Special Issue "Analysis and Design of Robust Control Systems Applied to Robotic Systems"

Dear Colleagues,

The analysis and design of automatic control systems is essential to physically implementing and operating robotic systems. These functions are based on mathematical models defined for each physical system; however, there is no mathematical model that truly represents the dynamic behavior of a system. This in turn leads to great concern about how to overcome this issue. To compensate for modeling errors, different lines of research have been created. Such is the case for robust control, where it is assumed that the mathematical model has uncertainty (dynamic or parametric) during the analysis stage so that the results obtained are closer to reality than in cases where uncertainty is not considered. Robotic systems are dynamic systems with multiple applications in industry. One example is manipulator robots, which carry out various activities, including welding, painting, and assembly of electronic parts. Additionally, robot manipulators can be applied in tasks where human life may be at risk or in processes that require a very high production rate. Unmanned aerial vehicles are another type of robotic system, being used in civilian and military applications, such as forest fire detection, oil pipeline inspection, high-voltage line failure detection, and surveillance. This Special Issue is addressed to all types of robotic systems that use robust analysis or design to improve performance.

Dr. Gerardo Romero Galván
Guest Editor

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• robust control design (parametric or dynamic uncertainty)
• robust stability analysis (parametric or dynamic uncertainty) 
• failure detection 
• multi-agent systems 
• multi-robot systems
• robot manipulator control 
• inverse dynamics control
• trajectory control
• pd control 
• passive fault-tolerant control
• intelligent robotics
• sliding mode control
• unmanned aerial vehicles
• underwater autonomous vehicles
• complex systems
• human–robot interaction
• cognitive robotics
• artificial vision