Due to computational burden and dynamic uncertainty, the classical model-based control approaches are hard to be implemented in the multivariable robotic systems. In this paper, a model-free fuzzy sliding mode control based on neural network is proposed. In classical sliding mode controllers, system dynamics and system parameters are required to compute the equivalent control. In Radial Basis Function Neural Network (RBFNN) based fuzzy sliding mode control, a RBFNN is developed to mimic the equivalent control law in the Sliding Mode Control (SMC). The weights of the RBFNN are changed for the system state to hit the sliding surface and slide along it with an adaptive algorithm. The initial weights of the RBFNN are set to zero and then tuned online, no supervised learning procedures are needed. In the proposed method, by introducing the fuzzy concept to the sliding mode and fuzzifying the sliding surface, the chattering can be alleviated. The proposed method is implemented on industrial robot (Manutec-r15) and compared with a PID controller. Experimental studies carried out have shown that this approach is a good candidate for trajectory tracking applications of industrial robot. http://dx.doi.org/10.5755/j01.itc.40.2.430

This paper focuses on designing a new robust controller for flexible joint manipulator using time-varying sliding surface. The sliding mode control is thought to be a powerful choice to overcome nonlinear and parametric uncertainties of flexible joint robot manipulators. The basic idea of the proposed control method is based on fuzzifying the sliding surface. Mamdani type fuzzy controller is used for this purpose. The results have demonstrated that the designed control method has superior performance. It is observed that the proposed control algorithm continues to be efficient in the presence of disturbances. In addition, the results are compared with both fuzzy sliding mode control in which the switching control gain was adjusted by fuzzy logic and the classical sliding mode control. It is observed that the method of adjusting the sliding surface with fuzzy logic follows the desired trajectory with less error than other methods. At the same time, the chattering is eliminated by the proposed method.