Sliding Mode Control

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Sliding mode control designs a surface representing desired system behavior, then uses switching feedback to drive the system toward it and resist disturbances or model errors. Common variants include adaptive, terminal, integral, and super-twisting methods, used for robot tracking, contact control, formation stabilization, and aerospace guidance. Researchers also combine sliding-mode controllers with disturbance observers, neural models, safety filters, or admittance control, and evaluate them in simulations and hardware tests. A central challenge is balancing fast, robust convergence with chattering, noise sensitivity, and strict actuator limits.

Latest papers

14 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

How Tuning, Reference Preview, Actuator Modeling, and Fallback Policies Change Quadrotor Controller Comparisons: A Reproducible Simulation Study
Oct 26, 2026Xuan-Quy Dao, Le-Minh Nguyen, Ngoc-Bich LeQuadrotor Control Model Predictive Control
Eastern International University Binh Duong, Vietnam · Industrial University of Ho Chi Minh City Ho Chi Minh City, Vietnam
Nonlinear Control of Aerial Manipulator
Oct 25, 2026Sandesh ThapaAerial Robotics Aerial Manipulation
School of Mechanical and Aerospace Engineering Control, Robotics & Automation Lab (CoRAL), Spring 2017 Oklahoma State University Stillwater, Oklahoma
A Response to the Reply Presented in "A Fair Comparison of Sliding-Mode and Immersion-and-Invariance Observers"
Oct 14, 2026Luis Cervantes-Pérez, Víctor Santibáñez, Jesús Sandoval +2Sliding Mode Control Adaptive Control
Tecnológico Nacional de México / I. T. de La Laguna, Torreón, Coahuila, México · Tecnológico Nacional de México / I. T. de La Paz, La Paz, Baja California Sur, México · Departamento de Ingeniería Eléctrica y Electrónica / Instituto Tecnológico Autónomo de México, CDMX, Ciudad de México, México
Adaptive stabilization of a leaderless bearing-constrained formation with disturbances
Sep 6, 2026Minh Hoang Trinh, Chuong Van Nguyen, Quoc Van Tran +1Multi-Robot Formation Control Robust Control
East Asia University of Technology, Hanoi, Vietnam · Viterbi School of Engineering – Department of Aerospace and Mechanical Engineering, University of Southern California, CA, USA and Ghost Robotics, Philadelphia, PA, USA · Department of Mechatronics, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam +1
Robust Sliding Mode and Admittance Control of Underactuated Aerial Manipulators for Contact-Based Inspection
Aug 11, 2026Tareq Aziz Alqutami, Yvan Petillot, Matthew W. Dunnigan +1Robotic Inspection Admittance Control
Petroliam Nasional Berhad (PETRONAS), Malaysia · School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK
Active Stiffness Control of a Supportive Continuum Robot
Aug 4, 2026Rana Danesh, Farrokh Janabi-Sharifi, Farhad AghiliRobotic Control Robust Control
Department of Mechanical, Industrial, and Mechatronics Engineering, Toronto Metropolitan University, Toronto, ON, Canada · Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC, Canada
Time-delay Control Using a New Nonlinear Adaptive Law for Cable-Driven Robots
Jul 29, 2026Wenbo Gao, Yaoyao Wang, Jiawang Chen +2Robotic Control Robust Control
National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China · Donghai Laboratory, Zhoushan 316021, China · China Academy of Machinery Beijing Research Institute of Mechanical and Electrical Technology Co., Ltd., Beijing 100044, China
Hybrid Neural Network and Conventional Controller Approach for Robust Control of Highly Unstable Systems: Application to Tilt-Rotor Control
Jun 7, 2026Ali Kafili Gavgani, Amin Talaeizadeh, Aria Alasty +1Robust Control Feedback Control
Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
Terminal Time and Angle-Constrained Nonlinear Intercept Guidance
Jun 1, 2026Shivam Bajpai, Abhinav SinhaSliding Mode Control
Guidance, Autonomy, Learning, and Control for Intelligent Systems (GALACxIS) Lab, Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati, OH, 45221, USA
Bounded-Input True Proportional Navigation for Impact-Time Control
May 13, 2026Lohitvel Gopikannan, Shashi Ranjan Kumar, Abhinav SinhaFeedback Control Sliding Mode Control
Intelligent Systems & Control (ISaC) Lab, Department of Aerospace Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India · Guidance, Autonomy, Learning, and Control for Intelligent Systems (GALACxIS) Lab, Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, OH 45221, USA
BiPneu: Design and Control of a Bipolar-Pressure Pneumatic System for Soft Robots
May 12, 2026Yu Mei, Xinyu Zhou, Vedant Naik +2Robotic Control Robust Control
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots
Apr 27, 2026Shubham Sawarkar, P Sangeerth, S Saharsh +1Robot Trajectory Tracking Mobile Robotics
Centre for Cyber-Physical Systems and Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, India. · Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, India.
Robust Autonomous Control of a Magnetic Millirobot in In Vitro Cardiac Flow
Apr 2, 2026Anuruddha Bhattacharjee, Xinhao Chen, Lamar O. Mair +3Robotic Control Robotics
Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA. · Division of Magnetic Manipulation and Particle Research, Weinberg Medical Physics, Inc., Rockville, MD 20852, USA. · Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA.
CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance
Mar 3, 2026Hanyang Wang, Yiyang Liu, Jiawei Chi +3Diffusion Model Guidance Classifier-Free Guidance
Tsinghua University

Oct 26, 2026J/K move · Enter open · S save

How Tuning, Reference Preview, Actuator Modeling, and Fallback Policies Change Quadrotor Controller Comparisons: A Reproducible Simulation Study

Xuan-Quy Dao, Le-Minh Nguyen, Ngoc-Bich Le

Eastern International University Binh Duong, Vietnam · Industrial University of Ho Chi Minh City Ho Chi Minh City, Vietnam

Quadrotor Control Model Predictive Control Sliding Mode Control Linear Quadratic Regulator

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Quadrotor controller comparisons commonly report a single tracking error, although the resulting ranking also depends on tuning effort, reference information, actuator prediction, optimizer fallback, and command timing. We quantify these effects for cascade PID, hover LQR, boundary-layer sliding-mode control (BL-SMC), linear MPC with LQR fallback (LMPC-F), a preview-enabled LMPC-F, and a geometric

SO(3)

controller on a common nonlinear plant with rotor gyroscopic coupling, motor lag, and saturation. A primary study (556 episodes at 50Hz) combines matched twelve-candidate tuning, 400 paired evaluation episodes, and a 48-cell MPC factorial; a confirmation study (1462 episodes) repeats the evaluation on 50 fresh realizations with tuning budgets of 12, 24, and 48 candidates. A matched twelve-candidate tuning protocol changed the mean-RMSE ordering obtained with the fixed baseline settings, moving BL-SMC from last to first; the new ordering recurred on the fresh realizations, and a 48-candidate budget left it unchanged. Reference preview substantially reduced MPC tracking error and, on the figure-eight task, removed fallback activity. On an abrupt step, the outcome depended on the interplay between fallback policy, predictor, and actuator authority. Command timing also changed the outcome: replaying measured command-call delays terminated three of four nominal MPC configurations on a desktop computer and all four on a Raspberry Pi5, whereas capping the solver at 100 iterations removed every deadline miss in the tested nominal timing profiles. Controller rankings therefore depend not only on tracking accuracy but also on the tuning protocol, reference information, fallback behavior, and implementation timing. We propose a reporting checklist covering tracking accuracy, tuning effort, completion and failure rates, fallback activity, applied-input variation, and command timing.