Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

Abstract

In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) laboratory equipment, where the cart position and the arm angular position of TCS were controlled using two Single Input-Single Output control system structures running in parallel. The parameters of the data-driven algorithms were tuned in a model-based way using a metaheuristic algorithm in order to improve the efficiency of energy consumption.

A Fuzzy Model of Risk Assessment for Environmental Start-Up Projects in the Air Transport Sector

“…The convenience of these methods is manifested in increasing the degree of validity of decisions, as all possible scenarios of development depicting the continuous spectrum of the set of scenarios are taken into account [27,28,29]. Other experts and authors as in References [30,31] discussed the general ideas and advantages that underpin contemporary views on the use of fuzzy logic in decision support systems in various fields of application. To competently assess the risk of a start-up project, one must learn to scientifically model information uncertainty by drawing the formal boundaries between credible knowledge, knowledge with a certain level of certainty, and what we do not know.…”

“…To competently assess the risk of a start-up project, one must learn to scientifically model information uncertainty by drawing the formal boundaries between credible knowledge, knowledge with a certain level of certainty, and what we do not know. We also find inspiration in the work of authors on the “combination of data-driven active disturbance rejection and Takagi–Sugeno fuzzy control with experimental validation on tower crane systems” [31], or in the work on “density peaks clustering based on k-nearest neighbors and principal component analysis” [28], among others. We take advantage of experience from aviation risk assessment processes, as well as from work such as on the “security management education and training of critical infrastructure sectors’ experts” focused on transportation [32], on the model of supplier quality management in transport companies [33], or on the implementation of free route airspace (FRA) [34], among others.…”

Research on Position Servo System Based on Fractional-Order Extended State Observer

“…By integrating the ADRC technology with the fractionalorder theory, the fractional-order control concept is integrated into the ESO to observe the internal and external disturbances of the system. The nonlinear state feedback error control law can be replaced by fractional-order PID controller [22]. Then, the controller and the control object have fast response speed, good robust performance, high control accuracy, and wide parameter adjustment range and so on [23], [24].…”

A Center Manifold Theory-Based Approach to the Stability Analysis of State Feedback Takagi-Sugeno-Kang Fuzzy Control Systems

“…The model-free tuning of fuzzy controllers is an alternative approach to the modelbased design of these controllers in order to benefit from the advantages of data-driven control and fuzzy control and to mitigate, if possible, their shortcomings. The indirect model-free tuning of fuzzy controllers has initially been proposed and applied in [82][83][84], and continued in [85][86][87] by structures that combine data-driven control and fuzzy control in order to incorporate model-free features in novel fuzzy control system structure. Thus, steps forward towards direct model-free tuning of fuzzy controllers are currently taken.…”