Vaishali Patne
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Position:
Department: Lab: eMail: Homepage: |
PhD Researcher
Instrumentation and Control Embedded Systems Lab [email protected] http://www.coepembeddedlab.com/patne |
Biography
Mrs. Vaishali Patne is currently working as full time research scholar in the Department of Instrumentation and Control, at College of Engineering, Pune. She is working in Embedded Laboratory under the guidance of Dr. D. N. Sonawane. Her field of research includes Model Predictive Control and Embedded Control. She received her Bachelor's degree in Instrumentation and Control Engineering from College of Engineering, Pune in 1990 and Masters degree in Electronics and Telecommunication Engineering with specialization in Electronic Instrumentation in 1994 also from College of Engineering, Pune. She has total experience of over 15 years in industry as well as in teaching.
Personal
Research Interests
- Model Predictive Control
- Embedded Systems
- Optimization
PhD Thesis
Optimal Co-Design of Embedded Model Predictive Controller For Real Time Application
Abstract:
Model Predictive Control is a proven industry accepted technology for advanced control of many processes mainly in petrochemical and process industry. The main advantages of MPC are that it handles constraints explicitly; it can deal with challenging dynamics without much difficulty, and can cater to multiple inputs and outputs simultaneously.
In MPC, given dynamic model of the plant, we try to foresee the consequences of current input on the future behavior of the plant. In practice many industrial systems are inherently nonlinear in MPC, it requires solving the nonlinear programming problem at each sampling interval. This becomes computationally complex and therefore limits its applicability to the control applications where it requires sample time in the range of milliseconds and seconds.
This research proposes to implement Nonlinear Model Predictive Control (NMPC) algorithm on suitable embedded platform to improve its performance in terms of execution time and memory in order to achieve the fast sampling time. Various embedded platforms like ARM or Field Programmable Gate Array (FPGA) will be explored from software as well as hardware resources point of view. The various possible ways to improve the NMPC performance like:
Abstract:
Model Predictive Control is a proven industry accepted technology for advanced control of many processes mainly in petrochemical and process industry. The main advantages of MPC are that it handles constraints explicitly; it can deal with challenging dynamics without much difficulty, and can cater to multiple inputs and outputs simultaneously.
In MPC, given dynamic model of the plant, we try to foresee the consequences of current input on the future behavior of the plant. In practice many industrial systems are inherently nonlinear in MPC, it requires solving the nonlinear programming problem at each sampling interval. This becomes computationally complex and therefore limits its applicability to the control applications where it requires sample time in the range of milliseconds and seconds.
This research proposes to implement Nonlinear Model Predictive Control (NMPC) algorithm on suitable embedded platform to improve its performance in terms of execution time and memory in order to achieve the fast sampling time. Various embedded platforms like ARM or Field Programmable Gate Array (FPGA) will be explored from software as well as hardware resources point of view. The various possible ways to improve the NMPC performance like:
- To explore the various gradient based optimization algorithms
- To accelerate the performance of linear solver
- To accelerate the performance of matrix algebra
- Task partitioning between hardware and software to improve the storage and execution time for the NMPC
- To explore the various embedded platforms and possibility of algorithmic parallelism
- Eventually to come up the robust, numerical stable and feasible solution of NMPC to deploy in embedded platforms for real-time control application.
Publications
- Vaishali Patne for paper on "FPGA Implementation Framework for Low Latency Nonlinear Model Predictive Control", 21st IFAC World Congress in Berlin, Germany, July 12-17, 2020.
- Vaishali Patane, Deepak Ingole, Dayaram Sonawane, FPGA Implementation Framework for Explicit Hybrid Model Predictive Control, IFAC Advances in Control & Optimization of Dynamical Systems, (ACODS 2020), IIT Madras, India, Feb. 16-19, 2020.
