Quantum mechanics

Pneumatic artificial muscles

Feedback linearization

Adaptive control

Algorithm

Iterative learning control

Control (management)

Linearization

Engineering

Biology

Control engineering

Actuator

Convergence (economics)

Stability (learning theory)

Economics

Computer science

Controller (irrigation)

Physics

Nonlinear system

Trajectory

Artificial muscle

Astronomy

Agronomy

Artificial intelligence

Economic growth

Control theory (sociology)

Iterative method

Machine learning

IEEE Transactions on Industrial Electronics

Pneumatic artificial muscles (PAMs) have been widely used in actuation of medical devices due to their intrinsic compliance and high power-to-weight ratio features. However, the nonlinearity and time-varying nature of PAMs make it challenging to maintain high-performance tracking control. In this article, a high-order pseudopartial derivative-based model-free adaptive iterative learning controller (HOPPD-MFAILC) is proposed to achieve fast convergence speed. The dynamics of PAM is converted into a dynamic linearization model during iterations; meanwhile, a high-order estimation algorithm is designed to estimate the pseudopartial derivative component of the linearization model by only utilizing the input and output data in previous iterations. The stability and convergence performance of the controller are verified through theoretical analysis. Simulation and experimental results on PAM demonstrate that the proposed HOPPD-MFAILC can track the desired trajectory with improved convergence and tracking performance.

High-Order Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle With Enhanced Convergence