Jul 2026· 2026 23rd International Conference on Ubiquitous Robots (UR)· pp. 313-320· 0 citations· 10 references
Computer Science
Abstract
Heterogeneous dual-robot collaborative systems offer significant operational flexibility but present coordination challenges due to kinematic mismatches. While Integral Sliding Mode Control (ISMC) effectively handles nonlinear dynamics, traditional fixed-gain ISMC suffers from a fundamental tradeoff between robustness and chattering suppression. To address this, this paper proposes a TD3-based Adaptive Acceleration-Level ISMC strategy. Unlike conventional velocity-level approaches, the proposed method operates at the acceleration level to generate smoother commands and employs a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent as a dynamic gain scheduler. By integrating a lookahead mechanism, the agent anticipates curvature changes and optimizes sliding mode parameters—switching gains (K1, K2), and integral gain (K3)—in real-time. This allows for aggressive disturbance suppression during maneuvers while minimizing control effort on straight paths. Simulation results in a high-fidelity ROS 2/Gazebo environment demonstrate that the proposed approach significantly reduces chattering and achieves superior tracking precision (lower RMSE and maximum error) compared to fixed-gain baselines.
Against the backdrop of rapid progress in industrial automation and intelligent manufacturing, this paper presents a hierarchical fusion control strategy that combines High- Order Sliding Mode Control (SOSM), deep learning, and Model Predictive Control (MPC) to address the critical bottlenecks of traditional Proportional-Integral-Derivativ (PID) control in dual-axis robotic arms, including initial trajectory deviation, residual vibration, and model uncertainty. The robotic arm is physically modeled using SolidWorks, imported into Sysplorer for dynamic simulation, and the control module is developed via Syslab. A well-designed sliding mode surface and exponential convergence law are adopted to realize robust SOSM control, while a dual-layer Long Short-Term Memory (LSTM) network is introduced to compensate for model uncertainties such as friction and vibration. A hierarchical control architecture is constructed: the upper layer uses MPC for 50 ms global trajectory planning, and the lower layer applies SOSM for real-time tracking with a 0.01 s sampling period, balancing global optimization and real-time response. Experimental results confirm that the proposed strategy outperforms conventional PID control in trajectory tracking precision, dynamic response, and robustness, providing a high-precision and robust control scheme for industrial applications. This work contributes to the technological innovation and engineering application of control system theory, robotics, artificial intelligence, and automation technologies, enriches the technical reserve of cross-field integration, and provides a reference for academic exchanges, technical demonstrations, and industrial cooperation in related cutting-edge fields, so as to boost global knowledge sharing and collaborative innovation.
Bilateral teleoperation systems that include joint flexibility better reflect real robotic systems used in surgery, space, and rehabilitation. However, joint flexibility together with time-varying communication delays makes it difficult to maintain stable and coordinated motion between the master and slave robots. To address this, we propose a hybrid control method that combines a stable Proportional-plus-Damping (P+d) controller with a model-free deep reinforcement learning agent based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The P+d controller provides basic stability under bounded delays, while the learning agent adjusts and tunes the remote-side proportional and damping gains in real time to reduce vibrations and improve tracking. Stability is guaranteed for bounded time-varying delays using Lyapunov-Krasovskii analysis. The approach provides a practical solution for teleoperation systems facing both joint flexibility and uncertain network delays.
Armin Attarzadeh, Mohammadali Ghaemifar, A. Khanzadeh et al.· arXiv.org· 2 citations
This study addresses the trajectory tracking control problem for an underactuated hovercraft subject to additive bias and multiplicative loss-of-effectiveness thruster faults under environmental disturbances. In these systems, actuator degradation structurally breaks the differential flatness mapping, driving nominal controllers to generate control actions that induce severe actuator saturation and cause instability. To resolve this challenge, a hierarchical physics-informed neural adaptive control (PINAC) framework is proposed. First, a gated-recurrent-unit physics-informed neural observer (PINO) is designed to isolate thruster faults from exogenous hydrodynamic disturbances. Second, a constrained Safe-TD3 reinforcement learning agent functions as a supervisor, computing an online dilation factor to slow down the mission timeline, thereby reconfiguring the reference trajectory to accommodate degraded actuator boundaries. Third, a low-level non-singular terminal sliding mode (NTSM) controller is implemented as a tracking-guarantee layer. Unlike classical asymptotic schemes where convergence is only achieved as time approaches infinity, or finite-time controllers where the settling time depends on the initial state, the proposed PINAC framework guarantees practical fixed-time stability, ensuring that the settling-time bound is independent of initial conditions. Simulation results demonstrate that the designed controller prevents actuator saturation, provides smooth trajectory adjustment, and reduces tracking errors under severe composite faults.
Shafqat Ali, Aamir Mehmood, Faiza Iftikhar et al.· Journal of Marine Science an...· 0 citations
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.
Jianzhen Zhang, Helin Wang, Kun Wei· Processes· 0 citations
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.
Accurate sensorless external force estimation is crucial for physical human-robot interaction. To address the challenge that existing momentum-based sliding mode observers face in simultaneously achieving fast dynamic response and effective chattering suppression, this paper proposes a Kalman-weighted adaptive second-order sliding mode observer (HKW-SOSMO). This method utilizes the discrete Riccati equation to compute the joint posterior covariance in real time, employing it as a dynamic weight to modulate the sliding mode switching gain. The gain is adaptively amplified in regions with sudden friction changes, while it decreases in steady-state regions as the covariance contracts. Based on Lyapunov theory, this paper proves the finite-time convergence of this variable-gain system. Simulation results demonstrate that the proposed method effectively resolves the trade-off between dynamic response and chattering, thereby significantly enhancing estimation accuracy.
Jia-Wei Sun, Chao Peng, Jian-Xiao Zou et al.· 2026 IEEE International Conf...· 0 citations
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