implement controllers

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 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 challenge that traditional PID tuning relies heavily on model identification and fails to capture the empirical expertise of engineers who iteratively adjust parameters based on observed system responses. To bridge this gap, the authors propose a novel tuning framework that integrates control-domain knowledge with the reasoning capabilities of large and small language models. The approach formalizes the engineer’s tuning process into an executable task by leveraging closed-loop response characteristics, diagnostic cues, tuning preferences, and IMC-based examples to guide parameter generation and refinement. Physical constraints and reinforcement learning are incorporated to enhance performance, with the method employing supervised fine-tuning (SFT) and a physics-informed group relative policy optimization (PI-GRPO). Evaluated on 200 FOPDT/SOPDT processes, the cloud-based large model achieves a success rate of 75–89%, while a locally deployed Qwen3-0.6B model, after optimization, attains a first-recommendation success rate of 94.0%.

This work addresses the impact of initial system state on convergence and the exploration–exploitation trade-off in automatic PID controller tuning. It presents the first empirical study conducted on real mobile robots—both omnidirectional and differential-drive platforms. We propose a synergistic parameter-tuning framework integrating Bayesian optimization with differential evolution, systematically quantifying how initial state and exploration intensity affect convergence rate, overshoot, and dynamic performance. Results demonstrate that the initial system state significantly influences tuning efficiency and closed-loop stability; moreover, exploration intensity exhibits strong coupling with the selection of initial sampling points—joint optimization of both factors enhances robustness and convergence reliability. The study establishes a reproducible evaluation paradigm, providing critical empirical evidence and methodological support for adaptive PID tuning in industrial applications. (149 words)

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 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.

Traditional controllers struggle to generalize across systems with varying orders and dynamic characteristics. This work proposes a universal learning-based controller that constructs a dynamic state-space representation using a masked attention mechanism, integrating system label encoding, multi-scale temporal processing, and a mixture-of-experts architecture to enable a single neural network to uniformly control diverse linear and nonlinear systems. Notably, the approach is the first to adapt—without architectural modifications—to challenging dynamics such as unstable and non-minimum-phase systems, while supporting zero-shot generalization to unseen operating conditions. Trained on 25 system classes and 314,630 trajectories, the controller matches the performance of specialized LQI controllers and maintains robustness under previously unobserved conditions, including actuator saturation, noise, and disturbances.