Impact of trajectory constraints on beailc and coilc convergence rates
Само за регистроване кориснике
2021
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Iterative learning control (ILC) is a suitable control method for industrial robot applications where they are required to execute repetitive tasks with high precision. In this paper, the impact of trajectory constraints on convergence rates of two constrained state space ILC algorithms is studied. Taking into account that in reality, robot’s operating space is limited, as well as the ILC’s transient error growth problem, the following constrained state space ILC algorithms were applied to the nonlinear 3DoF robot manipulator model: Bounded Error Algorithm (BEA) and Constrained Output Algorithm (CO). Both algorithms force the output trajectory to stay inside the predetermined boundaries defined by the safest distance from the desired trajectory and the coordinate limit, making their convergence rates closely dependent on the selection of these boundaries. Herein, tracking simulations of the desired trajectories defined in the generalized coordinates space were conducted in MATLAB and S...imulink environments, with the same feedback and learning parameters applied to both algorithms but with different sets of values for state space boundaries, hypercylinder radius eps for BEA and, the maximum and the minimum values of the joints’ generalized coordinates Qimax and Qimin for CO algorithm set in the way that the simulation results are comparable. Simulation results, analysis of the constraint parameters influence on the convergence rates, and their comparisons for the previously mentioned algorithms are shown later in this paper.
Кључне речи:
Robot control / ILC / bounded error / state space / constrained outputИзвор:
Book of abstracts, International Conference of Experimental and Numerical Investigations and New Technologies CNN TECH, 2021, 92, 2021, 92-92Издавач:
- Beograd : Inovacioni centar Masinskog fakulteta
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200105 (Универзитет у Београду, Машински факултет) (RS-MESTD-inst-2020-200105)
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200066 (Лола институт, Београд) (RS-MESTD-inst-2020-200066)
Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Dubonjac, Aleksandar AU - Lazarević, Mihailo AU - Vidaković, Jelena PY - 2021 UR - https://machinery.mas.bg.ac.rs/handle/123456789/4090 AB - Iterative learning control (ILC) is a suitable control method for industrial robot applications where they are required to execute repetitive tasks with high precision. In this paper, the impact of trajectory constraints on convergence rates of two constrained state space ILC algorithms is studied. Taking into account that in reality, robot’s operating space is limited, as well as the ILC’s transient error growth problem, the following constrained state space ILC algorithms were applied to the nonlinear 3DoF robot manipulator model: Bounded Error Algorithm (BEA) and Constrained Output Algorithm (CO). Both algorithms force the output trajectory to stay inside the predetermined boundaries defined by the safest distance from the desired trajectory and the coordinate limit, making their convergence rates closely dependent on the selection of these boundaries. Herein, tracking simulations of the desired trajectories defined in the generalized coordinates space were conducted in MATLAB and Simulink environments, with the same feedback and learning parameters applied to both algorithms but with different sets of values for state space boundaries, hypercylinder radius eps for BEA and, the maximum and the minimum values of the joints’ generalized coordinates Qimax and Qimin for CO algorithm set in the way that the simulation results are comparable. Simulation results, analysis of the constraint parameters influence on the convergence rates, and their comparisons for the previously mentioned algorithms are shown later in this paper. PB - Beograd : Inovacioni centar Masinskog fakulteta C3 - Book of abstracts, International Conference of Experimental and Numerical Investigations and New Technologies CNN TECH, 2021, 92 T1 - Impact of trajectory constraints on beailc and coilc convergence rates EP - 92 SP - 92 UR - https://hdl.handle.net/21.15107/rcub_machinery_4090 ER -
@conference{ author = "Dubonjac, Aleksandar and Lazarević, Mihailo and Vidaković, Jelena", year = "2021", abstract = "Iterative learning control (ILC) is a suitable control method for industrial robot applications where they are required to execute repetitive tasks with high precision. In this paper, the impact of trajectory constraints on convergence rates of two constrained state space ILC algorithms is studied. Taking into account that in reality, robot’s operating space is limited, as well as the ILC’s transient error growth problem, the following constrained state space ILC algorithms were applied to the nonlinear 3DoF robot manipulator model: Bounded Error Algorithm (BEA) and Constrained Output Algorithm (CO). Both algorithms force the output trajectory to stay inside the predetermined boundaries defined by the safest distance from the desired trajectory and the coordinate limit, making their convergence rates closely dependent on the selection of these boundaries. Herein, tracking simulations of the desired trajectories defined in the generalized coordinates space were conducted in MATLAB and Simulink environments, with the same feedback and learning parameters applied to both algorithms but with different sets of values for state space boundaries, hypercylinder radius eps for BEA and, the maximum and the minimum values of the joints’ generalized coordinates Qimax and Qimin for CO algorithm set in the way that the simulation results are comparable. Simulation results, analysis of the constraint parameters influence on the convergence rates, and their comparisons for the previously mentioned algorithms are shown later in this paper.", publisher = "Beograd : Inovacioni centar Masinskog fakulteta", journal = "Book of abstracts, International Conference of Experimental and Numerical Investigations and New Technologies CNN TECH, 2021, 92", title = "Impact of trajectory constraints on beailc and coilc convergence rates", pages = "92-92", url = "https://hdl.handle.net/21.15107/rcub_machinery_4090" }
Dubonjac, A., Lazarević, M.,& Vidaković, J.. (2021). Impact of trajectory constraints on beailc and coilc convergence rates. in Book of abstracts, International Conference of Experimental and Numerical Investigations and New Technologies CNN TECH, 2021, 92 Beograd : Inovacioni centar Masinskog fakulteta., 92-92. https://hdl.handle.net/21.15107/rcub_machinery_4090
Dubonjac A, Lazarević M, Vidaković J. Impact of trajectory constraints on beailc and coilc convergence rates. in Book of abstracts, International Conference of Experimental and Numerical Investigations and New Technologies CNN TECH, 2021, 92. 2021;:92-92. https://hdl.handle.net/21.15107/rcub_machinery_4090 .
Dubonjac, Aleksandar, Lazarević, Mihailo, Vidaković, Jelena, "Impact of trajectory constraints on beailc and coilc convergence rates" in Book of abstracts, International Conference of Experimental and Numerical Investigations and New Technologies CNN TECH, 2021, 92 (2021):92-92, https://hdl.handle.net/21.15107/rcub_machinery_4090 .