系统与控制
Resilient-by-design smart grid control demands frameworks capable of maintaining stability under physical disturbances and communication failures, without reliance on centralized coordination. While Centralized Training Decentralized…
Learning continuous-time representations of dynamical systems from observation data has emerged as a cornerstone of data-driven control and scientific machine learning. However, existing neural differential equations either treat external…
For nonlinear control systems on normed vector spaces, we characterize an incremental input-to-state stability (ISS) type property in which the overshoot constant multiplies both the initial-condition and the input terms. Working through…
This paper develops a computationally efficient framework for reachability analysis of transmission-level power system dynamics with synchronous generators, grid-forming and grid-following inverters, and uncertain power…
Modern power systems are increasingly dominated by Inverter-Based Resources (IBR), most of which work in Grid-following (GFL) mode. This implies that they do not directly control their terminal voltage, so the static voltage stability at…
Advanced control methods have proven effective for controlling cavity pressure, a key determinant of part-quality attributes, in the plastics injection molding process. However, the abstract nature of the resulting control laws makes them…
Benders decomposition solves optimization problems by separating the first-stage master problem from one or more second-stage sub-problems. While the standard Benders decomposition solves all sub-problems in each iteration, solving only…
Capacity expansion is a key tool for planning future energy systems. However, weather-dependent generation and long-duration storage result in problem sizes that exceed the computational limits of conventional interior-point solvers, making…
A method for analysing the stability of dynamical systems is proposed, based on the introduction of a weighted phase volume and time rescaling by a positive function. The advantage of the method is the ability to set the contraction…
This paper addresses the problem of designing recommendation systems for social networks and e-commerce platforms from a control-theoretic perspective. We formulate recommendation design as an infinite-horizon state-feedback optimal control…
The problem of simultaneous placement of distributed generators and DSTATCOMs in radial distribution networks (RDNs) is a combinatorial mixed-integer optimization problem whose scalability with growing decision dimensionality has been…
This study proposes a precipitation control framework integrating a realistic Numerical Weather Prediction (NWP) model with model predictive control (MPC). At each control instant in MPC, a finite-difference sensitivity matrix is…
Home Energy Management Systems (HEMS) can reduce residential electricity costs and support demand response, but adoption is limited by the difficulty of translating household preferences into technical scheduling constraints. This paper…
Water electrolysis plants, hyperscale data centers, and aluminum potlines represent gigawatts of demand-side flexibility for bulk power system balancing, operational planning, and procurement services. Such loads are scheduled through…
Hyperscale data centers and other large concentrated loads can impose substantial new demand on existing transmission networks. If import corridors lack sufficient transfer capability, operators may need to curtail load, delay…
The modulating function method is an algebraic framework that, thus far, has been used for state and parameter estimation, as well as fault detection, of linear, fractional-order, distributed, and some nonlinear systems. At the core of the…
Power-constrained 25kV AC railway sections, particularly under degraded feeding, are protected today by blunt, section-wide power limits that penalise every train irrespective of whether it contributes to the binding condition. This paper…
Explainable AI (XAI) is important for deploying machine learning systems in domains where stakes are very high and where transparency, trust and accountability are critical. Although black box models like deep neural networks often perform…
Volterra series feedback linearizes a class of nonlinear hyperbolic PDEs but produces a controller that, even after truncation, demands solving a tower of plant-specific kernel PDEs and evaluating nested integrals. We prove the truncated…
Backstepping for nonlinear PDEs yields exact feedback linearizing laws in the form of infinite Volterra series -- elegant in theory, but with challenges for implementation. This paper shows that even very low-order truncations of such…