Adaptive Control for Quadrotors with Uncertain and Faulty Actuators

Two adaptive control strategies are presented in this paper for two types of quadrotors to cope with potential uncertainties and faults in the actuator system. The four actuators of the considered quadrotors are distinct and suffered from loss of effectiveness (LOE) as well. To accomandate unknown actuator parameters, a filter-based immersion and invariance (I&I) adaptive controllers are designed for attitude and altitude subsystems, respectively, and an integral backstepping controller is developed for the horizontal subsystem to achieve null steady-state error. Both simulation and experiment results are carried out to illustrate the effectiveness of trajectory tracking performance and fault-tolerant accomondation ability of the proposed control schemes.

Optimizing the Performance of Containerized Cloud Software Systems Using Adaptive PID Controllers

Control theory has proven to be a practical approach for the design and implementation of controllers, which does not inherit the problems of non-control theoretic controllers due to its strong mathematical background. State-of-the-art auto-scaling controllers suffer from one or more of the following limitations: (1) lack of a reliable performance model, (2) using a performance model with low scalability, tractability, or fidelity, (3) being application- or architecture-specific leading to low extendability, and (4) no guarantee on their efficiency. Consequently, in this article, we strive to mitigate these problems by leveraging an adaptive controller, which is composed of a neural network as the performance model and a Proportional-Integral-Derivative (PID) controller as the scaling engine. More specifically, we design, implement, and analyze different flavours of these adaptive and non-adaptive controllers, and we compare and contrast them against each other to find the most suitable one for managing containerized cloud software systems at runtime. The controller’s objective is to maintain the response time of the controlled software system in a pre-defined range, and meeting the Service-level Agreements, while leading to efficient resource provisioning.

Global Adaptive Consensus Control for Multiagent Systems with Predefined Accuracy

This paper addresses a consensus problem for uncertain nonlinear multiagent systems with predefined precision under disturbance. By employing the neural networks method and backstepping technique, adaptive controllers for each agent are created. In contrast to the exiting global control methods for multiagent systems, global precision consensus control scheme is first put forward. Moreover, by using three n th-order continuous differentiable functions, adaptive tuning laws and virtual controllers and the real controller are designed. It is proved that the presented method can ensure that all signals are globally bounded and systems can be consistent with a given accuracy under disturbance. Finally, a practical simulation verifies the correctness for the devised control protocol.

Cluster synchronization in fractional-order network with nondelay and delay coupling

In this paper, cluster synchronization for fractional-order complex network with nondelay and delay coupling is investigated. Based on the stability theory of fractional-order systems and the properties of fractional derivative, both static and adaptive control schemes are adopted to design effective controllers. Sufficient condition for achieving cluster synchronization about static controllers is provided. From the condition, the needed feedback gains can be estimated by simple calculations. Further, adaptive control scheme is introduced to design unified controllers. Noticeably, in the adaptive controllers, the feedback gains need not be calculated in advance and can adjust themselves to the needed values according to updating laws. Finally, numerical simulations are given to demonstrate the correctness of the obtained results.