Abstract While artificial intelligence (AI) has been promising for fusion control, its inherent black-box nature will make compliant implementation in regulatory environments a challenge. This study implements and validates a real-time AI-enabled linear and interpretable control system for successful divertor detachment control with the DIII-D lower divertor camera. Using D2 gas, we demonstrate successful feedback divertor detachment control with a mean absolute difference of 2% from the target for both detachment and reattachment. This automatic training and linear processing framework can be extended to any image-based diagnostic for future fusion reactors.

Control and computation for linear ensemble systems using moment reduction

Correction: A Note on State Feedback Control of Uncertain Linear Systems with Fuzzy Dynamics

Abstract Designing controllers directly from measurement data has attracted growing attention in recent years, as it avoids the need for accurate system modeling or explicit system identification. This paper focuses on recent advances in data-driven control for linear discrete-time systems with unknown system matrices. For noisy input-state data, an in-depth analysis is provided on several representative approaches, including data-driven control based on Willems et al.’s fundamental lemma, quadratic matrix inequalities, linear fractional transformations for combining prior knowledge with data, and integral quadratic constraints. For noisy input-output data, a concise review is presented on control methods based on quadratic matrix inequalities, along with key insights into their structure and implications. The paper concludes by outlining several challenging problems that merit further investigation in future research.

Integration admissibility for structural controllability in linear networked systems

Dynamic event-triggered formation control of linear multi-agent systems with multiplicative noises

Complex-valued mixture generalized modified Blake–Zisserman algorithm for widely linear active noise control system

Unifying Direct and Indirect Learning for Safe Control of Linear Systems

Positive state reachability for positive linear control systems in continuous time

Higher derivatives of the end-point map of a control-linear system via adapted coordinates

This paper presents a unified framework for controllability and minimum-energy control of linear fractional differential systems with Caputo derivative order γ∈(0,1) and fully time-varying state and control delays. An explicit mild solution representation is derived using the fractional fundamental matrix, and a new controllability Gramian is introduced. Using analytic properties of the matrix-valued Mittag-Leffler function, we prove a fractional Kalman-type theorem showing that bounded time-varying delays do not change the algebraic controllability structure determined by (F,G,K). The minimum-energy control problem is solved in closed form through Hilbert space methods. Efficient numerical strategies and several examples—including delayed viscoelastic, neural, and robotic models—demonstrate practical applicability and computational feasibility.

This paper discusses the exact controllability of linear and nonlinear impulsive Caputo fractional systems. The exact controllability of a linear impulsive system is studied using the concept of generators and functional analysis. In contrast, the controllability of a nonlinear system is discussed using nonlinear functional analysis. An example is provided in the paper to support the results.

The classical output control problem for a linear system with an input delay and constant known parameters is considered. The plant may be unstable, making most of the known methods ineffective or unconstructive. A new control algorithm based on the Luenberger observer and the Smith predictor is proposed, incorporating correction terms defined by simple expressions that eliminate the need for complex calculations. The resulting regulator has a linear structure; however, the correction term provides for a periodic reset of the corresponding regulator variable. It is analytically proven that a closed system of a plant with an input delay and a modified Smith predictor is globally exponentially stable. The resulting method for controlling systems with an input delay surpasses all analogues known to the authors in terms of simplicity of implementation and effective performance for unstable systems. In future works, this approach will be extended to nonlinear and parametrically uncertain systems with an input delay.

Linear control systems on a 4D solvable Lie group used to model primary visual cortex V1

This paper focuses on designing an event-triggered dynamic output feedback controller for discrete-time linear systems subject to actuator and sensor constraints as well as external disturbances. A dynamic event-triggered condition with two generalized weighting parameters is introduced to regulate sensor-to-controller communication. By integrating generalized sector conditions, Lyapunov analysis, and linearization techniques, sufficient conditions are derived in terms of linear matrix inequalities, ensuring bounded closed-loop trajectories, prescribed H∞ performance, and asymptotic stability in the disturbance-free case. Furthermore, optimization problems are formulated to maximize the event-triggering rate while preserving the desired system performance. Simulation results show that, compared to time-triggered control, the event-triggered control effectively reduces the communication frequency, thereby significantly conserving communication resources. Compared with existing results, this work presents the first event-triggered dynamic output feedback scheme for discrete-time linear systems with dual saturation constraints. The inclusion of generalized weighting parameters and the use of generalized sector conditions allow the design to be carried out within a flexible local framework with reduced conservatism.

Off-Policy Reinforcement Learning for $H_\infty$ Control of Linear Discrete-Time Systems With Network-Induced Dropouts

Information transfer and its control in linear discrete stochastic systems

Security control for networked control systems with deception attacks: A stochastic model predictive control approach.

Quasi-data-driven static output feedback control of linear systems with input and state delays

Electromechanical suspension has the advantages of rapid response, high efficiency, and strong integration. As the actuator for electromechanical suspension, rotary motors exhibit higher efficiency compared to linear motors. However, the motion conversion mechanisms are required to apply rotary motors in vehicle suspensions. The motion conversion mechanism of the connecting rod rocker arm electromechanical suspension reduces the shock during reversal and transmits smooth torque. However, the dynamic model of the connecting rod rocker arm electromechanical suspension is challenging to develop accurately due to the strong nonlinearity of the mechanism. This paper proposes a fundamental dynamic modeling approach for the connecting rod rocker arm electromechanical suspension based on kinematics and mechanics analysis. The complex-vector method and the dynamic-static analysis method are employed in the proposed modeling approach to analyze the kinematics and mechanics characteristics of the electromechanical suspension. Simulations and small-scaled prototype tests are conducted to verify the effectiveness of the proposed modeling approach. In addition, a linearization control system of permanent magnet synchronous motor (PMSM)-based connecting rod rocker arm electromechanical suspension is preliminarily investigated.