Frank Allgöwer

Dear Editor, This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss. Firstly, we construct a temporal nonzero-sum game over predictive control input sequences, deriving multiple optimal predictive control input sequences from its solution. To obtain the Nash equilibrium solution of the temporal nonzero-sum game, we solve the problem through policy iteration of reinforcement learning. Then, we train the actor neural network and critic neural network to estimate the control policy and action value function, respectively, using the collected offline and online input-state data. Compared to traditional predictive control methods, the proposed framework does not rely on an explicit model and obtains a data-driven controller design. Finally, the effectiveness of the proposed algorithm is validated through a numerical example.

Encrypted controllers using homomorphic encryption have proven to guarantee the privacy of measurement and control signals, as well as system and controller parameters, while regulating the system as intended. However, encrypting dynamic controllers has remained a challenge due to growing noise and overflow issues in the encoding. In this paper, we review recent approaches to dynamic encrypted control, such as bootstrapping, periodic resets of the controller state, integer reformulations, and FIR controllers, and equip them with a stability and performance analysis to evaluate their suitability. We complement the analysis with a numerical performance comparison on a benchmark system.

The increasing automation of modern systems—across industry, healthcare, mobility, and beyond—has raised the demand for human reasoning and expertise, while alleviating the burden of repetitive tasks. This transformation is driving us toward Automation 5.0, a new paradigm aimed at unleashing human potential. Recently, the development of foundation models (FMs) has reinvigorated its realization, making it both urgent and critical to explore the concept of Automation 5.0 in this new era. In this article, we define Automation 5.0, discuss its significance, and emphasize its new world, thinking, and technology with the goal of achieving knowledge automation. A framework, based on business FMs, human-oriented operating systems, and scenarios engineering, is proposed, where biological, robotic, and digital humans work together in three modes: autonomous, parallel, and expert/emergency modes. Additionally, a diverse range of its scenarios and applications are summarized and discussed, such as Manufacturing 5.0, Healthcare 5.0, and Transportation 5.0. We believe that Automation 5.0 can drive the co-evolution of productivity and production relations across all domains, propelling society toward a “Safety, Security, Sustainability, Sensitivity, Service, Smartness (6S)” future.

Model predictive control (MPC) is a powerful control method that allows to directly include state and input constraints into the controller design. However, errors in the model, e.g., caused by unknown disturbances, can lead to constraint violation, loss of feasibility and deteriorate closed-loop performance. In this paper, we propose a new MPC scheme based on the internal model principle. This enables the MPC to reject unknown disturbances provided that the dynamics of the linear signal generator are known. We reformulate the output regulation problem as a stability problem, to ensure feasibility, constraint satisfaction, and convergence to the optimal reachable setpoint. The controller is validated on a real fourtank system.

This article presents a novel framework for the robust controller synthesis problem in discrete‐time systems using dynamic Integral Quadratic Constraints (IQCs). We present an algorithm to minimize closed‐loop performance measures such as the ‐norm, the energy‐to‐peak gain, the peak‐to‐peak gain, or a multiobjective mix thereof. While IQCs provide a powerful tool for modeling structured uncertainties and nonlinearities, existing synthesis methods are limited to the ‐norm, continuous‐time systems, or special system structures. By minimizing the energy‐to‐peak and peak‐to‐peak gain, the proposed synthesis can be utilized to bound the peak of the output, which is crucial in many applications requiring robust constraint satisfaction, input‐to‐state stability, reachability analysis, or other pointwise‐in‐time bounds. Numerical examples demonstrate the robustness and performance of the controllers synthesized with the proposed algorithm.