Magnetic resonance imaging (MRI)-guided focused ultrasound (MRg-FUS) is an effectivenoninvasiveintervention. However, when extending to mild hyperthermia treatment (mHT), accurate temperature control with uniform thermal distribution remains challenging for deep-seated targets in highly heterogeneous tissues. To this end, we propose a novel thermal modeling andclosed-loopcontrol scheme for robot-assisted MRg-FUS mHT. A discrete-time positive system is introduced for thermal dynamics modeling. By introducing an event-triggered model predictive controller, objective functions are formulated to optimize transducerphasesequences. Thermal feedback is leveraged to accommodate modeling uncertainties,closingthe control loop to generate constructive ultrasound interference. Our scheme enables simultaneous spatial and temporal heating control, maintaining target temperature within a narrow range ($41\,^\circ \mathrm{C}\text{--}43\,^\circ \mathrm{C}$). The thermal dose evaluated under unknown disturbances demonstrates the robustness of the proposed scheme against severe tissue heterogeneity. Overheating can be avoided, enhancing the potential for safe intervention. Results from mechanical transducer adjustment further support the feasibility of treating large target areas in robot-assisted mHT.

Distributed power control is crucial for ensuring reliable communication and minimizing energy consumption in wireless communication networks, particularly under random switching topologies and uncertain network dynamics. Existing methods primarily rely on traditional norms or control paradigms, often facing computational challenges due to inherent nonconvexities. In this paper, we propose a novel distributed optimization framework for wireless networks using DC programming to minimize the L1/L∞-Hankel norm of the system. Unlike conventional approaches that may yield suboptimal solutions or suffer from computational inefficiency, the DC programming approach systematically decomposes the nonconvex Hankel norm minimization problem into tractable convex subproblems, ensuring rapid convergence and globally optimal or near-optimal solutions. Our method specifically addresses dynamic topologies modeled by random switching and parameter uncertainties, significantly improving robustness and interference management. Simulation results demonstrate that the proposed framework effectively maintains the desired SINR levels and accelerates convergence of global solution.

This article focuses on the issues of security sampled-data-based$H_{\infty }$control design for interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems against false-data injection (FDI) attacks and external disturbances using a supervision of machine learning (SML) algorithm. To improve the bounds of sampled data period, an SML algorithm is proposed to optimize the allocation of network resources and ensure the effective utilization of bandwidth. Besides, two improved integral inequalities are introduced to estimate the integral quadratic terms that account for signal transmission delays and sampling information. Meanwhile, an appropriate asymmetric looped Lyapunov-Krasovskii functional (ALLKF) is constructed incorporating information on the fuzzy membership function (FMF), aperiodic sampling pattern, and transmission delays to facilitate model conservativeness. Next, based on the constructed fuzzy ALLKF along with the proposed inequalities, a set of necessary conditions for achieving asymptotic stability with$H_{\infty }$performance is derived through linear matrix inequalities (LMIs), and then control parameters can be obtained to guarantee the less conservative stabilization results of considered closed-loop systems. Finally, to verify the superiority and feasibility of the proposed theoretical observations, the hacked permanent magnet synchronous generator (PMSG)-based wind energy system (WES) is numerically validated. Additionally, comparative examples are presented to demonstrate the improved conservativeness of the proposed technique.

This article introduces an innovative approach to tackle the consensus issue for positive multiagent systems (MASs) with input nonlinearities. By analyzing a differential equation model of MASs with sector-bounded nonlinearity, positivity conditions are derived. Furthermore, a distributed state-feedback control protocol is applied to achieve consensus and simultaneously to preserve positivity. An iterative algorithm is used to compute a consensus controller gain. The saturation nonlinearity and its approximated form are also considered. Finally, the effectiveness of proposed method is verified through numerical examples. The findings of this study can potentially contribute to developing control strategies for MASs with nonlinear input dynamics.

Soft robots have great potential in surgical applications due to their compliance and adaptability to their environment. However, their flexibility and nonlinearity bring challenges for precise modeling, sensing, and control, especially in constrained cavities. In this article, a simple, compact two-segment soft robot for flexible laser ablation is proposed. The proximal hydraulic-driven segment can offer omnidirectional bending so as to navigate toward lesions. The distal segment driven by tendons enables precise, fast steering of laser collimator for laser sweeping on lesion targets. The dynamics of such mechanical steering motion can be enhanced with a metal spring backbone integrated along the collimator, thus facilitating the control with certain linearity and responsiveness. A soft robot modeling and control scheme based on Koopman operators is proposed. We also design a disturbance observer so as to incorporate the controller feedback with real-time fiber optic shape sensing. Experimental validation is conducted on simulated orex-vivolaser ablation tasks, thus evaluating our control strategies in laser path following across various contours/patterns. As a result, such a simple compact laser manipulation can perform up to 6 Hz sweeping with precision of path following errors below 1 mm. Such modeling and control scheme could also be used on an endoscopic laser ablation robot with unsymmetric mechanism driven by two tendons.

This article investigates the nonnegative consensus tracking problem for networked systems with a distributed static output-feedback (SOF) control protocol. The distributed SOF controller design for networked systems presents a more challenging issue compared with the distributed state-feedback controller design. The agents are described by multi-input multi-output (MIMO) positive dynamic systems which may contain uncertain parameters, and the interconnection among the followers is modeled using an undirected connected communication graph. By employing positive systems theory, a series of necessary and sufficient conditions governing the consensus of the nominal, as well as uncertain, networked positive systems, is developed. Semidefinite programming consensus design approaches are proposed for the convergence rate optimization of MIMO agents. In addition, by exploiting the positivity characteristic of the systems, a linear-programming-based design approach is also proposed for the convergence rate optimization of single-input multi-output (SIMO) agents. The proposed approaches and the corresponding theoretical results are validated by case studies.