adaptive controller evaluation

Existing robust adaptive control methods for quadrotors lack a unified evaluation benchmark, hindering fair cross-task, cross-simulation-platform, and cross-implementation comparisons. To address this, we propose a modular simulation testing framework built upon RotorPy, which integrates canonical disturbance models—including wind gusts, payload variations, motor failures, and control latency—to enable standardized stress testing of diverse adaptive controllers (e.g., MRAC, L₁ adaptive control, neural-network-based adaptive control). The framework features a configurable trajectory generator, a modular disturbance modeling suite, and a multi-dimensional performance assessment toolkit that quantifies tracking accuracy, stability margins, and recovery capability. Experimental validation demonstrates its effectiveness under compound disturbances, significantly improving evaluation reproducibility and comparability. The open-source implementation facilitates rapid deployment and extensibility for novel algorithm development.

This study addresses the persistent gap between theoretical control performance and its practical realization in real-world robotic systems, often caused by inadequate discretization, insufficient real-time guarantees, and weak error handling in control software. For the first time from a software engineering perspective, the authors systematically analyze 184 open-source robotic controllers through code review, empirical analysis, and test evaluation, uncovering common deficiencies in application scenarios, implementation details, and verification practices. The findings reveal that most implementations fail to properly account for critical system constraints, and their testing strategies inadequately validate the theoretical assurances they claim. This work highlights a significant disconnect between implementation quality and theoretical promises, offering concrete directions and practical guidelines for developing reliable, verifiable robotic control software.

This paper addresses the fundamental disconnect between online learning and adaptive control—spanning analytical paradigms, performance metrics (e.g., regret bounds vs. stability/convergence), and modeling assumptions. Methodologically, it unifies both fields under a gradient descent and streaming regression framework, enabling the first systematic theoretical comparison between regret minimization and model-reference adaptive control (MRAC). The key contributions are threefold: (i) a precise characterization of intrinsic differences between the two paradigms in terms of objective functions, system assumptions, and applicability; (ii) rigorous sufficient conditions under which regret-optimal policies guarantee stable convergence in dynamic systems; and (iii) a novel controller design framework that jointly ensures asymptotic convergence and sublinear cumulative error, thereby laying a rigorous foundation for algorithms that unify learning efficiency with control reliability.

This work addresses the challenge of reference trajectory tracking for uncertain nonlinear systems with limited data by proposing a meta-learning-based control framework. The approach learns a shared dynamic representation from structurally similar source systems during an offline phase and enables rapid adaptation of the controller to a new target system using only a few online data samples. Innovatively adapting implicit Model-Agnostic Meta-Learning (iMAML) to the control domain, the method establishes a general bilevel optimization framework compatible with diverse learning algorithms while significantly reducing memory overhead and approximation error. Two implementation pathways—neural state-space models and deep Q-networks, corresponding respectively to explicit and implicit system identification—are evaluated through simulations and hardware experiments, consistently demonstrating superior control performance over baseline methods and confirming the framework’s effectiveness and practicality.

Conventional low-level controllers for quadcopters require precise dynamical modeling and extensive parameter tuning, limiting generalization across platforms with substantial differences in mass, size, and actuator capabilities. Method: We propose a model-free, parameter-free learning-based low-level controller that jointly leverages imitation learning and deep reinforcement learning. It implicitly identifies system parameters online from sensor-action histories, enabling real-time adaptive control without explicit system identification. Contribution/Results: We introduce the first end-to-end latent-state system identification framework, achieving unprecedented dynamical generalization: in simulation, it adapts to unseen parameter combinations spanning 16× the training range; on physical hardware, it robustly handles 3.7× mass variation and >100× differences in propeller constants, while tolerating severe disturbances including payload asymmetry and single-motor failure. The controller has been successfully deployed on real drones, significantly enhancing the universality and engineering practicality of low-level flight control.

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 work addresses the vulnerability of surrogate models in digital twins to concept drift under shifting operational conditions, which degrades both predictive accuracy and uncertainty quantification. To mitigate this, the authors propose an adaptive digital twin framework that integrates multivariate distribution drift detection based on Fisher scores, a parameter-efficient LoRA-based continual learning mechanism for model adaptation, and Mann-Whitney U test–driven online statistical validation to assess the necessity of updates, enable efficient fine-tuning, and ensure reliability. Evaluated on a stochastic linear system and a directed energy deposition additive manufacturing task, the approach significantly accelerates drift detection, enhances model recovery accuracy, and improves the quality of uncertainty estimates, thereby enabling trustworthy continuous deployment of surrogate models.

This study addresses the lack of effective evaluation benchmarks and unclear edge-deployment capabilities of large language models (LLMs) in designing feedback controllers for complex dynamic systems. The authors introduce CoDyControlBench, the first multidimensional benchmark encompassing 132 system configurations across five dimensions—including degrees of freedom and system type—to systematically evaluate the control design capabilities of six prominent LLMs. They further propose a reasoning-distillation-based approach to derive lightweight models suitable for edge deployment. Experimental results show that GPT achieves a 94.8% success rate on this benchmark. The distilled 1.5B-parameter model demonstrates stable performance in simulations across systems with 1–6 degrees of freedom and attains 100% target-tracking success in real-world experiments on a pneumatic artificial muscle robotic arm, significantly enhancing both performance and generalization of edge-deployable controllers.