報(bào)告時(shí)間: 11月4日（周一）上午8:30-12:00，下午13:30-17:00

      11月5日（周二）上午8:30-12:00,，下午13:30-17:00

報(bào)告地點(diǎn)：船電樓A310

報(bào)告摘要：

Adaptive control covers a set of techniques which provide a systematic approach for automatic adjustment of the controllers in real time, in order to achieve or to maintain a desired level of performance of the control system when the parameters of the plant dynamic model are unknown and/or change in time. Networked adaptive control refers to adaptive loops in the presence of network-induced constraints (packet losses, quantized information, etc.) or distributed implementations (multi-agent consensus, coordination, etc.).

The course presents a basic ground for analysis and design of adaptive and networked adaptive control systems: it covers both established adaptive schemes based on continuous adaptation, more recent logic-based adaptive schemes with discontinuous adaptation, and applications in connected and automated vehicles. After an introduction to Model Reference Adaptive Control (MRAC), such a framework will be discusses both from a continuous adaptation point of view and in discontinuous environments, with emphasis on networked environments (switched dynamics and quantization phenomena). Such dynamics are useful to deal with connected and automated vehicles, where vehicle-to-vehicle communication naturally gives rise to switched communication and other network-induced constraints.

報(bào)告內(nèi)容：

(Day 1)

Lecture 1: Basics of Model Reference Adaptive Control (MRAC)

Lecture 2: Robust adaptive control and adaptive switched control

Lecture 3: Adaptive control for switched and networked dynamics

Lecture 4: Introduction to connected and automated vehicles

(Day 2)

Lecture 5: MRAC for adaptive platooning

Lecture 6: Adaptive platooning with switched scenarios

Lecture 7: Adaptive disturbance decoupling in platooning

Lecture 8: Platoons of mixed human-driven/automated vehicles

Prerequisites:

Notions of linear systems theory and Lyapunov stability at the intermediate level might help.

Notions of hybrid/switched systems might turn out useful as well.

Suggested book:

Ioannou P. A. and Fidan B., Adaptive Control Tutorial, SIAM, 2006.

報(bào)告人簡介:

Simone Baldi is a professor at Southeast University, Jiangsu Provincial Key Lab of Networked Collective Intelligence since 2019. Prior to this, he held assistant professor position at Delft University of Technology in the group of prof. Bart De Schutter (IEEE Fellow), postdoctoral positions at ITI-CERTH and University of Cyprus cooperating with profs. Elias Kosmatopoulos, Petros Ioannou (IEEE Life Fellow, IFAC Fellow, former EiC of IEEE-ITS) and Markos Papageorgiou (IEEE Life Fellow, IFAC Fellow, former EiC of Transp. Reas. C). He got his Ph.D. in Systems Engineering from University of Florence, Italy, in 2011, under prof. Edoardo Mosca (IEEE Life Fellow, IFAC Fellow). His research interests focus on adaptive and learning systems, with applications in cooperative traffic and smart energy. Within European and Chinese projects, some of the algorithms by dr. Baldi have been implemented in intelligent traffic systems and energy-efficient solutions, in cooperation with the Traffic Control Department of the city of Chania, Honeywell, Fraunhofer Institute for Building Physics, and Dutch Central Government Real Estate Agency. Prof. Baldi was awarded outstanding reviewer of Applied Energy (2016), Automatica (2017), IEEE/CAA Journal of Automatica Sinica (2019). He serves as a subject editor of Int. Journal of Adaptive Control and Signal Processing, and as an associate editor of Journal of The Franklin Institute, IEEE/ASME Trans. on Mechatronics, IEEE Control Systems Letters.

船舶電氣工程學(xué)院

2024年11月1日