design control planes

This work addresses nonlinear systems subject to unknown dynamics and external disturbances. Methodologically, it proposes an integrated online system identification and model predictive control (MPC) framework that combines reproducing kernel Hilbert space (RKHS) modeling, random Fourier feature approximation, online least-squares parameter adaptation, and learning-based receding-horizon MPC—compatible with control-affine structures. The approach achieves sublinear dynamic regret against an adversarial clairvoyant controller for the first time, while ensuring finite-time near-optimality and asymptotic convergence to optimality. To jointly handle modeling errors and exogenous disturbances, it introduces self-supervised learning and state- and input-adaptive disturbance modeling. Extensive validation is conducted on an inverted pendulum, quadrotor simulation, and real-world quadrotor hardware under challenging conditions—including wind gusts, ground effect, and aerodynamic drag—demonstrating robustness and high-precision trajectory tracking performance.

Traditional robot design and control are typically decoupled, leading to morphologies poorly aligned with task requirements. This paper proposes a simulation-driven co-optimization framework for morphology and control, breaking the conventional “design-then-control” paradigm to enable task-oriented, end-to-end joint search. Our method employs gradient-free optimization to simultaneously evolve structural parameters and controller policies within a URDF-based multi-task reinforcement learning simulation environment. Key contributions include: (1) demonstrating that controller retraining significantly improves performance, yielding an average gain of 37%; and (2) revealing an inverse correlation between morphological complexity and controller training budget—providing theoretical justification for structural simplification under resource constraints. We validate the framework across four public simulation benchmarks, showing that co-optimization consistently yields more compact, robust, and task-adapted robot morphologies compared to sequential approaches.

This paper addresses discrete-time interconnected systems whose subsystem dynamics and interconnection topology are partially unknown. Method: We propose a data-driven, compositional approach to construct finite-state abstractions for formal verification and distributed controller synthesis. Subsystems are modeled individually from input-output data, and—novelly—the unknown static interconnection mapping is treated as a learnable object, enabling its symbolic abstraction. Compositionality and rigorous error propagation analysis ensure that the resulting abstraction strictly satisfies an approximate simulation relation. Contribution/Results: We theoretically establish scalability and verifiability of the abstraction. Experiments demonstrate substantial mitigation of the curse of dimensionality, enabling high-precision, low-complexity controller synthesis while preserving formal guarantees.

Traditional control theory neglects computational uncertainty—such as mathematical object distortion induced by finite-precision arithmetic—leading to reliability gaps between Lyapunov stability analysis and digital controller implementation. Methodologically, this paper introduces the first constructive control framework that explicitly treats computational uncertainty as an independent modeling dimension in controller synthesis and system analysis. Leveraging tools from computability theory, constructive analysis, and measurable selection, we establish a constructive Danskin theorem and provide computable reconstructions of fundamental objects—including control Lyapunov functions (CLFs), Carathéodory trajectories, and eigenvalue problems. Our primary contribution is a computationally feasible paradigm for stability and stabilization proofs: all mathematical constructs are uniformly approximable by finite-precision algorithms while rigorously preserving required properties. This ensures robustness and implementability of digital controllers under realistic computational constraints.

This work addresses discrete-time nonlinear optimal control problems by unifying classical algorithms—including gradient descent, Gauss–Newton, Newton’s method, and differential dynamic programming (DDP)—within a differentiable programming framework. Methodologically, it introduces the first modular, end-to-end differentiable algorithm template library built upon linear/quadratic approximations (e.g., LQR), enabled by automatic differentiation. Theoretically, it provides a unified derivation of computational complexity and sufficient optimality conditions across all methods. Practically, it incorporates adaptive line search and regularization strategies, and validates efficacy on benchmark tasks such as autonomous racing with a bicycle model. All implementations are open-sourced, demonstrating both efficient gradient propagation and strong generalization across diverse control problems.

This study addresses the challenges of control design in complex industrial processes characterized by multivariable coupled dynamics by proposing an automated control strategy generation framework that integrates large language models (LLMs) with Bayesian optimization. The approach decomposes control design into structured code generation steps, ensuring physical consistency through execution-based validation and feedback-driven repair. It pioneers the automatic synthesis of decentralized PI controller architectures and their tuning environments directly from dynamic process models. Evaluated on a nonlinear gas preheater benchmark, the generated control schemes—subsequently refined via Bayesian optimization—achieve a 26.5% improvement in closed-loop performance and significantly enhance the transient response of pressure loops, thereby demonstrating the method’s effectiveness and novelty.

This study demonstrates that adversaries can exploit ArduPilot’s sensitivity to parameter changes by issuing short sequences of legitimate MAVLink commands to covertly manipulate PID gains, EKF configurations, and failsafe mechanisms across multiple control layers, thereby steering the drone into unsafe states. Through software-in-the-loop (SITL) simulations and experiments on the Pixhawk 2.4.8 hardware platform, the work provides the first systematic evidence that combining authorized commands with logical vulnerabilities in control logic can critically degrade attitude stability, angular rate response, trajectory tracking accuracy, and state estimation integrity—ultimately leading to loss of control or crash. These findings expose critical security blind spots in flight controller implementations concerning parameter handling and state validation.

This work addresses the common challenges in autonomous drone development—such as fragmented requirements, architectural inconsistencies, and poor traceability—stemming from disjointed design processes. To bridge these gaps, the authors propose a SysML-based model-driven systems engineering framework that integrates a unified four-layer model encompassing requirements, functions, logical components, and physical/software elements. Crucially, this approach establishes, for the first time, a deep alignment between multiple SysML diagrams—including requirement, activity, and block definition diagrams—and the ROS 2 architecture, specifically its nodes, topics, services, and actions. The framework enables end-to-end traceable design, allowing early-stage allocation of requirements, precise interface specification, clear subsystem responsibility assignment, and verification planning—all prior to simulation or deployment—thereby effectively supporting typical mission scenarios such as obstacle avoidance and return-to-home operations.

This work addresses the lack of machine-verifiable foundations in control theory for cyber-physical systems by developing an open-source formal library within the Lean interactive theorem prover. The library formalizes Lyapunov stability theory and the small-gain theorem, supporting continuous, discrete, and hybrid dynamical systems. A key contribution is a unified formulation of Lyapunov’s theorem applicable to both points and sets, alongside a relational definition of input–output systems that avoids well-posedness assumptions, enabling a fully formalized proof of the small-gain theorem. Leveraging mathematical tools such as neighborhood filters, the project establishes a scalable verification framework for control theory, laying the groundwork for trustworthy, machine-checked validation of cyber-physical systems.