WHAT WE DO

ISC is at the forefront of researching and developing effective solutions to enhance controller capabilities by integrating cutting-edge Artificial Intelligence (AI) technology, in particular data-driven Machine Learning (ML) tools, and advanced control theory paradigm.

Data-Driven AI: Machine Learning

Data-driven AI techniques, particularly machine learning (ML), are increasingly sought after for their ability to automate the management of diverse data sources and uncover hidden correlations within the data. These capabilities empower technology adopters to extract valuable insights from the vast amounts of data they accumulate. Machine learning techniques are powerful because they can automatically learn from data without requiring any prior knowledge of the underlying physical system.

Data-Driven Machine Learning

Techniques

• Meta-heuristic optimization - These techniques provide a nonlinear optimization framework permitting to solve complex optimization problems by limiting the magnitude of the sub-optimality provided due to effects of local minima.
• Fuzzy Logic - It is a method to identify the unknown parameters of a highly complex and nonlinear system, such as a EV battery. It does not require a mathematical model but only uses the input data and the fuzzy rule base.
• Neural Networks (NN) - Inspired by the human brain, NN is a framework of many different machine learning algorithms to perform different tasks. The NN has self-adaptability and learning abilities to establish a highly complicated and non-linear system, such as a battery.
• Support Vector Machine (SVM) - SVM techniques have attracted considerable attention in recent years and have become a powerful tool for solving regression problems in nonlinear systems through different kernel functions and regression algorithms to transmute a nonlinear model into a linear model.

AI for Control

Because of its flexibility, modern ML based AI solutions can be used to overcome the limitations of modern control theory, enhancing the performance of control algorithms across different fields, including Robotic Process Automation, Decision Management, Adaptive Manufacturing.

Control-aimed ML solutions can be classified in two main categories, off-line and on-line ML-based control solutions:

• Off-line solution - Typical solutions consider AI-based automatic calibration systems, data-generators for Rapid Prototyping (RP) test and algorithms for off-line estimation of unmeasurable signals/parameters.
• On-line solution - the set of applications involving the use of AI algorithm for working in conjunction with controllers/observers developed according to standard control theory for defining a closed loop AI-based control system.