Faisal Alhwikem

Deep Evolutionary Software Design Using Adaptive Intelligence for Maintainability and Refactoring

Fixed-Time Stable Fault-Tolerant Control of Underactuated Hovercraft via Physics-Informed Neural Adaptation

This study addresses the trajectory tracking control problem for an underactuated hovercraft subject to additive bias and multiplicative loss-of-effectiveness thruster faults under environmental disturbances. In these systems, actuator degradation structurally breaks the differential flatness mapping, driving nominal controllers to generate control actions that induce severe actuator saturation and cause instability. To resolve this challenge, a hierarchical physics-informed neural adaptive control (PINAC) framework is proposed. First, a gated-recurrent-unit physics-informed neural observer (PINO) is designed to isolate thruster faults from exogenous hydrodynamic disturbances. Second, a constrained Safe-TD3 reinforcement learning agent functions as a supervisor, computing an online dilation factor to slow down the mission timeline, thereby reconfiguring the reference trajectory to accommodate degraded actuator boundaries. Third, a low-level non-singular terminal sliding mode (NTSM) controller is implemented as a tracking-guarantee layer. Unlike classical asymptotic schemes where convergence is only achieved as time approaches infinity, or finite-time controllers where the settling time depends on the initial state, the proposed PINAC framework guarantees practical fixed-time stability, ensuring that the settling-time bound is independent of initial conditions. Simulation results demonstrate that the designed controller prevents actuator saturation, provides smooth trajectory adjustment, and reduces tracking errors under severe composite faults.

A Mutation-Guided Safety Assurance Framework for Safety-Critical Cyber-Physical Systems

Safety-critical cyber-physical systems require strict verification methodologies explicitly taken to reference safety properties in their tests. Conventional methods of mutation testing simply lump all mutants together, regardless of their effects on product safety, resulting in ineffective resource allocation and insufficient understanding of the behaviors of crucial safety interest. The novel mutation-guided safety assurance framework presented by this paper is called MuGu and it combines mutation testing and formal safety property enforcement. It uses a hierarchical safety constraint analyzer that classifies mutants (temporally) according to their ability to break temporal safety specifications modeled in Signal Temporal Logic (STL). A graph attention network encodes program semantics and control-flow dependencies to forecast the probability of safety violations, enabling effective prioritization of safety-critical mutants. The proposed safety-conscious mutation delineators use vital areas of the code, such as sensor interfaces, actuator commands, and decision-making code in autonomous systems. Extensive testing on two publicly accessible benchmark sets, namely the Software-artifact Infrastructure Repository (SIR) and Defects4J, shows that MuGu achieves 96.3% safe property coverage and requires 71.8% fewer new tests than traditional methods. The scheme determines 2.4 times as many safety-violating methods as state-of-the-art methods and decreases wasted effort on equivalent mutants by 64.2%. The statistical analysis demonstrates overwhelming improvements across all primary metrics in 12 baseline comparisons p<0.001. MuGu provides a rational basis for applying safety-oriented next-generation mutation testing in autonomous systems.