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Title Page
Contents
Abstract 7
I. INTRODUCTION 8
II. ITERATIVE LEARNING MECHATRONICS 15
III. SIMULATION STUDY: TUNING OF M-C-K PARAMETERS 22
A. Simulation Setting 22
B. Implementation of the ILM 22
C. Simulation Results 25
1) Simulation with Ideal Cases 25
2) Simulation with Practical Factors 26
IV. EXPERIMENTAL STUDY: TUNING OF THE MECHANICAL STIFFNESS OF A ROBOTIC LEG 31
A. Hardware Configuration 31
B. Experimental Setup 31
C. Governing Equations 33
D. Implementation of the ILM 35
E. Control System Setting 36
F. Experimental Results 37
V. CONCLUSION 45
REFERENCE 47
Table I: Experimental Results 41
Fig. 1: The basic idea of iterative learning control and iterative learning... 13
Fig. 2: Comparison between ILC and ILM; conceptual block diagrams of... 16
Fig. 3: Iterative learning mechanical design procedure by the ILM 21
Fig. 4: The schematic of an 1DOF mass-spring-damper system 23
Fig. 5: Simulation results with ideal cases 27
Fig. 6: Simulation results with practical factors 30
Fig. 7: The mechanical design and the schematic of a joint actuation system 32
Fig. 8: Experimental results of the initial internal pressure tuning for a robotic... 39
Fig. 9: Experimental results in different sit-to-stand motions 42
Fig. 10: Iterative value of the Jcost(이미지참조) 44
초록보기 더보기
In most mechatronics applications, the best control performance cannot be obtained by only shaping a control input signal, because control is effective only within the performance range realizable by an actuator and a controller system in practice. Therefore, in order to obtain the best control performance, the mechanical system should be optimized considering the control performance first. It is, however, difficult to accurately expect the control performance without an actual experiment, because the control performance is dependent on not only the mechanical design parameters, but also various practical factors, such as input and output saturations of an actuator, heat problem, sensor limitations, and so on. For these rasons, a recursive mechanical parameter tuning process based on control experiments is proposed in this paper. Based on a set of control signals (e.g., a control input and a tracking error), the proposed mechanical parameter tuning method seeks a better mechanical design parameter for improving the control performance (i.e., to reduce the control input power). For verification of the proposed method, it is applied to case studies including simulations and experiments.
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