Identification (biology)

Sparrow

Biology

Algorithm

Artificial intelligence

Ecology

Chaotic

Computer science

Botany

Computer vision

Robot

Applied Intelligence

Parameter identification is essential for the model-based high-accuracy control of robot manipulators. The objective of this work is to develop a swarm intelligence-based technique to estimate distinct system parameters and to promote the control accuracy of robot manipulators. For this purpose, an improved chaotic sparrow search algorithm (ICSSA) integrated with Kent chaotic mapping, Student’s t-distribution, and the Lévy flight strategy is implemented based on the basic sparrow search algorithm (SSA). Benefitting from the unique advantages in local and global optimization of the ICSSA, the inertial and friction parameters of a two-link robot manipulator with unknown payloads are estimated via simulation experiments. The estimated parameters are analyzed and compared with other popular advanced optimization methods and the classic least square method. The results demonstrate that the ICSSA provides more competitive results over other popular optimization algorithms. It might offer another promising approach of high-level accuracy for advanced control techniques in industry robot manipulators.

Parameter identification of robot manipulators with unknown payloads using an improved chaotic sparrow search algorithm