ajusteaz

Ajusteaz is a software framework and ecosystem designed to assist practitioners in tuning and calibrating complex systems. The name suggests adjustment and optimization, reflecting its focus on automated parameter changes driven by data. The core concept is automated parameter adjustment: given a model and observed data, ajusteaz proposes changes to improve performance while respecting safety and feasibility constraints.

The platform provides a modular, plugin-based architecture that supports a variety of data sources, control interfaces, and simulation environments. It includes adapters for common communication protocols such as OPC UA and MQTT, a Bayesian optimization backbone for efficient search over parameter spaces, and tools for offline and online experimentation. Users can run parameter sweeps in simulation, then deploy validated configurations to real systems, with support for versioning and rollback.

Ajusteaz is applicable across domains where calibration, drift correction, or adaptive tuning are valuable. Typical use cases include industrial process control, robotics calibration, energy management, and instrumentation where system behavior evolves over time. The framework emphasizes reproducibility and observability, offering experiment planning tools, monitoring dashboards, and detailed parameter histories to support auditing and knowledge transfer.

In practice, ajusteaz aims to lower the barrier to adaptive tuning by providing accessible workflows for data collection, model fitting, and safe parameter updates. It also invites community involvement to expand adapters, improve algorithms, and share best practices. As with any automated optimization tool, its adoption benefits from governance, safety controls, and transparent explainability.

See also: parameter estimation, automatic control, Bayesian optimization, model predictive control, calibration.