In this note we present an adaptive gain twisting sliding mode control strategy achieving chatter alleviation for the control of pneumatic artificial muscles robot arm. The key-point of the paper relies on a simple modification of the twisting algorithm where the control action is proportional to the desired sliding surface and its derivative instead of the sign of it, then the adaptive gain twist control law, continuously drives the sliding surface and its derivative to a predefined domain in finite time in the presence of bounded disturbance with unknown boundary. The derivation and the proof of the adaptation algorithm are derived from the proposed Lyapunov function, and the adaptation law doesn't overestimate the values of the control gain. The efficiency of the controller is evaluated through simulation results.

In this note we present a novel intelligent twisting sliding mode controller using neural network, achieving chatter reduction for the control of pneumatic artificial muscles robot arm. The system is highly non-linear and somehow difficult to model therefore resorting to robust control is required. Thanks to their property as universal approximators, in this work a two layer NN with on line adaptive learning law is used to reconstruct unknown and unmodeled robot dynamics, and the realisation of a two sliding mode is achieved through the design of a nonlinear sliding surface. The stability of the overall system is guaranteed by lyapunov method. Experimental results are presented and discussed.

We are concerned with the control of a 3-DOF robot arm actuated by pneumatic rubber muscles. The system is highly non-linear and somehow difficult to model therefore resorting to robust control is required. In order to alleviate the effects of nonlinearities and uncertainties, a combined control strategy based on neural network (NN) and the concept of sliding mode control (SMC) is proposed systematically. In this control structure a simple “two-layer” feedforward neural network (NN) with on line adaptive learning laws is used to estimate unknown plant dynamics and chattering phenomenon in conventional SMC is eliminated by incorporated a modified corrective control term. The algorithm is derived from Lyapunov stability analysis, so that both system tracking stability and error convergence can be guaranteed in the closed-loop system. Experimental results are presented and discussed.

Pneumatic artificial rubber muscles (Parms) are similar to biological muscles as both act as springs. Stiffness varies with pressure for the Parm and with neural impulse for the biological muscle. As a Parm system is highly non linear, its classical control does not reveal to be totally indicated. Robust control techniques such as variable structure controls generating sliding modes are quite indicated. However, sliding mode control although robust generates chattering as the control switches across the sliding manifold. In this work, two controls are applied onto a robot driven by Parms in order to reduce this undesirable chatter. The first law is a generalised variable structure algorithm while the second one is a 2-sliding law, the “twisting” algorithm. Experimental results are presented and discussed.

We are concerned with the control of a 3-DOF robot arm actuated by pneumatic rubber muscles. The system is highly non-linear and somehow difficult to model therefore resorting to robust control is required. The work in this paper addresses this problem by presenting two types of robust control. One uses neural network control, which has powerful learning capability, adaptation and tackles nonlinearities; in our work the learning performed on-line is based on a binary reinforcement signal without knowing the nonlinearities appearing in the system and no preliminary off-line learning phase is required. The other control law is a Classical variable structure which is robust against parameters variations and external disturbances. Experimental results together with a comparative study are presented and discussed.