Control Strategies for Underactuated Mechanical Systems
Summary
Underactuated mechanical systems are characterised by having fewer independent actuators than the number of degrees of freedom they possess. This underactuation offers advantages in weight, cost and energy consumption, but presents significant control challenges arising from nonholonomic constraints, coupling between actuated and passive coordinates, and external disturbances. Broadly speaking, control approaches divide into energy-shaping methods, feedback linearisation and manifold-based techniques. Energy-shaping and passivity-based control exploit the natural dynamics by modulating potential and kinetic energy to steer the system. Feedback linearisation, often combined with backstepping, aims to cancel nonlinearities to achieve a target linear behaviour on a subset of the state space. Sliding mode control and disturbance-observer schemes deliver robustness against model uncertainties and external perturbations, enforcing convergence to prescribed surfaces or equilibrium points. Adaptive and learning-based strategies—using neural networks or iterative update laws—gradually compensate unknown dynamics without detailed system models. Fault-tolerant and oscillatory-compensation techniques have been developed to maintain performance when joints become passive or actuators degrade. These advances are demonstrated on benchmark platforms such as inverted pendulums, underactuated manipulators and aerial vehicles, underscoring the practical significance of the field for robotics, aerospace and energy-efficient automation.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Control Strategies for Underactuated Mechanical Systems publication trend
The graph below shows the total number of articles in control strategies for underactuated mechanical systems across all publications each year (not limited to Nature Index journals).
Technical terms
Underactuated mechanical system: A system with fewer actuation channels than degrees of freedom, leading to passive coordinates that must be indirectly controlled.
Sliding mode control: A robust control technique that forces system trajectories onto a predefined switching surface and maintains them there despite uncertainties.
Disturbance observer: An algorithm that estimates external or unmodelled perturbations acting on a system so that the controller can compensate for them.
Iterative learning control: A method for improving control performance over repetitive tasks by updating inputs based on errors observed in previous iterations.
Model-free learning: A data-driven control approach that does not rely on an explicit mathematical model, instead adapting from experimental or operational data.
References
- Underactuated robotics: A review. International Journal of Advanced Robotic Systems (2019).
- A survey of underactuated mechanical systems. IET Control Theory and Applications (2013).
- Fast Terminal Sliding Control of Underactuated Robotic Systems Based on Disturbance Observer with Experimental Validation. Mathematics (2021).
- Design and implementation of fault-tolerant control strategies for a real underactuated manipulator robot. Complex & Intelligent Systems (2022).
- Fast Model-Free Learning for Controlling a Quadrotor UAV With Designed Error Trajectory. IEEE Access (2022).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.