No Arabic abstract
Nowadays, the application of fully autonomous system like rotary wing unmanned air vehicles (UAVs) is increasing sharply. Due to the complex nonlinear dynamics, a huge research interest is witnessed in developing learning machine based intelligent, self-organizing evolving controller for these vehicles notably to address the systems dynamic characteristics. In this work, such an evolving controller namely Generic-controller (G-controller) is proposed to control the altitude of a rotary wing UAV namely hexacopter. This controller can work with very minor expert domain knowledge. The evolving architecture of this controller is based on an advanced incremental learning algorithm namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS). The controller does not require any offline training, since it starts operating from scratch with an empty set of fuzzy rules, and then add or delete rules on demand. The adaptation laws for the consequent parameters are derived from the sliding mode control (SMC) theory. The Lyapunov theory is used to guarantee the stability of the proposed controller. In addition, an auxiliary robustifying control term is implemented to obtain a uniform asymptotic convergence of tracking error to zero. Finally, the G-controllers performance evaluation is observed through the altitude tracking of a UAV namely hexacopter for various trajectories.
There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PACs autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control systems tracking error and the controllers consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controllers efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.
Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous Unmanned Aerial Vehicles (UAVs). In this work, a four wing Natureinspired (NI) FW MAV is modeled and controlled inspiring by its advanced features like quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability when contrasted with comparable-sized fixed and rotary wing UAVs. The Fuzzy C-Means (FCM) clustering algorithm is utilized to demonstrate the NIFW MAV model, which has points of interest over first principle based modelling since it does not depend on the system dynamics, rather based on data and can incorporate various uncertainties like sensor error. The same clustering strategy is used to develop an adaptive fuzzy controller. The controller is then utilized to control the altitude of the NIFW MAV, that can adapt with environmental disturbances by tuning the antecedent and consequent parameters of the fuzzy system.
The joint design of control and communication scheduling in a Networked Control System (NCS) is known to be a hard problem. Several research works have successfully designed optimal sampling and/or control strategies under simplified communication models, where transmission delays/times are negligible or fixed. However, considering sophisticated communication models, with random transmission times, result in highly coupled and difficult-to-solve optimal design problems due to the parameter inter-dependencies between estimation/control and communication layers. To tackle this problem, in this work, we investigate the applicability of Age-of-Information (AoI) for solving control/estimation problems in an NCS under i.i.d. transmission times. Our motivation for this investigation stems from the following facts: 1) recent results indicate that AoI can be tackled under relatively sophisticated communication models, and 2) a lower AoI in an NCS may result in a lower estimation/control cost. We study a joint optimization of sampling and scheduling for a single-loop stochastic LTI networked system with the objective of minimizing the time-average squared norm of the estimation error. We first show that under mild assumptions on information structure the optimal control policy can be designed independently from the sampling and scheduling policies. We then derive a key result that minimizing the estimation error is equivalent to minimizing a function of AoI when the sampling decisions are independent of the state of the LTI system. Noting that minimizing the function of AoI is a stochastic combinatorial optimization problem and is hard to solve, we resort to heuristic algorithms obtained by extending existing algorithms in the AoI literature. We also identify a class of LTI system dynamics for which minimizing the estimation error is equivalent to minimizing the expected AoI.
Controlling of a flapping flight is one of the recent research topics related to the field of Flapping Wing Micro Air Vehicle (FW MAV). In this work, an adaptive control system for a four-wing FW MAV is proposed, inspired by its advanced features like quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability. Sliding Mode Control (SMC) theory has been used to develop the adaptation laws for the proposed adaptive fuzzy controller. The SMC theory confirms the closed-loop stability of the controller. The controller is utilized to control the altitude of the FW MAV, that can adapt to environmental disturbances by tuning the antecedent and consequent parameters of the fuzzy system.
This paper investigates the visual servoing problem for robotic systems with uncertain kinematic, dynamic, and camera parameters. We first present the passivity properties associated with the overall kinematics of the system, and then propose two passivity-based adaptive control schemes to resolve the visual tracking problem. One scheme employs the adaptive inverse-Jacobian-like feedback, and the other employs the adaptive transpose Jacobian feedback. With the Lyapunov analysis approach, it is shown that under either of the proposed control schemes, the image-space tracking errors converge to zero without relying on the assumption of the invertibility of the estimated depth. Numerical simulations are performed to show the tracking performance of the proposed adaptive controllers.