Abstract: Aerial manipulation provides adaptable solutions for executing tasks in constrained environments, particularly in civil engineering and disaster response. This paper presents a UAV-based aerial manipulation system for the precise handling and transportation of long objects, such as pipes, in uncertain conditions. Compared to single- or dual-arm systems, which are difficult to scale and maintain, the proposed design features a modular two-finger gripper, enhancing scalability and reliability while reducing mechanical complexity. To address challenges such as positional drift, which can compromise mission success, the system employs a SO-BFBEL controller controller to enhance stability and precision and it is compared with DNN-MRFT-based PID and Fuzzy SMC controller. Experimental results demonstrate that the SO-BFBEL controller reduces the position tracking error up to 50% and compensates for wind disturbances and battery discharge fluctuations more effectively than conventional methods. Additionally, the SO-BFBEL controller can help to conserve battery life during manipulation phases which can boost the operational efficiency without incurring any additional costs.

Abstract: Compliant manipulators with variable stiffness actuation systems are crucial for safety in physical human-robot interactions, improving performance during unexpected collisions. However, their inherent compliance poses motion control challenges, especially with rapid stiffness changes and uncertain end-effector loads, adversely affecting controller stability and accuracy. To address uncertainties from stiffness variations and changing payloads, this paper proposes the Bidirectional Fuzzy Brain Emotional Learning controller—a fuzzy neural network with a unique bidirectional brain emotional learning algorithm for weight adaptation—that effectively handles varying stiffness and payload uncertainties. Additionally, a novel algorithm for systematic establishment of fuzzy layers is presented which significantly reduces the effort and time for implementation. Simulation and experimental results demonstrate the proposed controller’s superior adaptability and tracking performance under varying stiffness and payload uncertainties compared to the conventional PID controller. This new algorithm for fuzzy layer setup can serve as a default for all fuzzy neural network-based controllers, enhancing their ease of use across various applications. The controller can also be directly extended to grasping uncertain objects using variable stiffness actuated systems, improving safety and reliability in physical human-robot interactions.

Abstract: Nano helicopter unmanned air vehicles (NHUAVs) offer significant advantages due to their compact size, maneuverability, and cost-effectiveness, making them versatile and flexible platforms for various applications. Despite these merits, achieving full autonomy in NHUAV control presents challenges including multivariable, strongly coupled nonlinear systems, susceptibility to external disturbances, uncertainty in system dynamics, and altitude loss during sharp turns. This article introduces a novel robust adaptive bidirectional fuzzy brain emotional learning (BFBEL) control strategy to address these challenges and achieve stable pose control and highly accurate trajectory tracking for uncertain NHUAVs. The BFBEL controller integrates fuzzy inference, a neural network, and a bidirectional brain emotional learning (BBEL) mechanism, enabling rapid adaptation and precise flight control. Implemented on a customized 32g NHUAV platform, the proposed control algorithm demonstrates remarkable performance in the presence of wind disturbance and system uncertainty. The obtained results underscore the superiority of BFBEL, showcasing significant advancements over self-adaptive sliding surface-based Takagi–Sugeno fuzzy control and conventional PID control approaches in terms of robustness to disturbances and trajectory tracking precision.

 Abstract: A self-organizing bidirectional fuzzy brain emotional learning (SO-BFBEL) controller is developed to control a quadcopter UAV in an uncertain environment. The proposed SO-BFBEL controller improves the performance of the existing BFBEL controller by generating more accurate fuzzy layers in real-time and removes the need to depend on expert knowledge to set the fuzzy layers. The proposed SO-BFBEL controller is applied to control the position of a quadcopter UAV for accurate 3-D eight shape trajectory tracking for three different speed settings under extreme wind disturbances up to 5 m/s in real-time experimentation. Two industrial fans are used to create the wind disturbance for the experiments. The performance is compared to the DNN-MRFT based PID controller and to the BFBEL controller. The experimental results show that the proposed SO-BFBEL controller achieves robust trajectory tracking for both circle and 3-D eight shaped trajectory under extreme wind disturbance and with lower computational cost. The proposed self-organizing algorithm can be applied to optimize other controllers with fuzzy neural network structure.

 Abstract: A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is nonmodel-based and has a simplified fuzzy neural network structure and adapts with a novel bidirectional brain emotional learning algorithm. It is applied to control all six degrees-of-freedom of a QUAV for accurate trajectory tracking and to handle the payload uncertainties and disturbances in real-time. The trajectory tracking performance and the ability to handle the payload uncertainties are experimentally demonstrated on a QUAV. The experimental results show a superior performance and rapid adaptation capability of the proposed BFBEL controller. The proposed BFBEL controller can be used for the commercial drone applications.

Abstract: Unmanned Aerial Vehicles (UAVs) have played an essential role in military and civilian domains. The research in this thesis contributes to the field of Intelligent Control Systems (ICSs) and especially achieving reliable and convenient autonomous control for Rotary Wing UAVs (RUAVs). In particular, the challenge of adapting to unmodelled dynamics and disturbances such as changing payloads in mid-air is tackled. UAVs can carry extra weights such as sensors, cargo and even underslung loads which are known as the payload. Many strategies have been developed to stabilize the drone with changing payload but they all assume the payload to be rigid and the Centre of Gravity (CoG) to be static and known. Variations in the payload mass and its type during flight, can dramatically affect the dynamics of the drone, requiring a controller to adapt to maintain satisfactory closed loop performance. A scenario where a fleet of delivery drones could be launched from a larger aircraft (like a weather balloon) in mid-air with random pose attitude is also not yet explored. Finally, uncertainties such as unmodelled dynamics and wind gusts pose challenges to flight operations, so ICSs are essential to deal with these uncertainties but not enough attention is given to the design and development of non-model-based ICSs. Motivated by these research gaps, this thesis tackles the control problem of handling payload with changing CoG and pose independent launch in mid-air. To address these problems and to achieve the desired trajectory tracking control, a novel non-model based ICS called the Bidirectional Fuzzy Brain Emotional Learning (BFBEL) control system is presented. The proposed control system merges fuzzy inference, neural networks and a novel Bidirectional Brain Emotional learning (BBEL) algorithm based on reinforcement learning. The proposed BFBEL controller is capable of adapting rapidly from scratch and it is introduced to control all the Six Degrees of Freedom (6DOF) of the RUAVs. To expand the applicability of the proposed controller, both Single-Input-Single-Output (SISO) and Multi-Input-Multi-Output (MIMO) architecture are developed. The two RUAV models considered for this research are the Quadcopter UAV (QUAV) and the Helicopter UAV (HUAV). The SISO version of BFBEL control system is applied to QUAV to address the problem of handling external payload with varying CoG and weight. The MIMO version of the BFBEL control system is applied to a HUAV to address the problem of pose independent launch in mid-air. Both systems are evaluated with simulations and the problem of handling external payload with uncertain CoG is verified with experiments. Finally, the flight capabilities and control performance are compared with a conventional Proportional Integral Derivative (PID) controller scheme under the same control scenarios.