Adaptive Fuzzy Active-Disturbance Rejection Control-Based Reconfiguration Controller Design for Aircraft Anti-Skid Braking System

The aircraft anti-skid braking system (AABS) is an essential aero electromechanical system to ensure safe take-off, landing, and taxiing of aircraft. In addition to the strong nonlinearity, strong coupling, and time-varying parameters in aircraft dynamics, the faults of actuators, sensors, and other components can also seriously affect the safety and reliability of AABS. In this paper, a reconfiguration controller-based adaptive fuzzy active-disturbance rejection control (AFADRC) is proposed for AABS to meet increased performance demands in fault-perturbed conditions as well as those concerning reliability and safety requirements. The developed controller takes component faults, external disturbance, and measurement noise as the total perturbations, which are estimated by an adaptive extended state observer (AESO). The nonlinear state error feedback (NLSEF) combined with fuzzy logic can compensate for the adverse effects and ensure that the faulty AABS maintains acceptable performance. Numerical simulations are carried out in different runway environments. The results validate the robustness and reconfiguration control capability of the proposed method, which improves AABS safety as well as braking efficiency.

Dynamic-SoS: An Approach for the Simulation of Systems-of-Systems Dynamic Architectures

Systems-of-Systems (SoS) combine heterogeneous, independent systems to offer complex functionalities for highly dynamic smart applications. Besides their dynamic architecture with continuous changes at runtime, SoS should be reliable and work without interrupting their operation and with no failures that could cause accidents or losses. SoS architectural design should facilitate the prediction of the impact of architectural changes and potential failures due to SoS behavior. However, existing approaches do not support such evaluation. Hence, these systems have been usually built without a proper evaluation of their architecture. This article presents Dynamic-SoS, an approach to predict/anticipate at design time the SoS architectural behavior at runtime to evaluate whether the SoS can sustain their operation. The main contributions of this approach comprise: (i) characterization of the dynamic architecture changes via a set of well-defined operators; (ii) a strategy to automatically include a reconfiguration controller for SoS simulation; and (iii) a means to evaluate architectural configurations that an SoS could assume at runtime, assessing their impact on the viability of the SoS operation. Results of our case study reveal Dynamic-SoS is a promising approach that could contribute to the quality of SoS by enabling prior assessment of its dynamic architecture.

How to Efficiently Reconfigure Tunable Lookup Tables for Dynamic Circuit Specialization

Dynamic Circuit Specialization is used to optimize the implementation of a parameterized application on an FPGA. Instead of implementing the parameters as regular inputs, in the DCS approach these inputs are implemented as constants. When the parameter values change, the design is reoptimized for the new constant values by reconfiguring the FPGA. This allows faster and more resource-efficient implementation but investigations have shown that reconfiguration time is the major limitation for DCS implementation on Xilinx FPGAs. The limitation arises from the use of inefficient reconfiguration methods in conventional DCS implementation. To address this issue, we propose different approaches to reduce the reconfiguration time drastically and improve the reconfiguration speed. In this context, this paper presents the use of custom reconfiguration controllers and custom reconfiguration software drivers, along with placement constraints to shorten the reconfiguration time. Our results show an improvement in the reconfiguration speed by at least a factor 14 by using Xilinx reconfiguration controller along with placement constraints. However, the improvement can go up to a factor 40 with the combination of a custom reconfiguration controller, custom software drivers, and placement constraints. We also observe depreciation in the system’s performance by at least 6% due to placement constraints.