This paper proposes a novel state/fault estimation observer for uncertain polynomial fuzzy systems (PFSs) with unmeasurable premise variables (PVs), where the uncertainties in PVs and membership functions (MFs) are modeled by interval type-2 (IT2) fuzzy systems. To handle the effect of unmeasurable PVs caused by inherent uncertainties and sensor faults (SFs), compensation vectors are employed, thereby eliminating the need for restrictive assumptions on system stability or the linear growth conditions of these effects. Furthermore, taking the singularity problem in the estimation of actuator fault (AF) into consideration, a fully mismatched observer is constructed and eliminates the need for special structure of the traditional methods while enhancing the design flexibility simultaneously. In the stability analysis process, a one-step method is proposed to simplify the computational complexity compared to path-following techniques while introducing a trade-off in conservativeness, and a membership-function-dependent (MFD) theorem is given to reduce the conservativeness. Finally, a single-link rigid robot arm is introduced as an simulation example to verify the effectiveness of the proposed method.

This paper addresses attitude tracking and vibration suppression problems during the flexible spacecraft maneuvers under the influence of system parametric uncertainties, external disturbances, and actuator nonlinear saturation without utilizing intelligent materials or smart vibration suppression actuators. Specifically, we develop a dual observer-based backstepping (DOB-BST) controller that employs a recursive control design to devise observers to estimate and attenuate the effect of perturbations in spacecraft attitude kinematics and dynamics. Moreover, the proposed DOB-BST controller uses an input compensator to mitigate the adverse impacts of saturation nonlinearity in a closed-loop system. The proposed control structure offers finite time convergence of closed-loop system states to a small neighborhood of zero. In addition, we give system state convergence regions and derive explicit tuning conditions to downsize the convergence region using Lyapunov stability analysis. Finally, the simulation experiment results confirm the performance of the proposed control algorithm despite the multiple system constraints.

We present a continuous attitude-tracking control strategy for energy-efficient real-time torque selection in autonomous aerial systems (AAS). The conventional unit-quaternion-based controllers may induce unwanted extra rotations due to the double-cover property of quaternions, which makes them inefficient in terms of time and energy. This paper presents an energy-efficient framework for attitude-tracking control of AAS in the presence of external disturbances and parameter uncertainties. We introduce a scalar error function to construct an associated error vector that regulates attitude convergence. The error vector serves as the basis for controller design when both the unit-quaternion equilibria are stable. The proposed energy-efficient framework-based controller inherently avoids unwanted rotations during attitude maneuvers and ensures almost global orientation tracking. Compared to conventional unit-quaternion-based controllers, the proposed energy-efficient framework is 71% more efficient in terms of energy. The simulation results show satisfactory performance of the proposed energy-efficient framework.

Maritime rescue mission presents a substantial operational challenge due to the unpredictable and challenging environment, inaccessibility, and scarcity of skilled manpower. This paper proposes a distributed task allocation method for the autonomous deployment of a multi-fixed-wing UAV (FW-UAV) system for rapid resource distribution in the Deep Sea. A dynamic distributed collaborative task allocation (DCTA) algorithm is developed using a distributed adaptive genetic algorithm (DAGA) for optimal resource assignment. The proposed DAGA-based DCTA framework facilitates cooperative decision-making and decentralized task allocation, confirming UAV agents reach a consensus on synchronized rescue execution with rapid arrival and optimal spatial task distribution. We conduct simulation experiments to demonstrate the robust and effective performance of the proposed DAGA-based DCTA method in terms of mission success rates, computational efficiency, and uniform resource allocation for the time-critical rescue of moving maritime distressed vessels (MMDVs).

Accurate, reliable, and fast trajectory tracking in the presence of system uncertainties and external disturbances that vary with time is essential for robotic manipulator operating in adverse environments. Unmodeled dynamics and perturbations can severely degrade closed-loop performance and interfere with task execution. This paper introduces a predefined-time trajectory tracking control strategy for robotic manipulators with time-varying parameter uncertainties and unknown external perturbations. The objective is to design a robust controller that performs tracking tasks in user-defined time frames. First, a nonlinear sliding manifold is constructed, such that the system states reach the sliding mode surface within a predefined time, regardless of initial conditions. Then, a robust controller is developed to ensure the convergence of the system state errors to a small neighborhood around the origin within a user-specified time window. The proposed control framework guarantees fast and precise convergence. Numerical simulations performed on a robotic manipulator are presented to demonstrate the effectiveness and robustness of the proposed control strategy in terms of tracking accuracy, convergence rate, and disturbance rejection capability.

This paper presents an active fault-tolerant control (FTC) framework for aerial vehicles operating under system uncertainties, external disturbances, and actuator faults or failures. Specifically, we propose a nonlinear integral sliding mode-based fault-tolerant control allocation scheme to address actuator faults and failures in an octocopter system. The goal is to perform trajectory tracking control and ensure system stability under practical operating conditions while preserving nominal closed-loop performance during fault scenarios. A baseline controller is first designed using a backstepping approach and combined with a nonlinear integral sliding mode control law to achieve nominal stability. To manage actuator degradation, a control allocation strategy is developed to redistribute control efforts among healthy redundant actuators based on real-time estimates of actuator effectiveness. Key features of the proposed FTC framework include its ability to handle faults and failures online while maintaining robustness against system perturbations. The simulation results demonstrate the effectiveness of the proposed BT-ISMC-based FTC method in sustaining performance under fault and failure conditions.

Traditional charge-discharge methods for supercapacitors frequently lead to issues such as overcharging, overheating, and reduced efficiency, which negatively affect the performance and lifespan of supercapacitors. To address these problems, this paper proposes a design scheme of a smart charge-discharge control system for supercapacitors, which integrates the traditional constant current method, the constant voltage method, and the PID control algorithm, along with a high-efficiency boost discharge circuit. In the implementation, the STM32 microcontroller with the LM5106 chip, LTC1871 chip, and INA226 chip is employed to achieve intelligent charging, boost discharging, and real-time monitoring. Finally, experiments are carried out, and the results indicate that the proposed control system significantly improves the charge-discharge efficiency and optimizes energy utilization.