Control Strategies for Mobile Inverted Pendulum Systems

Mobile inverted pendulum (MIP) systems, typified by two-wheeled self-balancing robots and emerging wheel-legged platforms, present intrinsically unstable, underactuated dynamics that demand sophisticated control solutions. Early efforts employed linear feedback schemes such as proportional–integral–derivative (PID) and linear–quadratic regulator (LQR) controllers to maintain upright posture and execute simple manoeuvres. However, real-world applications introduce nonlinearities, payload variations and external disturbances that exceed the capabilities of purely linear approaches. To address these challenges, researchers have developed adaptive and optimal controllers—among them model predictive control (MPC) for real-time trajectory optimisation, sliding mode control (SMC) to reject uncertainties with minimal chattering, and fuzzy-logic frameworks to emulate expert decision making under sensor ambiguity. Recent advances integrate state estimation techniques, notably Kalman filtering and disturbance observers, to compensate for measurement noise and unforeseen perturbations. Innovations such as gyroscopic stabilisers and active appendages have further expanded the operational envelope, enabling agile balance recovery on uneven terrain, efficient energy use and higher payload tolerance. The synthesis of these strategies continues to drive practical deployments in personal mobility, industrial automation and exploratory robotics.

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Research from all publishers

Recent work has demonstrated the benefits of gyroscopic precession control in two-wheeled robots by integrating a control moment gyroscope with an LQR scheme to mitigate recoil-induced instabilities. Simulations reveal that this hybrid approach yields superior static balance and dynamic stability with low actuator demand. In another development, a trajectory-tracking framework for wheeled inverted pendulum robots employs a hierarchical sliding mode controller in tandem with a nonlinear disturbance observer. By decoupling translational and tilt subsystems, this design achieves high-fidelity path following and robustness against unknown external forces. Additionally, robust navigational control in a sensed environment has been realised through a PD-PI architecture augmented by a Kalman filter, empowering self-balancing platforms to negotiate dynamic obstacles while maintaining connectivity with Internet of Things devices.

Control Strategies for Mobile Inverted Pendulum Systems publication trend

The graph below shows the total number of articles in control strategies for mobile inverted pendulum systems across all publications each year (not limited to Nature Index journals).

Technical terms

Mobile inverted pendulum (MIP): A dynamic system in which a mass is balanced above rolling wheels, requiring active control to prevent tipping.

Linear–quadratic regulator (LQR): An optimal control method that minimises a weighted sum of state and control effort via state-feedback gains.

Sliding mode control (SMC): A robust nonlinear strategy that forces system states to converge on and slide along a specified manifold, ensuring disturbance rejection.

Nonlinear disturbance observer (NDO): A real-time estimator designed to detect and compensate for external perturbations and model uncertainties.

Kalman filter: A recursive algorithm that fuses noisy measurements to produce statistically optimal estimates of system states.

References

1. Gyroscopic precession control for maneuvering two‐wheeled robot recoil stabilization. Journal of Field Robotics (2024).
2. Hierarchical Sliding Mode Control Combined with Nonlinear Disturbance Observer for Wheeled Inverted Pendulum Robot Trajectory Tracking. Applied Sciences (2023).
3. Robust Navigational Control of a Two-Wheeled Self-Balancing Robot in a Sensed Environment. IEEE Access (2019).