Optimal behaviour prediction using a primitive-based data-driven model-free iterative learning control approach

FPGA-based startup for AC electric drives: Application to a greenhouse ventilation system

“…Such implementation is not described, only the illustration of the main RTL schematic diagram is provided, making it difficult in that way to reproduce the reported results. The reader can find a complete survey of FPGA design methodology for industrial control systems as presented in [13], for the control of AC machine drives in [15], for the tuning of control systems in [8,18], and for control of industrial applications in [17,19,21].…”

An Enhanced Coupling Nonlinear Tracking Controller for Underactuated 3D Overhead Crane Systems

“…Radac et al . presented an optimal behavior prediction mechanism for multi‐input–multi‐output control systems in a hierarchical control system structure, using previously learned solutions to simple tasks called primitives. Zhang et al .…”

“…In [20][21][22][23], reference trajectories subject to trolley displacement, velocity, and acceleration limitation were defined, and then combined with anti-swing components, guaranteeing precise trolley positioning and anti-swing control. Radac et al [24] presented an optimal behavior prediction mechanism for multi-input-multi-output control systems in a hierarchical control system structure, using previously learned solutions to simple tasks called primitives. Zhang et al [25] achieve simultaneous motion regulation and payload swing suppression and elimination within a finite time.…”

Synchronized Dancing of an Industrial Manipulator and Humans with Arbitrary Music

“…Radac and Precup [23] classified the ways of learning from primitives as a) timescale transformation approaches, b) temporal concatenation of primitive-based approaches, and c) time-based decomposition approaches. The primitives are useful to simplify the task, which can be very complex.…”