This paper proposes a detailed investigation on the new neural-based feed-forward PID direct force control (FNN-PID-DF) approach applied to a highly nonlinear 2-axes pneumatic artificial muscle (PAM) manipulator in order to ameliorate its force output performance. Founded on the novel inverse neural NARX model dynamically identified to learn well all nonlinear characteristics of the contact force dynamics of the 2-axes PAM-based manipulator, the novel proposed neural FNN-PID-DF force controller is innovatively implemented in order to directly force control the 2-axes PAM robot system used as a rehabilitation device subjected to internal systematic interactions and external contact force deviations. The performance of the experimental tests has proven the advantages and merits of the new force control method compared to the classical PID force control method. The new neural FNN-PID-DF force controller guides the wrist/hand of subject/patient to successfully generate the predefined desired force values.

This paper reports on the use of a particular actuator in the field of legged robots. The proposed actuator, the Pleated Pneumatic Artificial Muscle, has some interesting characteristics which makes it suitable for machines which move by means of legs. An important issue is the actuator's adaptable passive behaviour which allows the stiffness of a joint that is actuated by two antagonistically coupled muscles, to be varied online. The natural frequency of the system can thus be changed in order to reduce control efforts and energy consumption. The idea of changing this natural frequency in combination with trajectory control will be implemented on a two-dimensional leg model. It will be shown that an appropriate choice of compliance can strongly reduce the amount of needed control activity and energy consumption while tracking a given trajectory.