Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.<br /

State of the art of control schemes for smart systems featuring magneto-rheological materials

This review presents various control strategies for application systems utilizing smart magneto-rheological fluid (MRF) and magneto-rheological elastomers (MRE). It is well known that both MRF and MRE are actively studied and applied to many practical systems such as vehicle dampers. The mandatory requirements for successful applications of MRF and MRE include several factors: advanced material properties, optimal mechanisms, suitable modeling, and appropriate control schemes. Among these requirements, the use of an appropriate control scheme is a crucial factor since it is the final action stage of the application systems to achieve the desired output responses. There are numerous different control strategies which have been applied to many different application systems of MRF and MRE, summarized in this review. In the literature review, advantages and disadvantages of each control scheme are discussed so that potential researchers can develop more effective strategies to achieve higher control performance of many application systems utilizing magneto-rheological materials

Measurements, Models, Systems and Design

531 s.

Fuzzy-neuro-genetic aerofin control

U ovom radu razmatrano je fazi-genetsko-neuro upravljanje elektromehaničkog aktuatora aerokrila za kontrolu leta projektila, pokretanim motorom jednosmerne struje sa četkicama i permanentnim magnetom koji je pogonjen drajverom sa konstantnom strujom. U našim prethodnim radovima, na osnovu razvijenog nelinearnog modela sistema, razvijena su i testirana različta konvencionalna i hibridna konvencionalno-inteligentna upravljanja. U ovom radu predloženo je fazi i neuro-fazi upravljanje sa genetskom optimizacijom. Predloženi inteligentni upravljački sistemi, koji obezbeđuju dobro ponašanje sistema, su verifikovani numeričkim simulacijima i upoređeni sa prethodnim rezultatima. .In this paper fuzzy-neuro-genetic control of an electromechanical actuator (EMA) system for aerofin control (AFC), with permanent magnet brush DC motor driven by a constant current driver, is investigated. In our previous papers, nonlinear model of the EMA-AFC system and different classical and hybrid classicalcomputationally intelligent control systems have been designed and tested. In this paper we have proposed fuzzy and neuro-fuzzy control with genetic optimization. Proposed intelligent control systems, providing good transient response and system behaviour, have been validated by various numerical experiments and compared to previous results.

Neuro-Fuzzy Algorithm Implemented In Altera’s FPGA For Mobile Robot’s Obstacle Avoidance Mission.

This paper presents the designed obstacle avoidance program for mobile robot that incorporates a neuro-fuzzy algorithm using Altera™ Field Programmable Gate Array (FPGA) development DE2 board