A5093316415- Extremum Seeking Control Systems
- Iterative Learning Control Systems
- Structural Health Monitoring Techniques
- Advanced machining processes and optimization
- Advanced Measurement and Metrology Techniques
- Robotic Locomotion and Control
- Advanced Control Systems Design
- Dynamics and Control of Mechanical Systems
- Combustion and flame dynamics
- Plasma and Flow Control in Aerodynamics
- Advanced Control Systems Optimization
- Advanced Sensor Technologies Research
- Prosthetics and Rehabilitation Robotics
- Real-time simulation and control systems
Eindhoven University of Technology
1995-2024
Existing extremum-seeking control (ESC) approaches typically rely on applying repeated perturbations to input parameters and performing measurements of the corresponding performance output. Performing these can be costly in practical applications, e.g., due use resources, making it desirable reduce number performed measurements. Moreover, required separation between different timescales ESC loop results slow convergence. With challenges mind, this work presents an approach aimed at both...
Predicting the post-impact velocity for torque-controlled flexible-joint robots enhances impact-aware control schemes which exploit intentional collisions achieving dynamic robotic manipulation and locomotion. Compared to a previous approach based on fully rigid-robot assumption, this paper shows how an improvement in prediction can be obtained by taking into account joints' motor inertias, transmission ratios, low-level torque gains, as well impact surface friction. The letter also proposes...
Adequate tuning of control laws is essential for high positioning accuracy, large system throughput, and reliability in high-end mechatronic robotic systems. However, a population such systems generally shows slight variations dynamic responses due to, e.g., manufacturing tolerances, different disturbance situations, or position-dependent dynamics. Given the time-consuming nature controller design, even by experienced engineers, typically just one law designed whole based on worst-case...
We present a novel type of sampled-data extremum-seeking control (ESC) aimed at speeding up convergence to the optimum and reducing number costly performance measurements in practical applications. The approach uses collected output construct online an approximation system's steady-state function using kernel-based approximation. In regions where this is detected be sufficiently accurate, proposed utilizes it determine search direction compute suitable optimizer gain for update step. not yet...