{"id":671,"date":"2021-01-30T20:24:20","date_gmt":"2021-01-30T20:24:20","guid":{"rendered":"https:\/\/elo-x.eu\/?p=671"},"modified":"2025-06-23T21:18:26","modified_gmt":"2025-06-23T21:18:26","slug":"renzi-wang","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=671","title":{"rendered":"Renzi Wang"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\n\t\t\t\tRenzi Wang \n\t\t\t<\/h2 > \n\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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PhD Candidate in Engineering Science<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Department of Electrical Engineering (ESAT), STADIUS division<\/b><\/span><\/p>

KU Leuven<\/b><\/span><\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Renzi Wang received her bachelor\u2019s degree in mechatronics with a focus on robotics from Harbin Institute of Technology, China, in 2018, and her master\u2019s degree in mechatronics with a focus on control theory from Karlsruhe Institute of Technology, Germany, in 2021. During her master’s study, she took one exchange semester at EPFL, Switzerland, and completed her master thesis with a topic on extending a data-enabled predictive control method on nonlinear systems there.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Project description<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Autonomous navigation in uncertain environments, where vehicles (autonomous cars, robots, drones) must interact with other vehicles or other users (e.g., pedestrians) was identified as a highly challenging control task. Thus, Renzi developed MPC strategies that took into account interaction and uncertainty. Traditional means of handling uncertainty such as robust or stochastic approaches were demonstrated to be either too conservative or too risk-prone. Moreover, existing MPC methodologies typically assumed that the uncertain behavior of surrounding users was completely exogenous, thus failing to take interaction into account. To explicitly account for interactions, Renzi developed stochastic models for surrounding users whose probabilistic structure depended on the states of other users. The parameter models were learned through historical trajectories using machine learning. The resulting NMPC formulations led to optimal control problems that were more complex and of larger scale than what state-of-the-art embedded solvers could handle. Renzi’s project successfully developed optimization algorithms for interaction-aware MPC for autonomous navigation in uncertain environments, including a novel EM++ parameter learning framework for stochastic switching systems and risk-sensitive MPC approaches with sequential convexification algorithms.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRead more about this project<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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1.<\/div>

Wang, Renzi; Schuurmans, Mathijs; Patrinos, Panagiotis<\/p>

Risk-Sensitive Model Predictive Control for Interaction-Aware Planning--A Sequential Convexification Algorithm<\/a> Working paper<\/span> <\/p>

2025<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@workingpaper{wang2025risk,
\r\ntitle = {Risk-Sensitive Model Predictive Control for Interaction-Aware Planning--A Sequential Convexification Algorithm},
\r\nauthor = {Renzi Wang and Mathijs Schuurmans and Panagiotis Patrinos},
\r\nurl = {https:\/\/doi.org\/10.48550\/arXiv.2503.14328},
\r\nyear  = {2025},
\r\ndate = {2025-03-18},
\r\nabstract = {This paper considers risk-sensitive model predictive control for stochastic systems with a decision-dependent distribution. This class of systems is commonly found in human-robot interaction scenarios. We derive computationally tractable convex upper bounds to both the objective function, and to frequently used penalty terms for collision avoidance, allowing us to efficiently solve the generally nonconvex optimal control problem as a sequence of convex problems. Simulations of a robot navigating a corridor demonstrate the effectiveness and the computational advantage of the proposed approach.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {workingpaper}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
This paper considers risk-sensitive model predictive control for stochastic systems with a decision-dependent distribution. This class of systems is commonly found in human-robot interaction scenarios. We derive computationally tractable convex upper bounds to both the objective function, and to frequently used penalty terms for collision avoidance, allowing us to efficiently solve the generally nonconvex optimal control problem as a sequence of convex problems. Simulations of a robot navigating a corridor demonstrate the effectiveness and the computational advantage of the proposed approach.<\/div>
Close<\/a><\/p><\/div>