TL;DR
Simulations encompassing aggressive manipulator motion with payloads, abrupt external disturbance injection and removal, model mismatches, composite sensor-noise corruption, and heavy-object transportation with additional position feedback noise validate the framework’s superiority in disturbance estimation accuracy and robust anti-disturbance performance.
Intelligent vehicle path tracking is challenged by uncertain disturbances, such as modeling inaccuracies and external environmental influences, which will significantly compromise both the path tracking accuracy and stability. To address this, this paper proposes a fixed-time prescribed-performance (FTPP) path tracking control method based on adaptive neural network disturbance estimation. Firstly, a radial basis function neural network with an online-updated adaptive law is developed for real-time estimation of uncertain disturbances, effectively compensating for their impact within the control model. Subsequently, a backstepping controller with FTPP is designed by integrating a composite dynamic surface control method with finite-time control techniques. This approach not only enhances the system convergence rate but also mitigates the derivative explosion problem inherent in traditional backstepping, yielding a control law with adaptive disturbances compensation for precise steering control. Finally, based on Lyapunov stability analysis, the boundedness of the closed-loop signals is established under the given assumptions, and the lateral path tracking error is shown to remain within the prescribed-performance bounds under feasible initial conditions. CarSim-Simulink-based co-simulation results validate the effectiveness of the proposed control method in improving both path tracking accuracy and stability.
With the rapid development of modern industry, robotic manipulators are required to achieve increasingly high trajectory-tracking accuracy and robustness in practical applications. To enhance tracking performance under complex operating conditions, this paper proposes an Adaptive Model Predictive Control with Sliding-Mode Robust Compensation (AMPC–SMC) scheme that integrates an adaptive mechanism with sliding-mode control theory. First, a dynamic model of the manipulator is established, and parameter linearization is employed to transform the nonlinear dynamics into a linearly parameterized form with unknown parameters. Second, an adaptive law is derived based on Lyapunov stability theory to update the model parameters online, thereby mitigating the adverse effects of parametric perturbations and external disturbances on tracking accuracy. Building on this, a receding-horizon optimization strategy is introduced by formulating a quadratic cost function that penalizes both tracking errors and control effort, and the optimal control input is obtained by solving the resulting optimization problem. Meanwhile, a sliding-mode term is incorporated as a robust compensator to eliminate residual tracking errors. Finally, the desired trajectory is generated via point-to-point path planning in Cartesian space, and the proposed method is validated on a real six-degree-of-freedom robotic manipulator. Comparative experiments against conventional model predictive control (MPC) and traditional sliding-mode control (SMC) demonstrate that the proposed AMPC-SMC controller achieves remarkably superior tracking performance compared with the conventional MPC and SMC controllers. In terms of tracking accuracy, the mean absolute errors (MAE) of Joint 2, Joint 4 and Joint 5 under AMPC-SMC are reduced by 91.7%, 92.1% and 86.1% respectively relative to MPC, and decreased by 76.1%, 77.3% and 85.1% compared with the standalone SMC controller.
This paper addresses the path tracking control problem for intelligent vehicles subject to parametric uncertainties, unmodeled dynamics, and external disturbances. A composite learning-based finite-time nonsingular terminal sliding mode control (CL-FNTSMC) strategy is proposed. Unlike conventional adaptive sliding mode controllers that rely solely on tracking errors for parameter update—and thus require the restrictive persistent excitation condition-the proposed scheme incorporates a serial–parallel estimation model to construct prediction errors, which together with tracking errors drive a composite learning law. This mechanism ensures accurate online estimation of unknown parameters under the significantly weaker interval excitation condition. A nonsingular terminal sliding surface, constructed with a continuously differentiable nonlinear function, guarantees finite time convergence while inherently avoiding singularity. Furthermore, a nonlinear disturbance observer is integrated to estimate and compensate for lumped disturbances in real time, substantially enhancing robustness. Rigorous Lyapunov-based analysis establishes the practical finite time stability of the closed loop system. Comprehensive comparative simulations under aggressive disturbances and significant parametric uncertainties demonstrate that the proposed CL-FNTSMC achieves superior tracking accuracy, faster convergence, and markedly improved disturbance rejection compared with conventional NTSMC, adaptive fast NTSMC, and PID controllers. The results confirm that the proposed framework offers an excellent balance of fast transient response, high steady-state precision, and strong robustness.
An adaptive proximate fixed-time terminal sliding mode control (FTTSMC) based on time-delay estimation (TDE) is proposed to ensure high-precision trajectory tracking of robot manipulators subject to unknown dynamics and external disturbances. The controller employs TDE to reconstruct system dynamics online, requiring only the inertia matrix bounds rather than full precise nominal models. Crucially, it replaces the conventional constant bound assumption for the TDE error with a robust state-dependent one, thereby enhancing robustness against discontinuous disturbances. Rigorous Lyapunov stability analysis confirms the fixed-time convergence of the sliding variable and the proximate fixed-time convergence of the tracking error, providing explicit upper bounds on the convergence time. Comparative simulations and experiments on a SCARA robotic platform demonstrate that the developed strategy maintains transient performance comparable to baseline fixed-time approaches while achieving superior steady-state accuracy. Characterized by a compact structure and low computational complexity, the proposed controller exhibits strong potential for high-performance real-time robotic applications.
This study presents an adaptive sliding mode control approach for trajectory tracking of a quadrotor unmanned aerial vehicle functioning under external interference and kinematic unpredictability. Linear and rotational motion equations formulated in inactive local coordinate system frames are developed with the aid of a nonlinear six-degrees-of-freedom quadrotor dynamic model. Through mitigating excessive switching activity, reliability is improved. Here, the proposed controller integrates sliding mode control with bounded adaptive switching gain factors and boundary-layer smoothing. The operational design is applied within a sequential outer-loop/inner-loop structure for linear and orientation control. The conventional sliding mode control, alongside the proportional derivative control, which employs MATLAB/Simulink R2024a simulations while being interference-affected with an unknown performance set-up, is deployed in this work to relatively appraise the proposed ASM controller. The assessment involves three-dimensional trajectory, control input characteristics, tracking error analysis, adaptive gain growth, and chattering analysis with quantitative performance metrics. The computational output revealed that the proposed ASMC attained superior tracking performance with limited oscillation and level control action. The controller achieves a total RMSE of approximately 0.38 m and a lower aggregate tracking error when using the conventional SMC and PD controllers under equivalent conditions. Furthermore, the adaptive gain mechanism successfully lowers chattering while maintaining robustness against interferences, a large amount of ambiguity, and inertial imbalance with signal noise. The results validate that the proposed ASMC delivers a functional balance between robustness, control smoothness, and tracking accuracy alongside execution homogeneity for autonomous quadrotor UAV trajectory tracking in unsettled and unstable environments.
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