In recent years, the concept of modular and reconfigurable robotics emerged as a means for flexible and versatile automation This concept allows for the execution of many complex tasks that cannot be performed by fixed-configuration manipulators Nevertheless, reconfigurable robots introduce a challenging level of complexity to the problem of design of controllers that can handle a wide range of robot configurations with reliable performance This paper addresses the position control of modular and reconfigurable robots We develop a practical intelligent-control architecture that can be easily used in the presence of dynamic parameter uncertainty and unmodeled disturbances The architecture requires no a priori knowledge of the system-dynamics parameters Adaptive control is provided using fuzzy gain tuning of proportional-integral-derivative parameters in the presence of external disturbances The architecture also provides learning control using feedforward neural networks Moreover, the architecture has the capability of updating the adaptive control under reconfigurability Experiments on a modular robot test bed are reported to validate the effectiveness of the control methodology

Neurofuzzy control of modular and reconfigurable robots

Fuzzy control of a dc motor driven four-bar mechanism

This paper presents a method of fuzzy control combined with grey system modeling approach of a mechatronic system to be used in driving a four-bar mechanism by a dc motor through a buck converter. The main contribution of this paper is that it predicts the future error values in terms of the previous error values using a grey estimator and determines the control action for the following step that depends on the predicted error value before it occurs. Despite the basic assumption that the angular velocity of the crank is constant in most of the mechanism analysis, this may not be valid when the mechanism is connected to an electric motor. In this paper, a complete state-space mathematical model of each part of the converter-motor-mechanism system is first developed and numerically simulated to demonstrate the crank angular speed fluctuations for the case of a constant voltage supply. Then, a fuzzy logic controller combined with grey system modeling approach is designed to regulate the crank angular speed of the mechanism and compared with a fuzzy controller used alone. Finally, results are obtained for each part of the system separately, then related parts are connected as cascade, and complete system control is tested for the proposed control approach. The connection between dc motor and four-bar mechanism has been considered as a dynamic load for the converter. Comparatively better results are obtained when the fuzzy controller is used together with grey system modeling approach. The results obtained from the proposed controller are not only superior in the rise time, speed fluctuations, and percent overshoot, but also much better in the controller output signal structure.

Hybrid Control of a Mechatronic System: Fuzzy Logic and Grey System Modeling Approach

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs.

Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

This work aims to propose a novel design for quick return mechanisms, and the new mechanism is composed by a generalized Oldham coupling and a slider-crank mechanism. First, the kinematic dimension...

P.Y. Lu

Papers

Fuzzy neural network control of a motor-quick-return servomechanism