Safe and Trustful AI for Closed-Loop Control Systems

Abstract

In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining these opportunities and directions makes it evident that employing AI in developing and implementing CLCSs is indeed feasible. Integrating AI into CLCS development or AI directly within CLCSs can lead to a significant improvement in stakeholder confidence. Integrating AI in CLCSs raises the question: How can AI in CLCSs be trusted so that its promising capabilities can be used safely? One does not trust AI in CLCSs due to its unknowable nature caused by its extensive set of parameters that defy complete testing. Consequently, developers working on AI-based CLCSs must be able to rate the impact of the trainable parameters on the system accurately. By following this path, this paper highlights two key aspects as essential research directions towards safe AI-based CLCSs: (I) the identification and elimination of unproductive layers in artificial neural networks (ANNs) for reducing the number of trainable parameters without influencing the overall outcome, and (II) the utilization of the solution space of an ANN to define the safety-critical scenarios of an AI-based CLCS.

Simulation of AI-Based PID Controllers on DC Machines Using the MATLAB Application

“…AI can also manage uncertainty since it can learn from data and adjust to changes. It can help save wear and maintenance costs by lowering chatter in the control signal through optimization [25]. Artificial intelligence (AI) may improve PID controller performance, increasing their adaptability and effectiveness for a greater variety of systems.…”

Design and Assurance of Safety-Critical Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control System

“…In the Internet of Things (IoT) era, safety-critical systems have also been growingly more distributed and based on embedded modules [13][14][15], such as those used in the control functions of Unmanned Ground Vehicles (UGVs) [16][17][18][19][20][21][22][23][24], Unmanned Aerial Vehicles (UAVs) [25][26][27], and in a new generation of distributed Communication-Based Train Control (CBTC) systems for metro and railway signaling [28,29]. Furthermore, the increased flexibility and processing capability provided by recent Field-Programmable Gate Arrays (FPGAs) and other Programmable Logic Devices (PLDs) allows these elements to play an even more important role in cutting-edge safety-critical systems than today [30,31] while keeping design costs reasonable for achieving the needed efficiency and performance levels [32,33].…”