To address the global path planning challenge for Unmanned Surface Vehicles in complex maritime environments characterized by dense islands and narrow waterways, this paper proposes a Hybrid Multi-Strategy Adaptive RRT* algorithm. The method combines a dynamic region-based sampling strategy with an improved artificial potential field based dynamic extension strategy, which introduces random-node attraction, dynamic repulsion adjustments, and additional repulsive forces. Additionally, a hierarchical side-retreat escape mechanism is applied to enhance obstacle avoidance and search efficiency in complex environments. The algorithm also incorporates heading-angle constraints and adaptive step-size adjustment to ensure the path complies with USV kinematic properties. Furthermore, an improved NSGA-II algorithm is proposed to perform multi-objective optimization of path length, smoothness, and safety, and B-spline interpolation is used to generate continuous and executable paths. Simulation results show that, compared with the standard RRT* algorithm, the proposed HMA-RRT* algorithm achieves average reductions of 7.85% in path length, 66.96% in node count, 48.73% in computation time, and 25.7% in mean turning angle across four representative complex maritime environments. These improvements significantly enhance search efficiency, path smoothness, and planning feasibility, thereby providing a reliable and efficient path-planning solution for autonomous USV navigation in complex maritime conditions. Graphical

This paper investigates state estimation for linear time-invariant systems where sensors and controllers are geographically separated and connected via a bandwidth-limited and errorless communication channel with the fixed data rate. All plant states are quantised, coded and converted together into a codeword in our quantisation and coding scheme. We present necessary and sufficient conditions on the fixed data rate for observability of such systems, and further develop the data-rate theorem. It is shown in our results that there exists a quantisation and coding scheme to ensure observability of the system if the fixed data rate is larger than the lower bound given, which is less conservative than the one in the literature. Furthermore, we also examine the role that the disturbances have on the state estimation problem in the case with data-rate limitations. Illustrative examples are given to demonstrate the effectiveness of the proposed method.

This paper investigates the observability and stabilisability problem for linear time-invariant systems, where sensors and controllers are geographically separated and connected by a stationary memoryless digital communication channel. The limited information of the plant state is transmitted over such a channel to the controller. We focus on explaining the effect imposed by limited data rates. A new quantisation, coding and control scheme is presented to minimise the required data rate for observability and stabilisability. Different from prior research, our study shows that, the required data rate is determined by the state prediction error. Namely, the smaller prediction error requires the smaller codeword length, which leads to the smaller data rate. It is shown that, there exists a lower bound on the average data rate above which the system is observable and stabilisable. Illustrative examples are given to demonstrate the effectiveness of the proposed quantisation, coding and control scheme.

This paper addresses linear quadratic Gaussian (LQG) control problems for multi‐input multioutput (MIMO), linear time‐invariant (LTI) systems, where the sensors and controllers are geographically separated and connected via a digital communication channel with limited data rates. An observer‐based, quantized state feedback control scheme is employed in order to achieve the minimum data rate for mean square stabilization of the unstable plant. An explicit expression is presented to state the tradeoff between the LQ cost and the data rate. Sufficient conditions on the data rate for mean square stabilization are derived. An illustrative example is given to demonstrate the effectiveness of the proposed scheme.

Accurate prediction of thermophysical properties of hybrid nanofluids (HNFs) is a major challenge in thermodynamic applications. This research provides a computational framework to solve the complexity of heat transfer and flow physics in Al ₂ O ₃ –Cu/water HNFs. A reconstruction of the statistical microstructure of the fluid is constructed by the Karhunen–Loève expansion. The physical processes are modeled with a system of partial differential equations that model the interactions between the base fluid and dispersed nanoparticles. In the context of flow variables and entropy generation with sharp gradients, a Bayesian inference extension of the physics-informed neural network (PINN) is developed to improve prediction accuracy. Bayesian inference PINN (BIPINN) is the extension that infers posterior distributions of hidden thermal flow states, conditioned on prior physical laws. The framework is validated through many physical problems such as convective flow, thermal conduction, and fluctuation control. BIPINN shows improved accuracy over the finite element method while also showing improved generalization under sparse data while illustrating robustness of the framework while modeling complex thermal-fluid systems.

This article addresses the output feedback stability problem for single-input single-output (SISO) linear systems with quantised measurements of the plant output, where sensors and controllers are connected via errorless digital channels carrying a finite number of bits per unit time. The main idea here is to present a lower bound of data rates, above which there exists a quantisation, coding and control scheme to guarantee both stability and a prescribed control performance of the unstable plant. A quantisation and coding scheme, which is based on the distribution of measurements and the dynamics of the plant, is proposed. The proof techniques rely on both information-theoretic and control-theoretic tools. An illustrative example is given to demonstrate the effectiveness of the proposed scheme.