Adaptive Terminal Sliding Mode Control Research Articles

Adaptive Terminal Sliding-Mode Synchronization Control with Chattering Elimination for a Fractional-Order Chaotic System

In this paper, an adaptive terminal sliding-mode control (ATSMC) method is proposed for the synchronization of uncertain fractional-order chaotic systems with disturbances. According to the sliding-mode control theory, a non-singular sliding surface is constructed. To overcome the chattering problem of ATSMC, a smooth term is used in the controller. In order to reduce the estimation error of an uncertain parameter, adaptive laws are designed to adjust the amplitude of the continuous function. Based on the Lyapunov stability theory, a stability analysis of the error system is performed to ensure that the tracking error eventually converges to the origin. The effectiveness and applicability of the proposed control strategy are verified using the simulation results.

Dynamic modeling and adaptive finite-time control of cable-driven parallel robots for ship cleaning

ABSTRACT Ship cleaning, often manually performed by numerous workers, is crucial in the shipping industry. This study introduces a marine cable-driven parallel cleaning robot (MCDPCR) to enhance cleaning security and efficiency. To investigate trajectory tracking under the moving base, the dynamic model of MCDPCR is established, and the optimisation of cable forces is considered. Then, an adaptive terminal sliding mode controller is designed, integrating the discontinuous sign function term to obtain a continuous actual control law and thus a chattering-free response. Meanwhile, the uncertainty is compensated by an adaptive method. The stability of the control strategy is analyzed using the Lyapunov stability theory. Finally, the effectiveness of the proposed control strategy is verified by several simulation scenarios. The results show that the controller has a fast global convergence speed, high tracking accuracy, and strong robustness. The method can provide a valuable reference for developing automated marine cleaning equipment.

Adaptive terminal sliding mode control for USV-ROVs formation under deceptive attacks

This work investigates the cooperative formation control problem of unmanned surface vehicle-remotely operated vehicles (USV-ROVs) subject to uncertainties under deceptive attacks. In the control design, with the utilization of the desired formation as well as the geometric position between USV and ROVs, a geometric transformation approach is developed and a geometric constraint relationship of governing formation positions is derived. Under the terminal sliding mode control (TSMC) design framework, a novel terminal sliding surface is crafted to circumvent the singularity issue. To further bolster robustness, using the sliding mode damper concept, a variable damping reaching law is devised. To refrain from the effectiveness of attacks and uncertainties, the adaptive technique is integrated into the TSMC framework. To save the communication resources, an event-triggering mechanism is established between the distributed controller and ROVs. Then, an event-triggered adaptive finite-time cooperative formation control scheme is developed for the USV-ROVs. The Lyapunov theory analysis shows that the cooperative formation control issue of USV-ROVs is realized and the deceptive attack can be suppressed efficaciously. The simulation, comparison, and quantitative analysis demonstrate the relative effectiveness and superiority of the developed scheme.

Robust adaptive iterative learning control for high-speed trains under non-strictly repeated conditions

In this paper, the speed and position control of high-speed trains (HSTs) under non-strictly repeated conditions is investigated. Considering the modeling accuracy and control cost, a multi-particle dynamics model is established in a single coordinate system. Based on this model, a novel robust adaptive iterative learning control (RAILC) method is proposed. The Lyapunov function is established to prove that the system state converges in a finite time, and the composite energy function (CEF) is constructed to prove that the control error approaches zero along the iteration axis. The application value in engineering practice of the proposed RAILC is verified by two simulation tests. Existing adaptive iterative learning control (AILC) and adaptive terminal sliding mode control (ATSMC) are introduced as control groups to show the system convergence of the RAILC on the iteration axis and time axis.

Reinforcement learning robust nonlinear control of a microgrid with hybrid energy storage systems

The parameter variations in the microgrid models including internal and external faults, modelling uncertainties, unexpected changes in load or disturbances and harmonic currents are unavoidable in a microgrid. Here, a hybrid energy generation system consisting of Proton-Exchange Membrane Fuel Cells)PEMFC(/PhotoVoltiaic)PV (/Battery Energy Storage System)BESS(is considered. A new Backstepping Fractional order Adaptive Terminal Sliding Mode Control (BFATSMC) based on Reinforcement Learning (RL) is designed to soar robustness and disturbance rejection for an autonomous microgrid. To verify the performance of the BFATSMC a comparative study is investigated and the proposed method is applied to an islanded microgrid. In the presence of nonlinear and unbalanced loads with harmonic, unmodeled dynamics, and parametric uncertainties, the proposed method has successfully controlled the voltage.

Adaptive terminal sliding mode control of a non-holonomic wheeled mobile robot

Adaptive terminal sliding mode control of a non-holonomic wheeled mobile robot

Fuzzy Adaptive Nonsingular Terminal Sliding Mode Control of a Miniature Helicopter

This paper presents a fuzzy adaptive terminal sliding mode control for an unmanned miniature helicopter including uncertainties and external disturbances, and a terminal sliding surface is utilized to provide faster convergence. Due to the fact that the presented controlled system is facing a singularity problem, the controlling structure is developed to a nonsingular one. In the proposed controlling structure, a continuous nonsingular terminal sliding mode control is combined with an adaptive learning algorithm and fuzzy logic system to estimate the uncertainties; and the parameter adaptation law is obtained based on the Lyapunov stability theorem. Analytical results show that the proposed approach enables a faster and more accurate tracking performance as compared to recent controlling methods.

Correction to: Novel neural adaptive terminal sliding mode control for TCP network systems with arbitrary convergence time

Correction to: Novel neural adaptive terminal sliding mode control for TCP network systems with arbitrary convergence time

Finite-time robust speed control of synchronous reluctance motor using disturbance rejection sliding mode control with advanced reaching law.

In recent years, there has been a significant focus on synchronous reluctance motors (SynRM) owing to their impressive efficiency and absence of magnetic material. Although the SynRM shows great potential for use in electric vehicles, its widespread adoption is limited by unmodeled dynamics and external disturbances. Moreover, the uncertainty factor significantly restricts SynRM's peak efficiency and superior control performance, leading to an unjustifiable current loop reference command. To address these issues, this work presents various new research contributions which focus on the robust control of SynRM to optimize performance through the novel reaching law-based sliding mode control. Initially, a novel advanced sliding mode control reaching law (ASMCRL) with adaptive gain is proposed, to enhance the acceleration of the system state reaching the sliding surface. After that, an extended state observer (ESO) is designed to estimate and compensate for the overall disturbances of the system. Finally, the ASMCRL and ESO are integrated to design two nonlinear controllers namely, the disturbance-rejection sliding mode controller (DRSMC) and the disturbance-rejection sliding mode speed regulator (DRSMSR) for SynRM. The proposed DRSMSR eliminates the steady-state error and eradicates inherent chattering in DRSMC. Moreover, this yields a system trajectory that converges to a predetermined proximity of the sliding surface, irrespective of any lumped disturbances. The steady-state error of DRSMSR is less as compared to DRSMC. Furthermore, the speed response of this technique is 22.62% faster as compared to the state-of-the-art finite-time adaptive terminal sliding mode control. Additionally, the asymptotic stability of the proposed system is validated using Lyapunov's theorem. Thus the experimental results demonstrate the effectiveness and robustness of the proposed approach.

Taguchi Method for Analysing Sliding Surface Parameters of an Adaptive Terminal Sliding Mode Controller for an Articulated Robot

Taguchi Method for Analysing Sliding Surface Parameters of an Adaptive Terminal Sliding Mode Controller for an Articulated Robot

Trajectory tracking control of a pneumatically actuated continuum manipulator in the presence of obstacles by using terminal sliding mode control

This paper proposes an efficient trajectory planning and dynamic tracking controller scheme for a pneumatic continuum manipulator under the effect of material hysteresis. First of all, generalized governing nonlinear dynamic equations in the form of partial differential equations for pneumatic continuum manipulator dynamics are developed by using discrete Cosserat-rod theory, where the manipulator material hysteresis is modeled by using a fractional order Bouc–Wen model. Then, the trajectory planning for the end-effector of a continuum manipulator is proposed, which accounts for the static obstacles in the workspace and the Jacobian singularity. Subsequently, an adaptive terminal sliding mode controller for the joint space control combined with a simple PI controller for task space control is proposed. The proposed controller guarantees exponential convergence of the manipulator tip positional error in finite time, even in the existence of external disturbances and model uncertainties, without any need for prior knowledge of their upper bounds. Finally, the proposed controller is applied to a two-segment continuum manipulator, the trunk of Robotino-XT, through numerical simulations and the performance gain over two controllers proposed in the literature for similar pneumatic continuum manipulators is demonstrated.

Novel Explicit Reference Governor-Based Adaptive Terminal Sliding Mode Control Design for Reentry Vehicles Equipped with Strapdown Seeker

Novel Explicit Reference Governor-Based Adaptive Terminal Sliding Mode Control Design for Reentry Vehicles Equipped with Strapdown Seeker

Novel neural adaptive terminal sliding mode control for TCP network systems with arbitrary convergence time

Novel neural adaptive terminal sliding mode control for TCP network systems with arbitrary convergence time

Estimation‐Based Event‐Triggered Adaptive Terminal Sliding Mode Control without Pressure Sensors for a Polymer Electrolyte Membrane Fuel Cell Air Feeding System

Appropriate control for a polymer electrolyte membrane fuel cell air feeding system is vulnerable to load current disturbance, parameter uncertainties, measurement noise, faulty sensors, and limited communication resources. Considering these practical issues, this study presents a novel estimation‐based event‐triggered nonlinear control scheme without pressure sensors to regulate the oxygen excess ratio (OER) at its optimal reference. First, to exactly reconstruct the unmeasured OER, a uniform robust exact differentiator and an adaptive high gain observer are developed to estimate the supply manifold pressure and the cathode pressure, respectively, thus redundant sensors and development cost can be saved. Then, a reduced‐order high gain observer is constructed to approximate the unmodeled lumped disturbance consisting of external disturbances and system uncertainties, which helps to decrease the control gain and improve the robustness. Further, an event‐triggered adaptive terminal sliding mode controller with disturbance compensation is proposed to guarantee accurate and fast OER tracking control with integral absolute error (IAE) of 0.447 and average settling time of 0.9 s while avoiding chattering effect and unnecessary communication in the controller‐to‐actuator channel. The comparison results illustrate that the proposed approach achieves superior estimation and control of OER with satisfactory robustness to parameter perturbations (less than 4% deviation in IAE).

Fuzzy adaptive terminal sliding mode control based on recurrent neural network compensation for a maglev system

Reluctance motor has high force density while strong uncertainties. This paper concentrates on the research of the intelligent control method for a reluctance-motor maglev system (RMMS) with limited system information. To this end, we propose a new fuzzy adaptive terminal sliding mode control (FATSMC) method based on a novel full-regulated recurrent neural network (RNN) compensator. The RNN approximates the lumped uncertainty of the RMMS, and the controller handles the residual error and external disturbances. The presented increasing–decreasing fuzzy adaptive law, without the prior knowledge of uncertain upper bound, adjusts the switching gain by the dynamic information of the system. And the overestimation of the switching gain can be reduced. Moreover, the proposed method only uses the nominal parameters and model of the RMMS, thus avoiding complex modeling. This study provides strong support for the application of RMMS in high-precision situations. By constructing a Lyapunov function, the stability of the proposed method is analyzed, which is uniformly and ultimately bounded (UUB). In the experiment, the proposed method is compared with three existing methods in four cases. The results demonstrate that the presented method achieves nanoscale suspension accuracy, suppresses chattering significantly, and has strong robustness.

Barrier function-based adaptive terminal sliding mode control of plug-in hybrid electric vehicle with saturated control actions

Plugin hybrid electric vehicles in contrast to traditional hybrid electric vehicles (HEV) are getting enormous attention in the modern electric transportation market as they address the environmental issues, fuel economy crisis, and CO2 emission. Fuel cell-based PHEV with smart charging mechanism is an important application of the clean energy generation and storage system. There are two main subsystems in a PHEV: A hybrid energy storage system (HESS) and an integrated charging unit (ICU). In this study, an effective control strategy for the charger and HESS comprising three power sources: Fuel cell (FC), battery, and a superconducting magnetic energy storage (SMES) connected to the DC bus is presented. Due to the non-linearity of power conditioning circuits in ICU and HESS, designing a robust nonlinear controller is still a challenge. A barrier function-based adaptive terminal sliding mode controller is proposed to ensure the finite-time convergence of the output variable. It also maintains it in a predefined neighborhood of zero without overestimating the control gain and hence exhibits better dynamic performance in the presence of external disturbances of unknown upper bound. The asymptotic stability of the system has been ensured by using the Lyapunov stability criterion. Finally, the energy management algorithm in HESS using the state of charge of the battery and SMES as the decisive factor is incorporated to maintain the stability of the system under varying load conditions. The performance of the proposed controller has been compared with conventional nonlinear control techniques like adaptive terminal sliding mode control (ATSMC) and supertwisting sliding mode control (STSMC) by using MATLAB/Simulink. The performance of the system is further validated by using a C2000 Delfino F28379D MCU dual-core microcontroller with a hardware-in-the-loop (HIL) setup.

Model-free adaptive control based on prescribed performance and time delay estimation for robotic manipulators subject to backlash hysteresis

Aim to solve the problem of over-dependence on dynamics model and low tracking accuracy of robotic manipulators under uncertainties and backlash hysteresis, an adaptive continuous nonsingular fast terminal sliding mode controller combining time delay estimation (TDE) and prescribed performance control (PPC) is formulated. Firstly, TDE is applied to estimate the model information and external disturbances, so that the dynamics model of robotic manipulators is simplified into a local model. Then, an adaptive terminal sliding mode controller is established based on the local model and PPC, which solves the singularity problem of traditional terminal sliding mode and improves the transient and steady-state performance of robotic manipulators. Next, by combining the reaching law with the adaptive term, robotic manipulators achieve favorable tracking performance, fast convergence time, and chattering suppression under complex disturbances. And the Lyapunov theory is applied to prove that the robotic manipulators can reach the steady state in finite time. Meanwhile, a saturation compensator is formulated to settle actuator saturation and the compensator can converge within a finite time. Finally, the simulation is applied to verify the formulated control strategy can effectively improve the tracking performance and enhance the robustness against disturbances.

Distributed fault‐tolerant control for high‐speed trains based on adaptive terminal sliding mode control

AbstractHere, a distributed multi‐body dynamics model of high‐speed train (HST) with actuator faults is established. Based on this detail model, a non‐singular terminal sliding mode control method is designed for HST distributed fault‐tolerant control, and the stability of the proposed controller is proved strictly. Considering the high uncertainty of the distributed dynamics model, a radial basis function neural network is introduced to approximate the system uncertainties. Then a universal fuzzy system is proposed to eliminate the chattering problem from sliding mode controller in train control, which further improves the comfort of the train. Finally, numerical simulations are carried out using data from CRH380B train, and model reference adaptive control (MRAC) and adaptive sliding mode control are also tested under the same condition for performance comparison. The results confirm the effectiveness of the proposed controller. Compared with existing MRAC, the root mean square of the speed and position tracking error are reduced by 23.4% and 52%, respectively.

Adaptive terminal sliding mode controller design for cyber‐physical systems under external disturbance and actuator cyber‐attack

SummaryThis article studies a novel technique of the adaptive terminal sliding mode (TSM) control for the finite time stabilization of cyber‐physical systems (CPSs). By using the proposed nonlinear sliding surface, the reaching mode is eliminated and the entire system robust procedure is ameliorated. The used adaptive laws are dealt with the cyber‐attacks and external disturbances which their upper bounds are not needed to be recognized, where suggested approach is more pliable in the real performance. The presented new adaptive TSM control technique guarantees the system robustness versus cyber‐attacks and external disturbances. Furthermore, the proposed control method obliterated the chattering behavior utilizing the hyperbolic tangent function and the continuous adaptive parameters in the control law. Numerical simulation results indicate the proposed adaptive TSM control technique's effectiveness and success compared to the results of state feedback control, conventional sliding mode control (SMC), traditional integral‐type SMC, and adaptive integral‐type SMC methods.

A novel model-free adaptive terminal sliding mode controller for bridge cranes

To achieve stabilisation control of an underactuated bridge crane system, a new robust control strategy for the sliding mode is proposed in this paper. It can realise finite-time-convergent stabilisation control under the conditions of model uncertainty, parameter perturbation and external interference. In contrast to the existing methods, our method does not need prior information of the dynamic characteristics of the bridge crane system, and can make the system converge to the equilibrium state at the preset time. Specifically, the nonlinear model of the bridge crane system is linearised with partial feedback, and adaptive signals are introduced. Then, according to the form of the transformed system, a fast terminal sliding mode surface is constructed, and an adaptive terminal sliding mode controller is designed. According to strict analysis, the proposed control law ensures that the system converges to the equilibrium point in finite time and provides the convergence time. Finally, the effectiveness and robustness of the proposed control method are verified by comparing the simulation and experimental results with existing methods.