This paper presents a preview-enhanced synchronous policy-iteration framework for zero-sum differential games in continuous-time Lipschitz nonlinear systems with differentiable bounded disturbances. By embedding finite-horizon reference preview into an augmented error system, the tracking problem is reformulated as a regulation-oriented zero-sum game. A synchronous actor–critic–disturbance learning scheme is then developed to approximate the value function and the control/disturbance policies without solving the Hamilton–Jacobi–Isaacs equation. The preview signal acts as a known bounded forcing term, enabling anticipative tracking behavior. A Lyapunov-based analysis establishes uniform ultimate boundedness under local approximation, bounded preview forcing, and persistence of excitation. Simulations on a flexible-link robot show earlier tracking response, improved transient performance, bounded augmented dynamics, and stable neural-network weight adaptation compared with the non-preview case.
Intel SGX vs. Ephemeral Integrity Substrate We compare two approaches to achieving provably secure, decentralized time synchronization in networks of ephemerally identified nodes, such as open swarms. The first combines Intel SGX enclaves with Byzantine Fault Tolerant (BFT) conse…
This paper presents an enhanced control strategy for improving power quality and system stability in a low-voltage grid-connected solar photovoltaic (PV) and battery energy storage system. In the proposed system, a Dynamic Voltage Restorer (DVR) is integrated at the grid side to…
This study presents a nonlinear stabilization framework for industrial robotic systems based on differential topology and geometric control theory. System dynamics are modeled on smooth manifolds, where stabilization is achieved through topological invariants and non-smooth feedb…
This paper presents a Cloud-in-the-Loop (CIL) architecture that integrates cloud computing services with real-time simulation and Hardware-in-the-Loop (HIL) technologies to support the development, testing and validation of advanced energy management solutions for microgrids and…
Modern autonomous hardware is trapped between two flawed computational paradigms: power-hungry, data-dependent Deep Learning (AI) networks that lack physical predictability, and rigid Classical Control loops (Calculus) that fail when encountering unmodeled environmental dynamics.…
The manufacturing industry is undergoing a significant transformation driven by rapid advancements in digital technologies, automation, artificial intelligence, and interconnected production systems. Traditional manufacturing methods, which primarily depend on manual operations a…