Tracking Control Techniques for Mobile Robot Systems

Tracking control for mobile robot systems encompasses a diverse array of methods designed to ensure that autonomous platforms follow prescribed paths with high precision, robustness and adaptability. Fundamental distinctions arise between kinematic controllers, which govern the robot’s velocity profile to match a reference trajectory, and dynamic controllers that incorporate forces, torques and inertia to achieve more refined motion regulation. Nonholonomic constraints, such as those encountered in wheeled vehicles, pose mathematical challenges that have driven the development of specialised nonlinear methods. Sliding mode control and backstepping schemes offer robustness against model uncertainties and external disturbances, while model predictive control frameworks exploit finite‐horizon optimisation to respect input and state constraints. More recently, hybrid strategies integrating neural networks or fractional‐order controllers have emerged to compensate for unmodelled dynamics and improve transient behaviour. Across industrial inspection, logistics and service robotics, these techniques underpin safe navigation in cluttered environments, energy‐efficient operation and real‐time responsiveness. The global significance of tracking control is reflected in its application to aerial drones, planetary rovers and self‐driving vehicles, where reliable path following is paramount for mission success.

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Recent advances have demonstrated the efficacy of neural network–based adaptive controllers that combine online learning with model reference adaptation to mitigate parameter uncertainty and slip disturbances. These hybrid schemes employ a kinematic layer informed by neural estimation and a dynamic layer adjusted via adaptive laws, achieving rapid convergence to target trajectories in both simulation and real‐world tests. In parallel, adaptive fast nonsingular terminal sliding mode control has been formulated to deliver finite‐time convergence for nonholonomic wheeled robots. By designing a sliding surface free of singularities and tuning gains through Lyapunov analysis, these methods ensure robustness against external perturbations while guaranteeing a predefined settling time. Another strand of work merges recursive backstepping with fractional‐order PID regulators to balance rapid response and steady‐state accuracy. Through simultaneous tuning of integer and fractional dynamics—often via metaheuristic optimisation—such hybrid controllers reduce overshoot and improve energy efficiency during point-to-point motion under skidding conditions. Collectively, these contributions illustrate a trend towards controllers that seamlessly integrate learning, nonlinear stability theory and fractional calculus to meet the demands of complex operational scenarios.

Tracking Control Techniques for Mobile Robot Systems publication trend

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

Technical terms

Nonholonomic constraint: A motion restriction that cannot be expressed purely as a function of position coordinates, common in wheeled robots where lateral slip is prohibited.

Model predictive control (MPC): A control strategy that computes inputs by solving an optimisation problem over a finite future horizon and updates them at each timestep.

Sliding mode control (SMC): A robust control technique that forces system trajectories onto a predefined sliding surface to reject matched uncertainties.

Backstepping control: A recursive design methodology that uses Lyapunov-based steps to stabilise nonlinear systems in strict-feedback form.

Adaptive control: A class of controllers that adjust their parameters online to compensate for unknown or time‐varying system dynamics.

Fractional-order PID: An extension of the classical PID controller employing fractional calculus to provide additional tuning flexibility in both transient and steady‐state performance.

References

1. Neural Network-Based Adaptive Controller for Trajectory Tracking of Wheeled Mobile Robots. IEEE Access (2022).
2. Finite-time tracking control for nonholonomic wheeled mobile robot using adaptive fast nonsingular terminal sliding mode. Nonlinear Dynamics (2022).
3. A combined backstepping and fractional-order PID controller to trajectory tracking of mobile robots. Systems Science & Control Engineering (2022).
4. Nonlinear model predictive control for trajectory tracking of nonholonomic mobile robots. International Journal of Advanced Robotic Systems (2018).