Sliding mode control based on U model for nonlinear discrete system with modeling uncertainties

2018 ◽  
Vol 22 (S3) ◽  
pp. 7471-7480
Author(s):  
Fengxia Xu ◽  
Xiaohui Song ◽  
Hongliang Ren ◽  
Shanshan Wang
Keyword(s):  
Sliding Mode Control ◽  
Discrete System ◽  
Sliding Mode ◽  
Mode Control ◽  
Modeling Uncertainties ◽  
Nonlinear Discrete System

Neural network–based sliding-mode control of a tendon sheath–actuated compliant rescue manipulator

2019 ◽  
Vol 233 (8) ◽  
pp. 1055-1066 ◽  
Author(s):  
Qingcong Wu ◽  
Xingsong Wang ◽  
Bai Chen ◽  
Hongtao Wu
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Position Control ◽  
Sliding Mode ◽  
Radial Basis Function Network ◽  
Tendon Sheath ◽  
Step Response ◽  
Mode Control ◽  
Control Scheme ◽  
Modeling Uncertainties

The novel contribution of this article is to propose a neural network–based sliding-mode control strategy for improving the position-control performance of a tendon sheath–actuated compliant rescue manipulator. Structural design of a rescue robot with slender and compliant mechanical structure is introduced. The developed robot is capable of drilling into the narrow space under debris and accommodating complicated configuration in ruins. Dynamics modeling and parameters identification of a compliant gripper with flexible tendon sheath transmission are researched and discussed. Moreover, the neural network–based sliding-mode control scheme developed based on radial basis function network is proposed to improve the position-control accuracy of the gripper with modeling uncertainties and external disturbances. The stability of the proposed control system is demonstrated using Lyapunov stability theory. Further experimental investigation including trajectory-tracking experiments and step-response experiments are conducted to confirm the effectiveness of the proposed neural network–based sliding-mode control scheme. Experimental results show that the proposed neural network–based sliding-mode control scheme is superior to cascaded proportional–integral–derivative controller and conventional sliding-mode controller in position-control application.

Sliding Mode Control of Mechanical Systems Actuated by Shape Memory Alloy

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Hashem Ashrafiuon ◽  
Vijay Reddy Jala
Keyword(s):  
Sliding Mode Control ◽  
Shape Memory ◽  
Sliding Mode ◽  
Equations Of Motion ◽  
Mechanical Systems ◽  
Constitutive Law ◽  
System Model ◽  
Control Law ◽  
Mode Control ◽  
Modeling Uncertainties

This paper presents a model-based sliding mode control law for mechanical systems, which use shape memory alloys (SMAs) as actuators. The systems under consideration are assumed to be fully actuated and represented by unconstrained equations of motion. A system model is developed by combining the equations of motion with SMA heat convection, constitutive law, and phase transformation equations, which account for hysteresis. The control law is introduced using asymptotically stable second-order sliding surfaces. Robustness is guaranteed through the inclusion of modeling uncertainties in the controller development. The control law is developed assuming only positions are available for measurement. The unmeasured states, which include velocities and SMA temperatures and stresses, are estimated using an extended Kalman filter based on the nonlinear system model. The control law is applied to a three-link planar robot for position control problem. Simulation and experimental results show good agreement and verify the robustness of the control law despite significant modeling uncertainty.

scholarly journals Adaptive Sliding Mode Trajectory Tracking Control for Unmanned Surface Vehicle with Modeling Uncertainties and Input Saturation

2019 ◽  
Vol 9 (6) ◽  
pp. 1240 ◽  
Author(s):  
Bingbing Qiu ◽  
Guofeng Wang ◽  
Yunsheng Fan ◽  
Dongdong Mu ◽  
Xiaojie Sun
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Dynamic System ◽  
Sliding Mode ◽  
Input Saturation ◽  
Unmanned Surface Vehicle ◽  
Adaptive Sliding Mode ◽  
Mode Control ◽  
Control Scheme ◽  
Modeling Uncertainties

In the presence of modeling uncertainties and input saturation, this paper proposes a practical adaptive sliding mode control scheme for an underactuated unmanned surface vehicle (USV) using neural network, auxiliary dynamic system, sliding mode control and backstepping technique. First, the radial basis function neural network with minimum learning parameter method (MLP) is constructed to online approximate the uncertain system dynamics, which uses single parameter instead of all weights online learning, leading to a reduction in the computational burdens. Then a hyperbolic tangent function is adopted to reduce the chattering phenomenon due to the sliding mode surface. Meanwhile, the auxiliary dynamic system and the adaptive technology are employed to handle input saturation and unknown disturbances, respectively. In addition, a neural shunting model is introduced to eliminate the “explosion of complexity” problem caused by the backstepping method for virtual control derivation. The stability of the closed-loop system is guaranteed by the Lyapunov stability theory. Finally, simulations are provided to validate the effectiveness of the proposed control scheme.

Robust Region-Tracking for Multi-Agent Systems Using Sliding Mode Control

2010 ◽  
Author(s):  
Mark Bacon ◽  
Nejat Olgac
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
Autonomous Agent ◽  
Moving Target ◽  
Multi Agent Systems ◽  
Target Region ◽  
Agent Systems ◽  
Mode Control ◽  
Modeling Uncertainties ◽  
Multi Agent

Control of autonomous agent swarms is studied for targeted flocking exercises. The desired decentralized control also requires robustness against modeling uncertainties and bounded unknown forces. In this analysis, we consider the task of robustly driving multiple agents to a moving ‘target region’, as repulsive interactions help spread out the agents. An unconventional form of sliding mode control is implemented to provide the robust attraction towards the region’s center. For robustness a ‘boundary layer’ is conceived, which corresponds to the desired target region. The attraction is intentionally softened inside this target region, allowing agents to create a final formation utilizing their repulsion forces. Examples are given for moving circular and elliptical regions which illustrate the effectiveness of the proposed strategy.

scholarly journals Adaptive Discrete Second-Order Sliding Mode Control With Application to Nonlinear Automotive Systems

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Mohammad Reza Amini ◽  
Mahdi Shahbakhti ◽  
Selina Pan
Keyword(s):  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Real Time ◽  
Sliding Mode ◽  
Second Order ◽  
Operating Conditions ◽  
Data Sampling ◽  
Mode Control ◽  
Modeling Uncertainties ◽  
Highly Nonlinear

Sliding mode control (SMC) is a robust and computationally efficient model-based controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. However, the implementation of the conventional continuous-time SMC on digital computers is limited, due to the imprecisions caused by data sampling and quantization, and the chattering phenomena, which results in high-frequency oscillations. One effective solution to minimize the effects of data sampling and quantization imprecisions is the use of higher-order sliding modes. To this end, in this paper, a new formulation of an adaptive second-order discrete sliding mode controller (DSMC) is presented for a general class of multi-input multi-output (MIMO) uncertain nonlinear systems. Based on a Lyapunov stability argument and by invoking the new invariance principle, not only the asymptotic stability of the controller is guaranteed but also the adaptation law is derived to remove the uncertainties within the nonlinear plant dynamics. The proposed adaptive tracking controller is designed and tested in real time for a highly nonlinear control problem in spark ignition (SI) combustion engine during transient operating conditions. The simulation and real-time processor-in-the-loop (PIL) test results show that the second-order single-input single-output (SISO) DSMC can improve the tracking performances up to 90%, compared to a first-order SISO DSMC under sampling and quantization imprecisions, in the presence of modeling uncertainties. Moreover, it is observed that by converting the engine SISO controllers to a MIMO structure, the overall controller performance can be enhanced by 25%, compared to the SISO second-order DSMC, because of the dynamics coupling consideration within the MIMO DSMC formulation.

scholarly journals Synchronization of an Uncertain Fractional-Order Chaotic System via Backstepping Sliding Mode Control

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Zhen Wang
Keyword(s):  
Sliding Mode Control ◽  
Fractional Order ◽  
Chaotic System ◽  
Sliding Mode ◽  
Control Approach ◽  
Mode Control ◽  
System A ◽  
Control Scheme ◽  
Modeling Uncertainties ◽  
Backstepping Sliding Mode Control

Backstepping control approach combined with sliding mode control (SMC) technique is utilized to realize synchronization of uncertain fractional-order strict-feedback chaotic system. A backstepping SMC method is presented to compensate the uncertainty which occurs in the slave system. Moreover, the newly proposed control scheme is applied to implement synchronization of fractional-order Duffing-Holmes system. The simulation results demonstrate that the backstepping SMC method is robust against the modeling uncertainties and external disturbances.

scholarly journals Design of a Fuzzy Optimization Control Structure for Nonlinear Systems: A Disturbance-Rejection Method

2021 ◽  
Vol 11 (6) ◽  
pp. 2612
Author(s):  
Samia Charfeddine ◽  
Attia Boudjemline ◽  
Sondess Ben Aoun ◽  
Houssem Jerbi ◽  
Mourad Kchaou ◽  
...  
Keyword(s):  
Sliding Mode Control ◽  
Feedback Linearization ◽  
Simulation Analysis ◽  
Sliding Mode ◽  
Fuzzy Optimization ◽  
Control Design ◽  
Polynomial Systems ◽  
Continuous Stirred Tank Reactor ◽  
Mode Control ◽  
Modeling Uncertainties

This paper tackles the control problem of nonlinear disturbed polynomial systems using the formalism of output feedback linearization and a subsequent sliding mode control design. This aims to ensure the asymptotic stability of an unstable equilibrium point. The class of systems under investigation has an equivalent Byrnes–Isidori normal form, which reveals stable zero dynamics. For the case of modeling uncertainties and/or process dynamic disturbances, conventional feedback linearizing control strategies may fail to be efficient. To design a robust control strategy, meta-heuristic techniques are synthesized with feedback linearization and sliding mode control. The resulting control design guarantees the decoupling of the system output from disturbances and achieves the desired output trajectory tracking with asymptotically stable dynamic behavior. The effectiveness and efficiency of the designed technique were assessed based on a benchmark model of a continuous stirred tank reactor (CSTR) through numerical simulation analysis.

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