Focus and Scope

Journal of Robotics and Control (JRC) aims to become the leading-edge, most comprehensive, and most reliable source of theoretical and practice-oriented research information in discoveries, innovations, and inventions of robotics and control systems. JRC considers robots’ motion and control are equally essential and interdependently support each other.

Published papers are original manuscripts that either modify, implement, or design in one or more aspects of robotic’s motion and control.

The topic of published papers might be subjected to one or more of the following scopes:

Autonomous robots. Autonomous robots are intelligent machines capable of performing one or several tasks by themselves, without direct human control or force as an external influence. Topics from this scope may vary from implementation to control of robots’ manipulation, navigation, mapping, and vision. Practice-oriented research may take cases of flying robots, mobile robots, humanoid robots, underwater robots, industrial robots, etc.

Robotics modeling and design. Topics from this scope may vary from computational process to construction, small-scale home robots to industrial robots.

Embedded systems. Embedded systems are generally known as micro-processors or microcontrollers that are designed to perform a specific instruction. Topics from this scope widely vary from microcontrollers, robots’ sensors and actuators, to power electronics. It also can be extended into power electronics control which generally focuses on applying control systems to power electronics to build more improved and advanced motion of robots.

Control systems. Topics from control systems are widely varied. Generally, a control system can be defined as anything that manages, instructs, informs, or commands any system or behavior to achieve preferenced results. Sub-topics may be included in non-linear or linear control systems, intelligent control systems, automation control systems, and formation control.

Specified sub-topics can be categorized into Linear control such as Proportional Integral Derivative (PID) Control.

Modern control such as State-Feedback Control, Integral State Feedback, State Observer, and Fractional Order PID (FOPID).

Optimal control such as Linear Quadratic Regulator (LQR).

Nonlinear control such as Feedback Linearization, Gain scheduling, Lyapunov, Backstepping, High Gain Observer, and Passivity.

Robust control such as Sliding Mode Control, Terminal Sliding Mode Control, and Dynamic Sliding Mode Control.

Intelligent control such as Machine Learning Control, Neural Networks Control, Fuzzy Control, Expert Systems, and Reinforcement Learning.

Adaptive control such as Model Predictive Control.

Controller optimization using Genetic Algorithm, Particle Swarm Optimization. 

Path planning algorithms such as Potential Field.

Network Control Systems.