Neural Network-based Finite-time Control of Nonlinear Systems with Unknown Dead-zones: Application to Quadrotors

Over the years, researchers have addressed several control problems of various classes of nonlinear systems. This article considers a class of uncertain strict feedback nonlinear system with unknown external disturbances and asymmetric input dead-zone. Designing a tracking controller for such system is very complex and challenging. This article aims to design a finite-time adaptive neural network backstepping tracking control for the nonlinear system under consideration. In addition,  all unknown disturbances and nonlinear functions are lumped together and approximated by radial basis function neural network (RBFNN). Moreover, no prior  information about the boundedness of the dead-zone parameters is required in the controller design. With the aid of a Lyapunov candidate function, it has been shown that the tracking errors converge near the origin in finite-time. Simulation results testify that the proposed control approach can force the output to follow the reference trajectory in a short time despite the presence of  asymmetric input dead-zone and external disturbances. At last, in order to highlight the effectiveness of the proposed control method, it is applied to a quadrotor unmanned aerial vehicle (UAV)

Fuzzy control turns 50: 10 years later

In 2015, we celebrate the 50th anniversary of Fuzzy Sets, ten years after the main milestones regarding its applications in fuzzy control in their 40th birthday were reviewed in FSS, see [1]. Ten years is at the same time a long period and short time thinking to the inner dynamics of research. This paper, presented for these 50 years of Fuzzy Sets is taking into account both thoughts. A first part presents a quick recap of the history of fuzzy control: from model-free design, based on human reasoning to quasi-LPV (Linear Parameter Varying) model-based control design via some milestones, and key applications. The second part shows where we arrived and what the improvements are since the milestone of the first 40 years. A last part is devoted to discussion and possible future research topics.Guerra, T.; Sala, A.; Tanaka, K. (2015). Fuzzy control turns 50: 10 years later. Fuzzy Sets and Systems. 281:162-182. doi:10.1016/j.fss.2015.05.005S16218228

An Adaptive Dynamic Surface Controller for Ultralow Altitude Airdrop Flight Path Angle with Actuator Input Nonlinearity

In the process of ultralow altitude airdrop, many factors such as actuator input dead-zone, backlash, uncertain external atmospheric disturbance, and model unknown nonlinearity affect the precision of trajectory tracking. In response, a robust adaptive neural network dynamic surface controller is developed. As a result, the aircraft longitudinal dynamics with actuator input nonlinearity is derived; the unknown nonlinear model functions are approximated by means of the RBF neural network. Also, an adaption strategy is used to achieve robustness against model uncertainties. Finally, it has been proved that all the signals in the closed-loop system are bounded and the tracking error converges to a small residual set asymptotically. Simulation results demonstrate the perfect tracking performance and strong robustness of the proposed method, which is not only applicable to the actuator with input dead-zone but also suitable for the backlash nonlinearity. At the same time, it can effectively overcome the effects of dead-zone and the atmospheric disturbance on the system and ensure the fast track of the desired flight path angle instruction, which overthrows the assumption that system functions must be known

Adaptive Robust Control of Biomass Fuel Co-Combustion Process

The share of biomass in energy production is constantly growing. This is caused by environmental and industry standards and EU guidelines. Biomass is used in the process of co-firing in large power plants and industrial installations. In the existing power stations, biomass is milled and burned simultaneously with coal. However, low-emission combustion techniques, including biomass co-combustion, have some negative side effects that can be split into two categories. The direct effects influence the process control stability, whereas the indirect ones on combustion installations via increased corrosion or boiler slagging. The effects can be minimised using additional information about the process. The proper combustion diagnosis as well as an appropriate, robust control system ought to be applied. The chapter is devoted to the analysis of modern, robust control techniques for complex power engineering applications

ADAPTIVE FUZZY CONTROL CONCEPTS AND SURVEY

In this paper an adaptive fuzzy control concepts and survey are introduced. Starting with the global adaptive control towered the adaptive fuzzy control, the required concepts are explained. Some of the adaptive fuzzy control subjects are viewed as sequential steps with simplifying their views to enable the reader to get a fast and global idea with some details if it is necessary. Most of the stability considerations in the corresponding references are proved by using the lyapunov criteria, where the derivation is a mathematical concept with long steps. Therefore, it is mentioned without details, and for more information, the corresponding reference must be studied. It can be seen from this topic, that the main role of the fuzzy system in adaptive control is the system identification, controller construction and output predictor. The adaptive fuzzy control survey is presented at the end, so the reader can go along with the topics after he reviewed the necessary concepts