MUMBAI, India, Jan. 9 -- Intellectual Property India has published a patent application (202541133931 A) filed by Dr. Y. Devasena; Udaya Kiran P; Dr. Jini Varghese P; Dr. Srinivasan Nagaraj; Dr. R. Priya; Dr. M. Santhosh Kumar; Dr. R. Venkata Aravinda Raju; Mr. Swaroop Mallick; Dr. Chillara Soma Shekar; and Dr. S. Someshwar, Tirupati, Andhra Pradesh, on Dec. 31, 2025, for 'method and device based on fuzzy mathematical modeling and control system for dynamic systems with uncertainty and imprecision.'
Inventor(s) include Dr. Y. Devasena; Udaya Kiran P; Dr. Jini Varghese P; Dr. Srinivasan Nagaraj; Dr. R. Priya; Dr. M. Santhosh Kumar; Dr. R. Venkata Aravinda Raju; Mr. Swaroop Mallick; Dr. Chillara Soma Shekar; and Dr. S. Someshwar.
The application for the patent was published on Jan. 9, under issue no. 02/2026.
According to the abstract released by the Intellectual Property India: "This invention presents a pioneering method and device leveraging fuzzy mathematical modeling for controlling dynamic systems plagued by uncertainty and imprecision. Conventional deterministic models falter in scenarios with vague parameters or noisy measurements, such as in autonomous navigation or process control. The proposed framework fuses fuzzy logic with linear/nonlinear state-space representations, treating uncertainties as fuzzy sets with triangular membership functions over linguistic terms (e.g., "small uncertainty," "high imprecision"). The method involves: (i) fuzzification of inputs like error and its derivative; (ii) a rule base (e.g., 25-49 rules) blending domain expertise with perturbed dynamics; (iii) inference via min-max operations; and (iv) defuzzification yielding crisp actions. Novel adaptive mechanisms adjust membership slopes using error gradients, ensuring resilience to non-stationary noise. Stability is proven via fuzzy Lyapunov functions, minimizing conservative margins. The device, a compact embedded unit, integrates sensors, a fuzzy processor (e.g., via FPGA or MCU), and actuators, supporting real-time execution ( 5ms cycle). Case studies, including an inverted pendulum with 20% friction variance, show 25% faster convergence and 30% lower overshoot versus type-1 fuzzy PID. Prior arts like US8949170B2 overlook dynamic adaptation, while US7734400B2 limits to fault detection. This invention advances imprecise control, applicable to AI robotics, EVs, and Industry 4.0, promoting interpretable, efficient systems that mimic human-like decision-making under ambiguity. Scalable from simulation (MATLAB/Simulink) to hardware, it democratizes robust control for uncertain environments, potentially reducing operational costs by 15-20% through enhanced reliability."
Disclaimer: Curated by HT Syndication.
