Adaptive Backstepping Sliding Mode Control Research Articles

Stability and vibration control of electrostatically excited functionally graded microresonator using nonlinear observer based sliding mode controller

The dynamic stability analysis of microsystems is an important aspect in understanding the critical operating regions under different excitations. Present study proposes an observer-based adaptive back-stepping sliding mode controller (ABSMC) model to control and stabilize an electrostatically excited functionally graded microresonator. The dynamic model of a microsystem subjected to random disturbances is derived using modified couple stress theory and Euler–Bernoulli’s beam model. The effective material properties are obtained from Mori-Tanaka scheme and the equations of motion are derived using Hamilton principle and solved by Galerkin’s method. A trained neural network estimator predicts the disturbances and the adaptive back-stepping sliding mode controller is designed for improving the system stability. The results of the proposed controller are compared with conventional sliding mode control (SMC) and proportional-derivative (PD) control solutions and it is found that ABSMC reduces settling time and input control force by 52.42% and 88.40%, respectively, with minimal chattering. The proposed control methodology effectively extends the travelling range of FG microsystems within and beyond the pull-in voltage.

Adaptive backstepping sliding mode control for single-inductor double-output boost converter

To reduce the cross-regulation and improve the dynamic response performance of a single-inductor double-output Boost converter, an adaptive backstepping sliding mode control (ABSMC) strategy is proposed in this paper. The nonlinear mathematical model of the converter is established, an output function satisfying the exact feedback linearization (EFL) is constructed based on the inverse system theory, and the linearization and decoupling of the model are implemented. Meanwhile, the problem of EFL heavily relying on an exact model is solved by combining backstepping control with sliding mode control. Furthermore, an adaptive reaching law is proposed to adjust the gain of the switching function, and the chattering phenomenon of sliding mode control is reduced. The stability of the system is proven according to the Lyapunov stability theorem. Finally, compared with the existing control methods, both the simulation results and experimental results verify the effectiveness and superiority of the proposed ABSMC strategy.

Adaptive Sliding Mode Trajectory Tracking Control of Unmanned Surface Vessels Based on Time-Domain Wave Inversion

In this work, we develop a trajectory tracking control method for unmanned surface vessels (USVs) based on real-time compensation for actual wave disturbances. Firstly, wave information from the actual sea surface is extracted through stereoscopic visual observations, and data preprocessing is performed using a task-driven point cloud downsampling network. We reconstruct the phase-resolved wave field in real time. Subsequently, the wave disturbances are modeled mechanically, and real-time wave disturbances are used as feedforward inputs. Furthermore, an adaptive backstepping sliding mode control law based on command filters is designed to avoid differential explosion and mitigate sliding mode chattering. An adaptive law is also designed to estimate and compensate for other external disturbances and inversion error bounds that cannot be computed in real time. Finally, the feasibility of the proposed control strategy is validated through stability analysis and numerical simulation experiments.

An adaptive backstepping sliding mode control strategy for the magnetorheological semi-active suspension

This paper proposes an adaptive backstepping sliding mode control strategy for addressing nonlinear issues in the semi-active suspension systems, such as uncertainties and external disturbances. Firstly, a hyperbolic tangent model is chosen for parameters identification of the magnetorheological (MR) damper, and a model for the semi-active suspension system is established. Secondly, a control strategy is designed by combining the backstepping and sliding mode control strategies, and adaptive methods are employed to mitigate external disturbances, enhance the robustness of the controller, and estimate system uncertainties. Finally, the stability and controllability of the closed-loop system are verified using Lyapunov theory. Under the road excitations of A-Class, B-Class, and speed bump, the dynamic characteristics of the passive control, backstepping sliding mode control, and adaptive backstepping sliding mode control strategies applied to the MR semi-active suspension are analyzed. The vertical acceleration of vehicle body, suspension dynamic deflection, and tire dynamic load are selected as evaluation indexes, the results indicate that this control strategy significantly improved the ride comfort and handling stability of the vehicle.

Application of dynamic desired headway based adaptive backstepping sliding mode control design to mixed traffic system

To optimize the mixed car-following behavior of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), an adaptive backstepping sliding mode control (ABSMC) strategy for longitudinal velocity and distance control model (namely, MCF-ABSMCM) is put forth. First, the variable desired headway model (VDHM) is designed based on the vehicle-to-vehicle and vehicle-to-infrastructure information. Also, an asymmetric stochastic car-following model (ASCM) is designed to realistically describe the stochastic factor as well as vehicle acceleration and braking behaviors in mixed car-following. Second, an adaptive cruise sliding mode control law with a backstepping approach is devised to precisely track the desired following distance based on the VDHM. After creating the basic diagram of the two-model traffic flow, the intelligent driver model (IDM) is used as a baseline to compare and analyze the traffic capacity. Lastly, the effectiveness of the MCF-ABSMCM approach is confirmed in terms of both the mixed traffic flow density wave evolution and the vehicle control strategy, and the MCF-ABSMCM is validated under various adversary inputs. The simulation results demonstrate that the mixed-flow queue can finish headway tracking and keep a safe distance when using the MCF-ABSMCM described. This control strategy has been validated in VISSIM to significantly improve the efficiency of traffic operations compared to IDM and ASCM.

Trajectory tracking of a mobile robot manipulator using fractional backstepping sliding mode and neural network control methods

ABSTRACT In this paper, a fractional adaptive backstepping sliding mode control system based on the Caputo-Fabrizio derivative for a 2-DoF mobile robot manipulator is designed and described. The fractional dynamics of the system are obtained using the Atangana-Baleanu derivative and the Euler-LaGrange formalism, which include external disturbances, parametric uncertainties, and nonholonomic constraints. A fractional sliding mode control strategy is designed for trajectory tracking tasks. In order to compensate for the chattering phenomenon, the proposed controller is combined with a fractional backstepping strategy. Additionally, the Caputo-Fabrizio derivative is introduced to further reduce the chattering effects and improve the driver performance. A fractional adaptive control law is used to cope with the parametric uncertainties, while adding robustness to external disturbances. To further improve system performance, a tracking control strategy based on a fractional neural network is added. For a comparative analysis, the conventional adaptive neural network backstepping sliding mode control is implemented. Numerical simulations are described and discussed to validate the effectiveness of our control system for trajectory tracking tasks under different operating conditions such as trajectory changes and external disturbances. Our proposed control system and its traditional counterpart were tuned using the Cuckoo optimization approach.

Speed Control of Switched Reluctance Motor using Adaptive Fuzzy Backstepping Sliding Mode Control

The Switched Reluctance Motors (SRMs), with many outstanding advantages, are gradually being widely applied in industries, households, and recommended in many works. However, most studies in the field of SRM control only concentrate on the mathematical model of the motor itself, neglecting the nonlinearity introduced by the inverter, which is responsible for switching between phases to drive the motor. This paper proposes an adaptive backstepping sliding mode control algorithm based on the SRM nonlinear model that combines both the motor and the inverter. Firstly, a backstepping sliding mode controller is used to track the desired value and ensure the stability of the system according to the Lyapunov criterion. Secondly, a fuzzy logic system is added to adjust the controller parameters to account for uncertainty and external disturbance, as well as to minimize the chattering phenomenon. Finally, a few simulation scenarios are performed to assess the effectiveness of the proposed controller. The simulation results clearly demonstrate that the proposed controller surpasses the previously published H infinity controller in terms of speed control quality for the combined nonlinear model of SRM. The proposed controller exhibits zero steady-state error, zero overshoot, and a short settling time of approximately 0.5 seconds. Moreover, the system's output quickly stabilizes when affected by disturbance noise.

Adaptive backstepping sliding mode subject-cooperative control for a pediatric lower-limb exoskeleton robot

The passive-assist rehabilitation mode with a fixed desired trajectory neglects the subject’s active involvement and degrades the therapeutic performance in case of partial muscle strength. Therefore, this study proposes a novel subject-cooperative control based on a variable admittance control scheme and a robust trajectory control scheme for a pediatric lower-limb exoskeleton robot. Initially, the system description and dynamic modeling are briefly explained. Thereafter, a neural-fuzzy–based variable admittance control ( nf VAC) is designed to incorporate a realistic subject-exoskeleton interaction and consider the subject’s active participation. Finally, a robust adaptive backstepping sliding mode control with rapid reaching law is used to handle parametric uncertainties and external disturbances. A stepwise selection of Lyapunov functions is utilized to address the stability of the trajectory control. The effectiveness of the proposed adaptive backstepping sliding mode–neural-fuzzy variable admittance control (ABSM- nf VAC) scheme is compared with two contrast control schemes, namely, adaptive backstepping-fixed admittance control (AB-FAC) and adaptive terminal sliding mode-fuzzy variable admittance control (ATSM- f VAC) for the active-assist mode with the effect of sudden reflex. Based on the numerical results, the suggested cooperative controller has demonstrated favorable tracking performance, compliant interaction, and safety aspects during gait training.

Adaptive backstepping sliding mode compensation control for electro‐hydraulic load simulator with backlash links

AbstractWhen a mechanical backlash between the loading piston rod and the steering gear piston rod connection of the electro‐hydraulic load simulator (EHLS), it will lead to poor loading accuracy. To solve this problem, an adaptive backstepping sliding mode controller based on the backlash feedforward compensation and state observer is proposed. First, the mechanism of surplus force generation in the EHLS is analyzed, and a mathematical model containing position disturbance and backlash nonlinearity is established based on the analysis results. Second, a state observer is designed to estimate the system state and the parameter adaptive law is designed to estimate the system parameters. Third, the adaptive backstepping method is combined with the non‐singular terminal sliding mode control (NTSMC) strategy to deal with the position disturbance and parameter uncertainty and to enhance the system's dynamic performance. Finally, a backlash feed‐forward compensator is engineered to restrain the detrimental effects caused by the backlash nonlinearity, and the simulation experimental results show that the designed new method can realize the precise loading of the EHLS under different working conditions, and the surplus force is suppressed better at the same time.

Enhancing pressure control of low-friction pneumatic actuator with eddy current damper via valve dead-zone compensation

This article addresses the high-performance pressure control of a pneumatic actuator that incorporates an air-bearing piston and an eddy current damper (ECD). After optimizing the pneumatic actuator, achieving precise pressure control relies on maintaining an accurate and stable airflow through the control valve. A terminal sliding mode adaptive control method (TSMAC) is proposed, proficiently integrating considerations for dead zone nonlinearities in the proportional valve and the effects of unmodeled disturbances in the pneumatic actuator. Using the opening area of the proportional valve as the input to the pneumatic system greatly simplifies the control process and reduces the workload required for valve parameter calibration. The Lyapunov-based stability analysis demonstrates that excellent asymptotic output tracking can be achieved with low steady-state error simply by knowing the boundaries of the valve opening area. The control effect of the proposed control strategy is compared with an adaptive backstepping sliding mode control method based on a linearly fitted proportional valve model on an established constant pressure control test bench. Experimental results under various loads and operating conditions illustrate the effectiveness of the proposed approach.

Adaptive fuzzy-neural network effectively disturbance compensate in sliding mode control for dual arm robot

In this study, an Adaptive Backstepping Sliding Mode Controller (ABSMC) is introduced based on the Radial Basis Function (RBF) neural network and a fuzzy logic modifier. The proposed method is used to control a Dual-Arm Robot (DAR) – a nonlinear structure with unstable parameters and external disturbances. The control aims to track the motion trajectory of both arms in the flat surface coordinate within a short time, maintaining stability, and ensuring that the tracking error converges in finite time, especially when influenced by unforeseen external disturbances. The nonlinear Backstepping Sliding Mode Control (BSMC) is effective in trajectory tracking control; however, undesired phenomena may occur if there are uncertain disturbances affecting the system or model parameters change. It is proposed to use a neural network to estimate a nonlinear function to handle unknown uncertainties of the system. The neural network parameters can be adaptively adjusted to optimal values through adaptation rules derived from Lyapunov's theorem. Additionally, fuzzy logic theory is also employed to adjust the controller parameters to accommodate changes or unexpected impacts. The performance of the Fuzzy Neural Network Backstepping Sliding Mode Control (FNN-BSMC) is evaluated through simulation results using Matlab/Simulink software. Two simulation cases are conducted: the first case assumes stable model parameters without uncertain disturbances affecting the joints, while the second case considers a model with changing parameters and disturbances. Simulation results demonstrate the effective adaptability of the proposed method when the system model is affected by various types of uncertainties from the environment

Adaptive sliding mode attitude control of quaternion model for aircraft based on neural network minimum parameter learning method

AbstractThis paper studied the back-stepping adaptive sliding mode control (SMC) attitude problem of quaternion aircraft model based on radial basis function (RBF) network approximation. Firstly, a sliding mode controller is designed based on the back-stepping method (BSM) for the nonlinear aircraft model. Secondly, a RBF network algorithm is designed to compensate for the unknown and uncertain parts of the aircraft system. RBF network has simple network structure and good generalisation ability, avoids lengthy and unnecessary calculations, realises adaptive approximation of unknown parts in the aircraft model, and through the adjustment of adaptive weights, the convergence and stability of the entire closed-loop system (CLS) are guaranteed. Finally, the anti-interference performance of the controller is verified by simulation of the actuator fault model. Our proposed method has all-right control performance indicated by the simulation results.

Adaptive backstepping fast terminal sliding mode control of dynamic positioning ships with uncertainty and unknown disturbances

Considering the complexity of the operating environment of marine ships, higher demands are placed on the positioning accuracy and positioning performance of Dynamic Positioning (DP) systems. This paper designs an Adaptive Backstepping Fast Terminal Sliding Mode Control (ABFTSMC) to solve the problems of low positioning accuracy, slow convergence, and poor anti-interference performance in ship positioning due to uncertainty of dynamic model parameters and unknown time-varying environmental disturbances. A Fast Terminal Sliding Mode Control (FTSMC) is combined with backstepping techniques to ensure the robustness of the system to uncertainties and disturbances. Meanwhile, an adaptive law is used to estimate the unknown uncertainty terms. Moreover, the Lyapunov stability criterion is used to prove the convergence and stability of the designed controller. Multiple simulation results show that ABFTSMC has better control performance compared with Adaptive Backstepping Control (ABC) and Adaptive Backstepping Sliding Mode Control (ABSMC). All the comparisons could fully verify the effectiveness and superiority of the proposed control method.

Robustness Study of a Modular Smart Transformer Based on a Combined Control Strategy under Disturbed Operating Conditions in the Smart Distribution Grid

With the large-scale integration of renewable energy sources (RES) and electric vehicles, the smart distribution grid (SDG) represents a promising alternative to power distribution systems. However, the conventional transformer (CT) is a passive device that cannot meet SDG requirements. In this context, the smart transformer (ST) based on modular architecture is considered an attractive alternative to CT since it provides multiple DC buses in medium voltage (MV) and low voltage (LV) grids. This article proposes a modular ST model including a cascaded H-bridge rectifier in the MV AC-DC stage, dual active bridge converters in the high-frequency DC-DC stage, and a four-leg inverter in the LV DC-AC stage for SDG applications. To improve its dynamic performance, an adaptive backstepping-sliding mode control is introduced. The proposed controller enables the automatic estimation of unknown parameters, especially in the presence of intermittent RESs, by the adaptive laws of the adaptive backstepping controller (ABC) and the improvement of the ST robustness under grid disturbances by introducing the discontinuous control of the sliding mode controller (SMC). The obtained simulation results prove the effectiveness of the selected ST model and the higher robustness of the proposed controller against grid disturbances, compared to the ABC and SMC.

Observer-Based Adaptive Backstepping Sliding-Mode Control of the Satellite Attitude with a Partially Filled Spherical Fuel Tank

Observer-Based Adaptive Backstepping Sliding-Mode Control of the Satellite Attitude with a Partially Filled Spherical Fuel Tank

An Adaptive Backstepping Sliding-Mode Control for Improving Position Tracking of a Permanent-Magnet Synchronous Motor With a Nonlinear Disturbance Observer

To provide reliable position control for a permanent-magnet synchronous motor (PMSM) under conditions of lumped disturbances such as external load torque fluctuation and system parameter variation, an adaptive backstepping sliding-mode control (ABSMC) with a nonlinear disturbance observer (NDO) is proposed. An ABSMC is a non-cascade technique that employs a position-current single-loop control structure rather than a conventional cascade control structure for vector control of the PMSM. This method uses Lyapunov theory to design a control law to ensure the motor position reaches a desired value in a finite period of time and consequently achieves a rapid transient response. The proposed ABSMC incorporates an adaptive convergence gain to avoid a large overshoot under point-to-point position command. The NDO with a nonlinear observer gain is proposed to estimate unknown disturbances and provide feed-forward compensation for the ABSMC, improving robustness and reducing steady-state position error. The proposed methods are applied to an industrial motor drive to confirm their validity in real-world operating environments.

Trajectory tracking control of remotely operated vehicle based on thruster dynamics

Abstract In order to reduce the impact of thruster dead zone on remotely operated vehicle (ROV) motion performance and ensure that it can complete trajectory tracking control under external interference. In this paper, the adaptive backstepping sliding mode control(ABSMC) method based on improved rapid power-to-power approximation law is proposed. Under the influence of external interference; ABSMC can quickly reduce the position error of the system, namely, shorten the approaching time. When the position error is small, the system can smoothly enter the sliding surface to eliminate chattering. A new thrust distribution method based on the pseudo-inverse method is proposed for the dead zone of a thruster. This method uses the pseudo-inverse method when the thrust is saturated. When all thruster thrusts are unsaturated, the thrust will remain outside the dead zone for a long time. Combined with the ABSMC method, the thrust output of the thruster is more smooth and more stable with small position errors. Finally, the effectiveness of the ABSMC method and the superiority of the new thrust distribution method is verified by simulation experiments.

Adaptive Finite-Time Backstepping Sliding Mode Control for PMSM System With Backlash

The rapid tracking stabilization problem of the servo system with gear backlash and unmatched unknown disturbance is investigated. In this article, a novel finite-time sliding mode surface with a faster convergence rate is proposed to improve the anti-interference of the gear transmission servo system. This surface not only can adaptively and dynamically regulate the integral capability together with disturbances and tracking error variations, but also does not create singularity issues. An adaptive fixed-time reaching law (AFTRL) is proposed, which performs a faster convergence rate and chattering-free performances. The adaptive mechanism ensures the continuous property of the control output and dynamically regulates the fixed-time convergence rate regardless of initial states. The dSPACE platform is employed to validate the excellent tracking performance of the gear transmission servo system.

Adaptive chaos synchronization of an attitude control of satellite: A backstepping based sliding mode approach

This paper uses adaptive backstepping sliding mode control to synchronize two satellite attitude dynamics with perturbing torques. The external perturbing torques induce chaotic motion with no control inputs. The proposed control system uses Lyapunov theory and Barbalat's Lemma to guarantee the asymptotic stability of the controlled system. Simulation results confirm the effectiveness of the proposed design.

Adaptive coupled attitude-servo control for moving mass flight vehicles with high-mass-ratio

Moving mass flight vehicles (MMFVs) with high-mass-ratio suffer from a more severe nonlinear attitude-servo coupling than the traditional configurations that are under particle moving mass hypothesis. Therefore, the coupled attitude-servo control problem is investigated in this paper, and an adaptive backstepping sliding mode controller with extended state observers and neural-networks (ABSMC-ENN) is proposed. Based on the formulated control model with strict-feedback form, a sliding mode control law (SMC) with exponential reaching rate and a nonsingular terminal sliding mode control law (NTSMC) are combined to perform the fast and robust tracking of the command angle-of-attack. Considering the disturbances and uncertainties existing in the coupled dynamics, two nonlinear extended state observers (ESOs) are utilized to estimate the total disturbances online for compensation. To further enhance the adaptiveness and dynamic response performances of the controller in various flight conditions, two radial basis function neural networks (RBFNNs) are exploited to optimize the switching gains of the controller in real-time with performance indexes being the functions of tracking errors. Closed-loop stability of the whole system is proved via Lyapunov methodology. Comparison simulation studies considering multiple disturbances, various flight conditions, and different controllers successfully demonstrate the superiority of the ABSMC-ENN in robustness and adaptiveness.