Trajectory Tracking Control for Mobile Robotic Systems

Trajectory tracking control lies at the heart of enabling mobile robots to follow predetermined paths with precision and reliability. These systems encompass a wide range of platforms—from autonomous vehicles and agricultural tractors to warehouse carriers and underwater vehicles—each presenting unique kinematic and dynamic challenges. Central to the discipline is the formulation of error models that quantify deviations in position, orientation and velocity relative to a reference trajectory. Controllers must then drive these errors to zero, often under nonholonomic constraints that forbid certain instantaneous motions. A broad spectrum of techniques has emerged, including kinematic controllers that exploit simplified motion equations, dynamic approaches that account for inertia and actuator limits, and hybrid schemes that blend predictive optimisation with robust feedback elements. Advances in model predictive control have delivered real-time optimisation of control inputs under constraints, while sliding mode and backstepping designs have offered strong robustness against disturbances and model uncertainties. Fuzzy logic and adaptive observers have been introduced to cope with poorly known parameters, and intrinsically stable control laws have been developed for non-minimum-phase behaviours such as jackknifing in tractor-trailer arrangements. Practical applications demonstrate that modern trajectory tracking controllers can achieve centimetre-level accuracy in agricultural operations, safe high-speed navigation in urban settings and reliable manoeuvres of submersible vehicles in cluttered environments. The continued integration of learning-based modules and network-enabled sensing promises further gains in adaptability and resilience.

Research from Nature Portfolio

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Research from all publishers

Recent work in Frontiers in Plant Science introduced a fuzzy backstepping controller for tractor-trailer vehicles operating in agricultural fields. By embedding a fuzzy-tuned gain adaptation into a backstepping framework, the method achieved a 65 % reduction in lateral tracking error when following tight turns and cut the trailer’s time off-path by over one-third compared with baseline algorithms.

Agriculture journal reports a robust trajectory-tracking strategy that combines model predictive control at the posture level with sliding mode control at the dynamic level, augmented by a nonlinear disturbance observer. This double-loop structure ensures prescribed convergence rates and compensates for parameter uncertainties and external disturbances, yielding high tracking accuracy across diverse operating conditions.

IEEE/ASME Transactions on Mechatronics detailed an intrinsically stable model predictive control scheme designed to prevent jackknifing in tractor-trailer systems. The approach integrates input-output linearisation with a stability-guaranteed MPC corrective term, successfully constraining hitch-angle divergence during reverse motions. Experimental validation on one- and two-trailer prototypes confirmed enhanced safety and path-following performance under challenging manoeuvres.

Trajectory Tracking Control for Mobile Robotic Systems publication trend

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

Technical terms

Nonholonomic constraints: Motion restrictions that arise when certain velocity components cannot be arbitrarily assigned, typical in wheeled vehicles.

Kinematic model: A simplified representation of a robot’s geometry and motion relationships, neglecting inertial effects.

Dynamic model: A mathematical description incorporating forces, masses and inertia to predict vehicle response to control inputs.

Model predictive control (MPC): An optimisation-based strategy that computes control inputs by solving a finite‐horizon cost minimisation subject to system constraints.

Sliding mode control (SMC): A robust feedback method that drives system states to a predetermined manifold, offering insensitivity to matched disturbances.

Backstepping: A recursive control design technique that stabilises complex nonlinear systems by decomposing them into simpler subsystems.

Fuzzy logic: An inference framework that handles uncertainty by mapping linguistic rules to control actions, often used for gain scheduling.

Disturbance observer: A mechanism that estimates unmeasured external forces or modelling errors, enabling compensation in the control loop.

References

1. Fuzzy backstepping controller for agricultural tractor-trailer vehicles path tracking control with experimental validation. Frontiers in Plant Science (2024).
2. Robust Trajectory Tracking Control of an Autonomous Tractor-Trailer Considering Model Parameter Uncertainties and Disturbances. Agriculture (2023).
3. An Intrinsically Stable MPC Approach for Anti-Jackknifing Control of Tractor-Trailer Vehicles. IEEE/ASME Transactions on Mechatronics (2022).