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The main impetus for the development of bioinspired robotic technologies stems from their applications to some tasks which require a safe interaction with human beings, e.g., during rehabilitation exercises. In this context, this work presents the mechatronic design and the tracking control of a robotic exoskeleton actuated by biomimetic and soft actuators. The exoskeleton can perform some tasks of physical rehabilitation, whereas the intrinsic and tunable compliance of the robot joints guarantees the safety specifications required for human-robot interactions. The steps of design and realization described in this paper involve the synthesis and the implementation of a trajectory tracking controller. A nonlinear control law, which compensates for the nonlinear dynamics of the actuators, is employed in combination with a PID control action. The designed control law, experimentally validated during some sessions of robotic rehabilitation, achieves the robust regulation of the tracking error to zero.

A novel dynamic friction model, which allows to capture friction hysteresis with non-local memory, is presented in this paper. The model is conceived in order to find a trade-off between accuracy of the model prediction and difficulty of implementation in motion control systems with model-based friction compensation. The hysteresis function introduced into the model accounts for non-local memory, i.e., the property for which the friction output depends not only on the initial conditions but also on past extremum values of the input or the output. In comparison with other models incorporating a hysteresis function with non-local memory, the proposed model is demonstrated to reduce the number of parameters necessary to reproduce the hysteresis loops observed experimentally. Moreover, parameter identification can benefit from the availability of a closed form of the model solution.

Pneumatic Artificial Muscle (PAM) is becoming one of the most used actuator technology for the development of biorobotic applications, such as robotic orthoses and wearable exoskeletons, which require the accurate control of the impedance during human-robot interactions. Although the adaptable compliance of PAMs is desirable, the nonlinear and hysteretic relation between contraction length and pulling force, as well as the air pressure within the chamber of the PAM, make difficult the identification and the control of the dynamics of such actuators. After the description of the experimental setup designed for the dynamic identification of PAMs, this paper presents a novel and accurate model of the hysteresis of the mechanical response of PAMs. Some experimental tests have been performed on a real pneumatic muscle in order to reproduce the different features of the hysteretic behavior which are taken into account in the definition of the model. The proposed model, which has been validated through some experiments, provides some advantages in terms of ease of parameter identification and implementation into a control system, thanks to the use of a limited number of parameters. Index Terms—friction identification and compensation, hysteresis with nonlocal memory, pneumatic artificial muscles.

In this paper, a robust and optimal control problem for uncertain bilinear systems is formulated via a guaranteed-cost approach and then applied to tracking control design of a robotic arm actuated by Pneumatic Artificial Muscles (PAMs). Taking into account the nonlinear dynamics of the pneumatic actuators, the tracking error dynamics has been described as an uncertain bilinear systems, whereas unknown perturbations on robot dynamics and modelling mismatch are regarded as an external disturbance to be attenuated. Some numerical simulations are also given to demonstrate both robustness and optimality of the control performance in tracking desired trajectories with the required accuracy.

In this paper, an optimal control problem for uncertain bilinear systems is formulated via a guaranteed cost approach and then applied to the design of a stabilizing controller for a robot arm actuated by Pneumatic Artificial Muscles (PAMs). The results show that the contributed methodology is suitable for efficiently designing control systems which can match the requirements both on safety and on energy efficiency for PAMs-driven robots during human-robot interactions. The performances of the state-feedback control system are evaluated on the basis of some numerical simulations.