bacterial Foraging Algorithm (BFA) offers good exploration but suffers from oscillation and slow convergence.
• Spiral-Dynamic Algorithm (SDA) provides faster convergence and stability but risks local optima.
• Controlling nonlinear systems like inverted pendulum vehicles requires robust optimization.

Purpose of the Study:

• To introduce and evaluate the Hybrid Spiral-Dynamic Bacteria-Chemotaxis (HSDBC) algorithm.
• To leverage the strengths of BFA and SDA to overcome their individual limitations.
• To optimize the performance and energy consumption of nonlinear two-wheeled vehicle platforms.

Main Methods:

• The HSDBC algorithm combines the chemotaxis strategy of BFA with the exploitation and convergence speed of SDA.
• The HSDBC approach was applied to control single and double inverted pendulum-like vehicles with extended rods.
• Performance was evaluated by comparing HSDBC against BFA and SDA on these nonlinear systems.

Main Results:

• The HSDBC algorithm demonstrated superior performance in optimizing the control of the nonlinear systems.
• Comparative analysis showed HSDBC outperformed both BFA and SDA in achieving better system performance.
• The hybrid approach effectively balanced exploration and exploitation for improved accuracy and convergence.

Conclusions:

• The HSDBC algorithm presents a significant advancement in controlling complex nonlinear systems.
• This hybrid optimization technique offers enhanced performance and energy efficiency for two-wheeled robotic platforms.
• HSDBC provides a robust solution for overcoming the limitations of existing BFA and SDA methods.