This paper examines the path-tracking control issue for tracked mobile robots (TMRs) operating in complex terrains, focusing on improving their autonomous operation capabilities. Considering the system’s complex dynamic model, environmental uncertainties, and non-linear characteristics, especially the phenomenon of track slippage, a dynamic model that incorporates track slippage is proposed. A sliding factor observer is then designed to estimate slippage parameters, ensuring the control system remains stable and accurate despite uncertainties. A hierarchical control architecture is introduced, with the upper-level controller using a kinematic model to generate desired rotational speed commands for the left and right drive wheels. The lower-level controller, operating on a dynamic model, adjusts motor torque to achieve these desired speeds. Utilizing sliding mode control strategies, combined with adaptive laws and nonlinear control methods, the controller effectively addresses the issue of high-frequency chattering arising from the use of signum functions, thereby enhancing the lifespan of actuators and overall system control performance. A comprehensive simulation and experimental setup for real TMR systems is established to validate the proposed control strategy. Results demonstrate that the control scheme effectively achieves trajectory tracking across various unstructured terrains, exhibiting strong robustness and stability.

The formation of a coal mine roadway cross-section is a primary task of the boom-type roadheader. This paper proposes an intelligent robust control scheme for the cutting head trajectory of a coal mine tunneling robot, which is susceptible to unknown external disturbances, system nonlinearity, and parameter uncertainties. First, the working conditions of the cutting section were analyzed, and a mathematical model was established. Then, a high-gain disturbance observer was designed based on the system model to analyze cutting loads and compensate for uncertainties and disturbances. A sliding mode controller was proposed using the backstepping design method, incorporating a saturation function control term to avoid chattering. The eel foraging optimization algorithm was also improved and used to tune the controller parameters. A simulation model of the system was developed for performance comparison tests. Finally, experimental verification was conducted under actual working conditions in a tunnel face, and the results demonstrated the effectiveness of the proposed control method.

Electro-hydraulic position servo systems are widely applied in aerospace, industrial automation, and robotics due to their high power density, rapid response, and precision. However, inherent nonlinearities, parametric uncertainties, and external disturbances pose significant challenges to accurate control. This paper proposes a robust extended Kalman filter (EKF)-based rolling horizon H-infinity control strategy for output feedback control of such systems. First, a state-space model of the electro-hydraulic position servo system is established, integrating unknown dynamics into a single disturbance term. Second, a robust EKF with adaptive structure is designed to dynamically adjust the covariance matrix by integrating motion model observations and sensor measurements, achieving accurate state and disturbance estimation with convergence proven through rigorous stability analysis. Then, based on locally linearized system models, the control problem is transformed into a linear matrix inequality (LMI) problem, reducing real-time computational complexity. Through comparative simulations and experiments, the proposed control method demonstrates superior performance over traditional PID, backstepping, and standard MPC controllers in position tracking accuracy, disturbance rejection capability, and control smoothness. Specifically, under measurement noise and external disturbances, the proposed method achieves smaller mean square tracking error (0.022) and maximum error (0.055) while maintaining acceptable computation time (27.3 ms). The research findings provide an effective solution for high-performance control of electro-hydraulic position servo systems and offer valuable reference for improving control performance of similar nonlinear uncertain systems.

Displacement measurement in hydraulic support systems faces unique challenges from rock burst-induced vibrations that introduce non-smooth stochastic disturbances characterized by Wiener processes. This paper addresses measurement uncertainty in harsh underground environments through a fixed-time control framework. A second-order stochastic nonlinear model is established using Itô calculus to rigorously capture sensor-induced uncertainties. A fixed-time backstepping controller is developed, employing a self-organizing radial basis function neural network to approximate uncertain nonlinearities, with a novel power augmentation term introduced to eliminate singularities while preserving fixed-time convergence. Closed-loop probabilistic stability is proven via stochastic Lyapunov theory. Experimental validation is conducted on a Beckhoff-controlled hydraulic test bench, where Wiener noise is injected into sensor feedback signals to emulate underground stochastic disturbances. Results demonstrate tracking errors within ±2 mm and settling times independent of initial conditions, confirming the effectiveness of the proposed method under realistic measurement uncertainties.