In numerous electric drive applications, the mechanical phenomena in the velocity or position control loop determine real difficulties and challenges for the control system. So-called two-mass drive systems with a flexible shaft are the most important example of this situation. The problem becomes even more difficult if the characteristics of torque transmission along the shaft are nonlinear, nonlinear friction is present, and the plant parameters are unknown, as it happens in numerous robotic systems. A novel adaptive controller is derived for such a system. The recurrent design procedure is based on proper modifications of the adaptive backstepping scheme, including non-strict-feedback plant application, tuning functions to exclude controller overparameterization, robust adaptive laws, proper means to avoid controller complexity explosion, and a nonlinear PI controller in the initial loop to minimize quasi-steady-state tracking error. The closed-loop system uniform ultimate boundedness is proven using Lyapunov techniques and the design and tuning procedures are described. The attractive features of the obtained drive, including the robustness against the violation of assumptions, are presented using several examples.
Abstract. The problem of slip stabilization and tracking in railway vehicle applications is considered. A nonlinear adaptive control compensating for unknown disturbance in motion dynamics such as: friction, contact force variations and air resistance is proposed. The control is based on approximate models with adaptive parameters. The stability of several control algorithms is proven and performance of the derived controllers is investigated. The proposed controllers are evaluated in numerical simulations and by DSP application to slip control in a friction gear driven by a permanent magnet synchronous motor.
Abstract. We consider a Takagi-Sugeno-Kang (TSK) fuzzy rule based system used to model a memory-less nonlinearity from numerical data. We develop a simple and effective technique allowing to remove irrelevant inputs, choose a number of membership functions for each input, propose well estimated starting values of membership functions and consequent parameters. All this will make the fuzzy model more concise and transparent. The final training procedure will be shorter and more effective.
We discuss several fuzzy models to approximate friction and other disturbances in mechatronic systems, especially linear and rotarional electrical drives. Some methods of experimental identification of disturbance forces are presented. We consider several fuzzy models to compromise between model accuracy and complexity. Fuzzy model is used in an adaptive control loop. Several adaptive control algorithms are discussed and the influence of fuzzy model accuracy on the system performance is investigated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.