A Hybrid Kalman-Weighted Sliding Mode Observer for Sensorless Torque Estimation of Robotic Manipulators

PASSIVITY-BASED SLIDING MODE CONTROLLER/OBSERVER FOR A LIGHTWEIGHT ROBOTIC ARM

This paper presents a passivity-based sliding-mode controller-observer for a two-link lightweight robotic arm that accounts for structural flexibility and payload mass. The system’s dynamical model is obtained using the Euler-Lagrange formalism and the assumed modes method. The resulting mathematical model is highly nonlinear, with strong coupling between the rigid dynamics and the system's elastic behavior. To achieve accurate trajectory tracking with effective strain elimination, a full-order sliding-mode state observer is designed to estimate the state variables in the presence of parameter uncertainties and external perturbations. Then, the robust observer is combined with a passivity-based controller that uses the estimated states to achieve the desired trajectory, thereby improving system performance and robustness. The stability of the global system is demonstrated using Lyapunov theory and accounting for the passivity property, whereby the total energy is dissipated or stored within the system. The proposed controller/observer is evaluated using MATLAB/Simulink. Simulation results show good trajectory tracking with effective rejection of the external perturbations.

Adaptive SMC for Lower Limb Rehabilitation Robots Using a Sliding Mode Hyperbolic ESO

Adaptive Fuzzy Sliding-Mode Control for Trajectory Tracking of Six-Joint Robot Manipulators

This paper addresses the high-precision trajectory tracking control problem for robotic manipulators operating in uncertain environments by proposing a novel fuzzy adaptive gain-tuning sliding-mode control (FAGT-SMC) algorithm. While conventional sliding-mode control offers strong robustness against matched uncertainties, its fixed-gain switching mechanism inevitably induces severe chattering phenomena, causing actuator wear and performance degradation in practical implementations. To overcome this fundamental limitation, this paper designs an intelligent gain adaptation framework that dynamically regulates the sliding-mode switching gain through a fuzzy inference system. The control system structure integrates a nominal equivalent control component derived from the robotic dynamics model with an adaptively tuned discontinuous switching term. Theoretical analysis establishes global stability through Lyapunov-based methods, proving uniform ultimate boundedness (practical stability) of tracking errors under bounded uncertainties and residual fuzzy approximation errors. The proposed FAGT-SMC algorithm effectively balances robustness and control smoothness; therefore, numerical simulations demonstrate effectiveness for advanced robotic applications requiring both precision and adaptability in dynamic operating conditions.

Neural Network-Enhanced Super-Twisting Control for Industrial Robotic Manipulators via Time-Delay Estimation

Industrial robotic manipulators are often affected by uncertain nonlinear dynamics and limited measurable information. This paper proposes a time-delay-estimation-assisted neural super-twisting sliding mode control framework for uncertain robotic manipulators. The time-delay estimation mechanism reconstructs hard-to-measure physical quantities and provides training data for neural uncertainty estimation. Based on these data, a neural network is developed to approximate the unknown nonlinear dynamics, and its output is incorporated into a super-twisting sliding mode controller to compensate for residual errors and reduce chattering. Lyapunov analysis proves the asymptotic stability of the closed-loop system. Simulations and real-time experiments on an industrial robotic manipulator verify the effectiveness of the proposed method.

Adaptive Fixed-Time Terminal Sliding Mode Control for Robot Manipulators via Time-Delay Estimation

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.

Trajectory-tracking control of the UR10 manipulator based on radial basis function neural network and super-twisting sliding mode

Abstract. This paper proposes a composite control strategy combining a radial basis function (RBF) neural network and super-twisting sliding-mode control for high-precision trajectory tracking of the UR10 manipulator under parameter variations, nonlinear friction, and external disturbances. An RBF network is employed to approximate the lumped unknown nonlinear dynamics online, thereby reducing the equivalent disturbance upper bounds. Simultaneously, a super-twisting robust control term is introduced to compensate for approximation residuals and remaining disturbances, which ensures system robustness while effectively suppressing conventional sliding-mode chattering. Furthermore, a projection-based adaptive law is designed to guarantee the strict boundedness of the network weights. Based on Lyapunov theory, the finite-time convergence of the sliding variable and tracking error is proven. Simulations on a 6-degree-of-freedom UR10 manipulator – incorporating mass/inertia perturbations and Coulomb-viscous friction – demonstrate that the proposed method achieves high-precision tracking with small steady-state errors and smooth, chattering-free control torques, verifying its effectiveness and engineering applicability.