Actuators of robot operate in the joint-space while the end-effect or of robot is controlled in the task-space. Therefore, designing a control system for a robotic system in the task-space requires the jacobian matrix information for transforming joint-space to task-space, which suffers from uncertainties. This paper deals with the robust task-space control of electrically driven robot manipula...

tthe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. this paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. as a novelty, the proposed controller employs a simple gaussian radial-basis-function network as an uncertainty estimator. the proposed netw...

This letter 1 presents the application of the model-free control approach to the microgrid control. We show in simulation that the method allows to control , with a simple controller , voltage , current and power of inverter-based microgrids .

Some robotic tasks require an accurate control to follow the desired trajectory in the presence of unforeseen external disturbances and system parameters variations. In this case conventional control techniques such as PID must be constantly readjusted and a compromise solution must be adopted. This problem can be avoided using a learning process that automatically learns the appropriate contro...

model predictive controller is widely used in industrial plants. uncertainty is one of the critical issues in real systems. in this paper, the direct adaptive simplified model predictive control (smpc) is proposed for unknown or time varying plants with uncertainties. by estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly ca...

Model bias is an inherent limitation of the current dominant approach to optimal quantum control, which relies on a system simulation for optimization control policies. To overcome this limitation, we propose circuit-based training reinforcement learning agent tasks in model-free way. Given continuously parameterized circuit, learns its parameters through trial-and-error interaction with system...