A novel fuzzy friction compensation approach to improve the performance of a DC motor control system

Abstract

The compensation of friction nonlinearities for servo motor control has received much attention due to undesirable and disturbing effects that the friction often has on conventional control systems. Compensation methods have generally involved selecting a friction model and then using model parameters to cancel the effects of the nonlinearity. In this paper, a method using fuzzy logic for the compensation of nonlinear friction is developed for the control of a dc motor. The method is unique in that a single fuzzy rule is used to compensate directly for the nonlinearity of the physical system. As a result, the method introduces fewer adjustable parameters than a typical fuzzy logic approach while still incorporating many advantages of using fuzzy logic such as the incorporation of heuristic knowledge, ease of implementation and the lack of a need for an accurate mathematical model. The general approach, analysis and experimental results obtained for an actual dc motor system with nonlinear friction characteristics are presented and the effectiveness of the fuzzy friction compensation control technique is discussed. The smoothness of response, response times and disturbance rejection of a PI control system with and without the proposed fuzzy compensator are analyzed and discussed to illustrate the effectiveness of the proposed method.

Intelligent control of DC motor driven mechanical systems: a robust learning control approach

“…Recently, a lot of intelligent control approaches have been developed to attack the modelling and compensation problem of mechanical systems with friction [1,2,3,4,5,6,7,8,9,10]. These control schemes make use of some soft computing techniques in representing and estimating friction models for feedforward compensation.…”

Application of functional link neural network to HVAC thermal dynamic system identification

“…The authors stress the importance of self learning in building control systems and encourage further studies in the integration of optimal control and other advanced techniques into the formulation of such systems. The nonlinear functional mapping properties of neural networks are central to their use in identification and control [10]- [14]. Although a number of key theoretical problems remain, results pertaining to the approximation capabilities of neural networks demonstrate that they have great promise in the modeling of nonlinear systems.…”

Network-based control adaptation for network QoS variation

“…Each remote controller can send local measurements, such as motor current, speed, temperature, and local environment information, back to the central controller via a network. Each remote process has its own system dynamics that can be described by the state space description [9] shown in …”

“…The load can be a robot arm or an unmanned electric vehicle, for instance. The loop equation for the electrical circuit is [9]:…”

“…In this paper, the central controller uses a PI control algorithm to compute the control to the remote system for step tracking based on the monitored system signal sent from the remote system via the network link. The PI controller used has the form [9]:…”