Humanoid Lower Limb: Design, Analysis, Observer-Based Fuzzy Adaptive Control and Experiment

With flexibility similar to human muscles, pneumatic artificial muscles (PAMs) are widely used in bionic robots. They have a high power-mass ratio and are only affected by single-acting pneumatic pressure. Some robots are actuated by a pair of PAMs in the form of antagonistic muscles or joints through a parallel mechanism. The pneumatic pressure and length of PAMs should be measured simultaneously for feedback using a pressure transducer and draw-wire displacement sensor. The PAM designed by the FESTO (10 mm diameter) is too small to install a draw-wire displacement sensor coaxially and cannot measure muscle length change directly. To solve this problem, an angular transducer is adopted to measure joint angles as a whole. Then, the inertia of the lower limb is identified, and observer-based fuzzy adaptive control is introduced to combine with integrated control of the angular transducer. The parameters of the fuzzy control are optimized by the Gaussian basis neural network function, and an observer is developed to estimate the unmeasured angular accelerations. Finally, two experiments are conducted to confirm the effectiveness of the method. It is demonstrated that piriformis and musculi obturator internus act as agonistic muscle and antagonistic muscles alternatively, and iliopsoas is mainly responsible for strengthening because of the constant output force. Piriformis has a greater influence on yaw and roll angles, while musculi obturator internus is the one that influences the pitch angle the most. Due to joint friction, the dead zone of the high-speed on-off valve, lag of compressed air in the trachea, and coupling among angles are very difficult to realize precise trajectory tracking of the pitch, yaw, and roll angles simultaneously.

Switching mechanism-based event-triggered fuzzy adaptive control with prescribed performance for MIMO nonlinear systems

<p style='text-indent:20px;'>This paper investigates the switching mechanism-based event-trig-gered fuzzy adaptive control issue of multi-input and multi-output (MIMO) nonlinear systems with prescribed performance (PP). Utilizing fuzzy logic systems (FLSs) to approximate unknown nonlinear functions. By using the switching threshold strategy, the system has more flexibility in strategy selection. The proposed control scheme can better solve the communication resource limitation. On account of the Lyapunov stability theory, the stability of the controlled system is proved. And all signals of the controlled system are bounded. Moreover, the tracking errors are controlled in a diminutive realm of the origin within the PP bounded. Simultaneously, the Zeno behavior is avoided. Finally, illustrate the effectiveness of the control scheme that has been proposed by demonstrating some simulation consequences.</p>