Constrained incremental predictive controller design for a flexible joint robot
Abstract
In this paper, an improved predictive control algorithm for controlling a typical nonlinear flexible-joint robot (FJR) with input constraint is proposed. The receding horizon algorithm, called generalized incremental predictive control (GIPC), utilizes both present and previous states rather than present states only. The GIPC algorithm includes the weighted difference of the current and the previous states and the summation of the control action increments. In order to illustrate the effectiveness of the proposed control strategy, it is implemented to the FJR and the results are compared with those of generalized predictive control (GPC). It is demonstrated that the proposed GIPC algorithm is more robust than the standard GPC method. Furthermore, the constrained GIPC algorithm using the quadratic programming removes instabilities caused by actuator saturation.
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Different from conventional linearization methods such as Taylor series and Carleman approaches, the linear system (13) is obtained without a concrete mathematical model and is effective in the whole set D_x, not only locally around equilibrium points.
“Different from conventional linearization methods, such as Taylor series [29], [47] and Carleman approaches [48], the linear system (13) is obtained without concrete mathematical model. Besides, it is effective in the whole set D x , not only locally around equilibrium points.”
From Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator (2022) · cites this paper as [29]
MPC formulated with an incremental model generates state predictions by using both present and previous states, but since the incremental system is obtained from linearization of the nonlinear system, the controller is effective only locally around equilibrium points.
“Alternatively, MPC is formulated with an incremental model, which generates the state predictions by using both present and previous states [29]. However, since the incremental system is obtained from linearization of the nonlinear system, the controller is effective only locally around equilibrium points.”
From Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator (2022) · cites this paper as [29]
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Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator
Wang, Y., Leibold, M., Lee, J., Ye, W., Xie, J., & Buss, M. (2022) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2022.3142629 · Cited by 52 · see citing works
- Authors
- Ghahramani, N. O., & Towhidkhah, F.
- Affiliations
- Amirkabir University of Technology (IR)
- Corresponding author
- Towhidkhah, Farzad
- ORCID
- Towhidkhah, Farzad
- Venue
- ISA Transactions
- Publisher
- Elsevier BV
- Published
- 2009-07
- Volume
- 48
- Issue
- 3
- Pages
- 321-326
- ISSN
- 0019-0578 (ISSN-L)
- OpenAlex
- W1990909450
- Cited by
- 38
- References
- 24
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 1 · 2022: 4 · 2021: 5 · 2020: 2 · 2019: 2 · 2018: 6 · 2015: 1 · 2014: 1 · 2013: 3 · 2012: 1
- FWCI
- 3.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ISA Transactions (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Dynamics and Control of Mechanical Systems
- Physical Sciences › Engineering › Mechanical Engineering › Control Systems in Engineering
- Keywords
- flexible joint robots, input constraints, actuator saturation, generalized predictive control, model predictive control, quadratic programming, robustness
- MeSH terms
- Algorithms, Computer Simulation, Elasticity, Feedback, Quality Control, Robotics, Linear Models
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