Introduces the ε-gAAS relation for continuous-time systems to enable hierarchical control by tolerating larger mismatches between abstract and concrete models than existing simulation relations.
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Model-free unicycle source seeking controller with exponential convergence for higher-degree local objectives, supported by theory, simulations, and robotic experiments.
Graph-based modeling with restricted additive Schwarz decomposition in interior-point methods accelerates transient gas network optimization and multi-period AC optimal power flow by over 300%.
Introduces a DUIO framework that combines local state reconstruction with distributed optimization to achieve bounded-error estimation in discrete-time systems with unknown inputs.
Derives explicit formula for causally conditioned directed information rate of Gaussian sequences based on optimal prediction and proves O(N^{-1/2} log N) high-probability error bound for the resulting estimator.
A tensor decomposition technique enables scalable correct-by-design control synthesis for stochastic systems, delivering probabilistic guarantees for temporal logic specifications via robust dynamic programming and approximate simulation relations.
Uniform controllability and observability imply exponential decay of sensitivity under uniform Hessian boundedness, uSOSC, and uLICQ in dynamic optimization.
Generalizes 1D lumped OBSF control to 2D boundary-controlled Mindlin plates via staggered-grid finite-difference discretization, controllability decomposition, and strictly positive real gain design for guaranteed stability.
Robust stabilization conditions are derived for uncertain discrete switched affine systems with input delay via Lyapunov analysis and a nominal-parameter predictive min-switching controller, proving exponential convergence of trajectories and predictions to a robust limit cycle.
Extends ALADIN with adjoint SQP and event-triggered updates to achieve local convergence and improved communication efficiency in real-time distributed optimization.
Anderson acceleration speeds up local convergence of linearly converging SQP-type methods, with a heuristic for distant points, shown via optimal control examples in acados.
Physics-informed neural network models for large-steering-angle vehicle dynamics outperform purely physical baselines in accuracy while using less computation.
Reinforcement learning parametrizes duty cycles to solve switching-time optimal control for binary light inputs in optogenetic bioprocesses more scalably than mixed-integer optimization on fine grids.
Develops parallel continuous and discrete distributed adaptive estimation schemes over directed graphs that prove stability, signal boundedness, estimate convergence to the source, and ISS robustness bounds despite model uncertainty and disturbances.
The authors derive time-dependent LMI conditions via Lyapunov-Metzler inequalities for global asymptotic stability of observer-based sampled-data switched systems with Lipschitz nonlinearities under dwell-time constraints.
A prototype 3-DOF micro parallel robot actuated by base-integrated HASEL actuators is designed and modeled with port-Hamiltonian dynamics plus kinematics, parameters identified from laser-tracked experiments using nonlinear grey-box estimation.
Racing-parameterized DRL policies for AV collision avoidance outperform an MPC-APF baseline in simulation across three scenarios, achieve zero-shot hardware transfer, and run at 31x fewer FLOPS with 64x lower latency.
LSTM occupancy detection generalizes across apartments with 0.84 accuracy while logistic regression provides a competitive low-complexity alternative on same-apartment data.
A backstepping-based adaptive impedance controller with Taylor-series and force-bound estimators achieves semi-global practical finite-time stability for robots in uncertain contact without requiring dynamic parameters.
A multi-agent LLM system using CrewAI and RAG improves response coherence and correctness over a single-LLM RAG baseline for Brazilian labor law Q&A.
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Hierarchical Control for Continuous-time Systems via General Approximate Alternating Simulation Relations
Introduces the ε-gAAS relation for continuous-time systems to enable hierarchical control by tolerating larger mismatches between abstract and concrete models than existing simulation relations.
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Distributed State Estimation for Discrete-Time Systems With Unknown Inputs: An Optimization Approach
Introduces a DUIO framework that combines local state reconstruction with distributed optimization to achieve bounded-error estimation in discrete-time systems with unknown inputs.
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Correct-by-Design Control Synthesis of Stochastic Multi-agent Systems: a Robust Tensor-based Solution
A tensor decomposition technique enables scalable correct-by-design control synthesis for stochastic systems, delivering probabilistic guarantees for temporal logic specifications via robust dynamic programming and approximate simulation relations.
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Robust predictive control design for uncertain discrete switched affine systems subject to an input delay
Robust stabilization conditions are derived for uncertain discrete switched affine systems with input delay via Lyapunov analysis and a nominal-parameter predictive min-switching controller, proving exponential convergence of trajectories and predictions to a robust limit cycle.
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System Identification for Dynamic Modeling of Large Steering Angle Vehicles
Physics-informed neural network models for large-steering-angle vehicle dynamics outperform purely physical baselines in accuracy while using less computation.
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Switching-time bioprocess control with pulse-width-modulated optogenetics
Reinforcement learning parametrizes duty cycles to solve switching-time optimal control for binary light inputs in optogenetic bioprocesses more scalably than mixed-integer optimization on fine grids.
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Distributed Adaptive Estimation with ISS Guarantees for Sensor Networks with Partially Unknown Source Dynamics
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