A Passivity-based Control Combined with Sliding Mode Control for a DC-DC Boost Power Converter

Abstract

In this paper, a passivity-based control combined with sliding mode control for a DC-DC boost power converter is proposed. Moreover, a passivity-based control for a DC-DC boost power converter is also proposed. Using a co-ordinate transformation of state variables and control input, a DC-DC boost power converter is passive. A new plant is zero-state observable and the equilibrium point at origin of this plant is asymptotically stable. Then, a passivity-based control is applied to this plant such that the capacitor voltage is equal to the desired voltage. Additionally, the sliding mode control law is chosen such that the derivative of Lyapunov function is negative semidefinite. Finally, a passivity-based control combined with sliding mode control law is applied to this plant such that the capacitor voltage is equal to the desired voltage. The simulation results of the passivity-based control, the sliding mode control and the passivity-based control combined with sliding mode control demonstrate the effectiveness and show that the capacitor voltage is kept at the desired voltage when the desired voltage, the input voltage E and the load resistor R are changed. The results show that compared with the passivity-based control, the passivity-based control combined with sliding mode control has better performance such as shorter settling time, 8.5 ms when R changes and it has smaller steady-state error, which is indicated by the value of integral absolute error (IAE), 0.0679 when the desired voltage changes. The paper has limitations such as the assumed circuit parameters.

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“…These studies collectively illustrate the evolving landscape of robotic control strategies crucial for enhancing precision and efficiency across various industrial applications. The development of robotic arm controllers has seen significant progress, incorporating adaptive fuzzy systems [12], optimal control [13], adaptive sliding mode control [14], and adaptive neural network tracking control [15]. These techniques aim to enhance precision and robustness within the control system.…”

“…Similarly, [13] present an optimized PID controller based on the Ant Colony Algorithm (ACO-PID) for precise position control of multi-articulated systems, showcasing the integration of nature-inspired optimization techniques into control strategies. [14] propose a passivity-based control combined with sliding mode control for DC-DC boost power converters, demonstrating the application of hybrid control schemes to address stability and robustness concerns in power electronics systems. Additionally, [15] introduce an adaptive neural network fast fractional sliding mode control for exoskeleton robots, highlighting the integration of machine learning techniques to cope with modeling uncertainties and external disturbances in rehabilitation applications.…”