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This paper presents the use of nonlinear optimal predictive control on rubber artificial muscles. A neural network which can learn dynamic characteristics of rubber artificial muscles is employed in this control scheme in order to obtain the model. The actuator has strong nonlinear characteristics due to compression of both rubber and air. it is difficult to control with precision using the linear control theory. A suitable control method for this actuator has yet to be established. Computer simulations of a common pneumatic actuator, which is modeled quite easily were carried out to verify the proposed control scheme. This control scheme was adapted to a practical one dimensional robot manipulator which was developed for this study. The theory was compared with PlO control in order to explain availability of the theory. When desired targets are changed as a step response, this actuator can be controlled precisely in these simulations and experiments. The actuator can be controlled adaptively under changes of characteristics if the learning of the neural network continues. It is obvious from these results that the proposed control scheme is effective for rubber artificial muscle.

The present study proposes a new design method for a proportional-integral-derivative (PID) control system for first-order plus dead-time (FOPDT) and over-damped second-order plus dead-time (SOPDT) systems. What is presented is an optimal PID tuning constrained to robust stability. The optimal tuning is defined for each one of the two operation modes the control system may operate in: servo (reference tracking) and regulation (disturbance rejection). The optimization problem is stated for a normalized second-order plant that unifies FOPDT and SOPDT process models. Different robustness levels are considered and for each one of them, the set of optimal controller parameters is obtained. In a second step, suitable formulas are found that provide continuous values for the controller parameters. Finally, the effectiveness of the proposed method is confirmed through numerical examples.