Compliant Force Control for Robots: A Survey
Abstract
1. Introduction
- A taxonomy and comparative review of passive, direct, and indirect force control methods.
- An in-depth analysis of advanced compliant force control strategies, emphasizing.
- Robots with complex mechanical structures (e.g., aerial, mobile, cable-driven, and bioinspired platforms).
- Intelligent control algorithms that integrate artificial intelligence, learning, and robust control techniques.
- A detailed discussion of sensorless force control approaches and the challenges associated with their implementation.
- A synthesis of emerging trends, including the evolution of robot morphology and the integration of multi-modal sensing and control methodologies.
2. Passive Compliant Force Control Methods and Applications
3. Direct Force Control
3.1. Hybrid Force/Position Control
3.2. Parallel Force/Position Control
4. Indirect Force Control
4.1. Impedance Control
4.2. Admittance Control
5. Advance Force Control
5.1. Advance Structured Robots Force Control
5.2. Intelligent Compliant Force Control
5.3. Sensorless Compliant Force Control
6. New Trends of Compliant Force Control for Robots
6.1. From the View of Robot Itself
6.2. From the View of Control Methodology
- Multi-modal Perception Fusion: Integrating data from force sensors, tactile arrays, vision systems, and proprioceptive feedback is essential for robust and context-aware interaction. This sensor fusion enhances the control system’s situational understanding and responsiveness.
- Real-time Learning and Adaptation: The ability to adapt on-the-fly to changing task requirements, user intentions, or environmental uncertainties is becoming increasingly critical, especially for human–robot collaboration scenarios. Techniques such as reinforcement learning, meta-learning, and adaptive observers are expected to play major roles.
- Physical and Energy-aware Behavior: Control strategies should account for energy consumption, thermal dynamics, and mechanical stress, enabling more sustainable and long-term deployment of robots, especially in mission-critical settings.
- Hardware-aware Control Design: Future methods must be co-designed with the physical structure of the robot, leveraging characteristics such as variable stiffness actuators, flexible joints, and series elastic elements to enhance compliance and robustness.
- Many current methods assume accurate dynamic models or rely heavily on offline-trained models that lack generalizability in novel conditions.
- Ensuring closed-loop stability while enabling rapid adaptation to disturbances and unstructured environments remains a fundamental research bottleneck.
- The absence of standardized benchmarks and reproducible testing scenarios makes it difficult to quantitatively compare the performance of different methods.
- Most compliant control strategies are still designed for single-robot, single-contact settings. Expanding them to multi-robot cooperative manipulation or whole-body interaction requires more sophisticated control architectures and coordination strategies.
7. Comparative Analysis, Application Guidelines, and Case Study of Compliant Force Control Strategies
7.1. Comparison and Guidelines for Compliant Force Control Strategies
7.2. Illustrative Case Study: Comparative Analysis of Compliant Force Control Methods in Peg-in-Hole Assembly
- Hybrid Force/Position Control: This method partitions the task space into orthogonal force and position subspaces. For the peg-in-hole task, axial insertion along the hole direction is governed by force control, while lateral alignment is handled by position control. When accurate environmental models are available (e.g., hole geometry and insertion depth), hybrid control can achieve submillimeter precision and force tracking errors below 0.5 N [124,125,126]. However, it is sensitive to geometric uncertainties or surface deformation, limiting its adaptability in unstructured environments.
- Parallel Force/Position Control: Unlike hybrid control, parallel controllers handle both force and position commands simultaneously within the same task space. In peg-in-hole tasks that involve flexible components or unknown compliance, this strategy can prioritize force control to ensure safe contact while allowing the position controller to adjust accordingly. Its strength lies in reduced dependency on accurate environmental modeling, but tuning priorities between force and position loops becomes more complex, particularly when high-stiffness contacts occur.
- Impedance Control: By regulating the manipulator’s dynamic stiffness and damping, impedance control allows natural interaction with uncertain contact forces during insertion. In cases where hole tolerances are variable or slight misalignments exist, adaptive impedance control can absorb insertion errors, reducing insertion force peaks and avoiding excessive binding [127,128]. The challenge lies in selecting appropriate impedance parameters in real-time to balance compliance and stability.
- Admittance Control: For scenarios where position sensing is highly accurate and force feedback is reliable, admittance control enables the robot to modulate its motion directly based on external forces. This is particularly useful in human-supervised or teleoperated insertion tasks, where the operator indirectly guides the robot through contact forces [129,130]. However, stiff environments with abrupt contact transitions may induce instability if the dynamic parameters are not appropriately tuned.
- Intelligent Control Approaches (Neural Networks, Reinforcement Learning, Fuzzy Logic): Emerging intelligent control methods offer adaptive learning capabilities to handle unmodeled dynamics, contact uncertainty, and real-time disturbance compensation. For example, reinforcement learning-based compliant control can iteratively optimize insertion policies through direct interaction, even when hole positions or contact forces are unknown. Neural network-augmented impedance controllers can compensate for nonlinear contact dynamics without requiring explicit physical models [131,132]. Despite their promise, intelligent methods face challenges related to training data efficiency, safety assurance during learning, and computational load.
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Control Method | Key Characteristics | Advantages | Limitations | Ideal Applications |
---|---|---|---|---|
Passive Compliance | Mechanical elasticity (e.g., springs and dampers); no active sensing. | Simplicity, low cost, robustness to high-frequency disturbances. | Limited tunability, low precision, nonlinear dynamics. | Assembly tasks (e.g., peg-in-hole), environments with predictable contact forces. |
Hybrid Force/Position | Decoupled force/position control in orthogonal subspaces. | Explicit force tracking, suitable for constrained tasks. | Requires exact environment geometry; unstable under parameter uncertainties. | Industrial assembly, deburring, polishing. |
Parallel Force/Position | Unified task-space control with force prioritization. | Robust to surface uncertainties; no need for selection matrices. | Complex tuning; potential instability under high stiffness. | Human–robot collaboration, mobile manipulation. |
Impedance Control | Regulates dynamic behavior (mass-spring-damper) to shape force-motion relationship. | Adaptability to unknown environments; stable interaction. | Performance depends on impedance parameter tuning. | Physical human-robot interaction (pHRI), rehabilitation robotics. |
Admittance Control | Inverse of impedance; modulates motion in response to forces. | Effective in soft environments; intuitive for human-guided tasks. | Prone to instability in stiff environments; relies on accurate force sensing. | Assistive robotics, cooperative manipulation. |
Intelligent Control | AI/ML-based (e.g., NNs, RL, and fuzzy logic) for adaptive parameter tuning. | Handles nonlinearities and uncertainties; self-learning capabilities. | High computational cost; requires training data. | Unstructured environments (e.g., search-and-rescue and agricultural robotics). |
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Zhu, M.; Gong, D.; Zhao, Y.; Chen, J.; Qi, J.; Song, S. Compliant Force Control for Robots: A Survey. Mathematics 2025, 13, 2204. https://doi.org/10.3390/math13132204
Zhu M, Gong D, Zhao Y, Chen J, Qi J, Song S. Compliant Force Control for Robots: A Survey. Mathematics. 2025; 13(13):2204. https://doi.org/10.3390/math13132204
Chicago/Turabian StyleZhu, Minglei, Dawei Gong, Yuyang Zhao, Jiaoyuan Chen, Jun Qi, and Shijie Song. 2025. "Compliant Force Control for Robots: A Survey" Mathematics 13, no. 13: 2204. https://doi.org/10.3390/math13132204
APA StyleZhu, M., Gong, D., Zhao, Y., Chen, J., Qi, J., & Song, S. (2025). Compliant Force Control for Robots: A Survey. Mathematics, 13(13), 2204. https://doi.org/10.3390/math13132204