Dr. Maolong Lv
Faculteit Luchtvaart- en Ruimtevaarttechniek, TU Delft, 2628 CD Delft, The Netherlands
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Academic Career Length
11Years (2016-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications4 November 2020 in IEEE Transactions on Cybernetics
Journal Article
This work investigates the consensus tracking problem for high-power nonlinear multiagent systems with partially unknown control directions. The main challenge of considering such dynamics lies in the fact that their linearized dynamics contain uncontrollable modes, making the standard backstepping technique fail; also, the presence of mixed unknown control directions (some being known and some being unknown) requires a piecewise Nussbaum function that exploits the a priori knowledge of the known control directions. The piecewise Nussbaum function technique leaves some open problems, such as Can the technique handle multiagent dynamics beyond the standard backstepping procedure? and Can the technique handle more than one control direction for each agent? In this work, we propose a hybrid Nussbaum technique that can handle uncertain agents with high-power dynamics where the backstepping procedure fails, with nonsmooth behaviors (switching and quantization), and with multiple unknown control directions for each agent.
14 April 2021 in IEEE Transactions on Neural Networks and Learning Systems
Journal Article
This work investigates a reduced-complexity adaptive methodology to consensus tracking for a team of uncertain high-order nonlinear systems with switched (possibly asynchronous) dynamics. It is well known that high-order nonlinear systems are intrinsically challenging as feedback linearization and backstepping methods successfully developed for low-order systems fail to work. Even the adding-one-power-integrator methodology, well explored for the single-agent high-order case, presents some complexity issues and is unsuited for distributed control. At the core of the proposed distributed methodology is a newly proposed definition for separable functions: this definition allows the formulation of a separation-based lemma to handle the high-order terms with reduced complexity in the control design. Complexity is reduced in a twofold sense: the control gain of each virtual control law does not have to be incorporated in the next virtual control law iteratively, thus leading to a simpler expression of the control laws; the power of the virtual and actual control laws increases only proportionally (rather than exponentially) with the order of the systems, dramatically reducing high-gain issues.
18 June 2021 in IEEE/ASME Transactions on Mechatronics
Journal Article
This article proposes a fuzzy adaptive design solving the finite-time constrained tracking for hypersonic flight vehicles (HFVs). Actuator dynamics and asymmetric time-varying constraints are considered when solving this problem. The main features of the proposed design lie in 1) introducing a novel piecewise but differentiable switching control law, with an appropriate design thought to avoid the singularity issues typical of finite-time control; 2) handling actuator magnitude, bandwidth, and rate constraints, thanks to the introduction of an auxiliary compensating system counteracting the adverse effects caused by actuator physical constraints, while guaranteeing the closed-loop stability; and 3) handling asymmetric time-varying state constraints, thanks to the introduction of tan-type barrier Lyapunov functions working for both constrained and unconstrained scenarios. Comparative simulation results illustrate the effectiveness of the proposed strategy over existing methods for HFVs in terms of convergence, smoothness, actuator performance, and constraints satisfaction.
22 March 2023 in IEEE Transactions on Cybernetics
Journal Article
A fuzzy-neural-approximation-based pseudo nonaffine control protocol is proposed for waverider vehicles (WVs), which is capable of guaranteeing tracking errors with desired prescribed performance and rejecting the obstacle of fragility inherent to the traditional prescribed performance control (PPC). The pseudo control is defined to approximate the nonaffine dynamics of WVs, while there is no need of model affinization. Furthermore, fuzzy neural approximators are combined with the adaptive compensation strategy to resist both system uncertainties and external disturbances. Especially, a new type of nonfragile prescribed performance, being able to self-adjust its prescribed funnel, is proposed to remedy the fragility defect associated with the existing PPC. Finally, the realizability of the spurred prescribed performance is proved via stability proof, and the superiority of the addressed design is tested by compared simulations.
1 February 2022 in IEEE Transactions on Automatic Control
Journal Article
Practical tracking results have been reported in the literature for high-order odd-rational-power nonlinear dynamics (a chain of integrators whose power is the ratio of odd integers). Asymptotic tracking remains an open problem for such dynamics. This note gives a positive answer to this problem in the framework of prescribed performance control (PPC), without approximation structures (neural networks, fuzzy logic, etc.) being involved in the control design. The unknown system uncertainties are first transformed to unknown but bounded terms using barrier Lyapunov functions, and then these terms are compensated by appropriate adaptation laws. A method is also proposed to extract the control terms in a linear-like fashion during the control design which overcomes the difficulty that virtual or actual control signals appear in a non-affine manner. A practical poppet valve system is used to validate the effectiveness of the theoretical findings.
1 December 2023 in IEEE Transactions on Aerospace and Electronic Systems
Journal Article
About Me
Gongxian Lou is a Master’s degree candidate at the College of Air Traffic Control and Navigation, Air Force Engineering University. His main research interest focuses on the robust control of unmanned aerial vehicles (UAVs). He has been dedicated to exploring the application of robust control theories in UAV systems, aiming to enhance the operational stability and anti-interference capability of UAVs. Maolong Lv received the Ph.D. degree from the Delft Center for Systems and Control, Delft University of Technology, Deft, The Netherlands, in 2021. He was a Visiting Scholar with the University of Grenoble, Saint-Martin-d'Hères, France, from 2018 to 2019. He is currently with Air Force Engineering University, Xi'an, China. His research interests include adaptive learning control, distributed control, reinforcement learning, and intelligent decision-making with applications in unmanned ariel vehicles, intelligent air combat, and man–unmanned teaming. Prof. Maolong Lv was the recipient of a Descartes Excellence Fellowship from the French Government in 2018, which allowed him a research visit at University of Grenoble. He is a
national-level young talent in China. He is currently the associate editor of IEEE Robotics and Automation Letters, Chinese Journal of Aeronautics (CJA), etc.