Predictor-based compensators for networked control systems with stochastic delays and sampling intervals.

Area of Science:

• Control Systems Engineering
• Networked Systems
• Stochastic Processes

Background:

• Stochastic time delays in networked control systems (NCS) challenge controller design and performance.
• Current methods use fixed, worst-case delays, limiting accuracy for distributed systems.
• Accurate modeling is crucial for distributed computational architectures and network communication.

Purpose of the Study:

• To propose a novel modeling framework for linear multiple-input multiple-output (MIMO) NCS with stochastic time delays.
• To develop a predictor-based compensator to mitigate the impact of these stochastic delays.
• To evaluate the compensator's effectiveness using a cooperative adaptive cruise control (CACC) benchmark.

Main Methods:

• Developed a stochastic linear time-varying (SLTV) state-space model to represent stochastic sampling and delays.
• Designed a predictor-based compensator using a filtered Smith predictor.
• Compared the proposed compensator against a baseline fixed-delay predictor on a CACC system.

Main Results:

• The proposed method demonstrated a 55% reduction in worst-case tracking error energy compared to the baseline.
• A 65% reduction in worst-case control effort was achieved with the novel compensator.
• Performance metrics were normalized relative to the delay-free case for clear comparison.

Conclusions:

• The proposed SLTV modeling framework effectively addresses stochastic time delays in NCS.
• The developed predictor-based compensator significantly improves performance over fixed-delay approaches.
• This work offers a more accurate and effective solution for controller synthesis in complex networked systems.