Robust Adaptive Sliding Mode Control Research Articles

Vibration Control of Flexible Arm Based on Adaptive Robust Sliding Mode Control and LQR Control

Vibration Control of Flexible Arm Based on Adaptive Robust Sliding Mode Control and LQR Control

Continuous-time Lyapunov stability analysis and systematic parametrization of robust adaptive sliding mode controller for systems with matched and unmatched dynamics

Continuous-time Lyapunov stability analysis and systematic parametrization of robust adaptive sliding mode controller for systems with matched and unmatched dynamics

A Robust Adaptive Non-Singular Terminal Sliding Mode Control: Application to an Upper-Limb Exoskeleton with Disturbances and Uncertain Dynamics

This paper presents a new control strategy for uncertain upper-limb exoskeleton systems, which are known to have high nonlinearities, unmodeled dynamics, and uncertainties. The proposed technique is based on the terminal sliding mode control algorithm and its nonsingular design method and incorporates an adaptive control approach to estimate the upper bounds of the unknown system uncertainties, which helps to improve the accuracy of the control and reduce the effects of disturbances. The stability of the proposed control strategy is confirmed using Lyapunov theory, and its effectiveness is tested on a two-degrees-of-freedom upper-limb exoskeleton. The results demonstrate that the proposed control scheme provides robust, fast, and finite-time convergence as well as an effective control approach capable of dealing with the disturbances and uncertainties that such systems are prone to.

Nonlinear Robust Adaptive Sliding Mode Control Strategies Involve a Fractional Ordered Approach to Reducing Dengue Vectors

This study presents an innovative approach by integrating adaptive sliding mode control strategies with fractional order modeling to address the challenge of reducing Aedes aegypti mosquito populations, the primary vector of Dengue - a widespread and debilitating disease. By employing the Atangana-Baleanu-Caputo fractional operator to model the dynamics of the mosquito population, we achieve a more precise representation of complex and non-linear behaviors. The motivation behind adopting the Adaptive Sliding Mode Control (ASMC) approach lies in the critical need to efficiently control Aedes aegypti mosquito populations, a key step in combating the prevalence of dengue. The ASMC method dynamically adjusts control parameters based on evolving conditions, enhancing its adaptability to the changing dynamics of mosquito populations.The Lyapunov stability theorem ensures the reliability of tracking convergence and control structure. Additionally, we implement the Toufik Atangana method to solve both state and adjoint fractional differential equations using the ABC derivative operator. This incorporation adds a novel dimension to the study, providing a comprehensive framework for addressing the intricate dynamics inherent in the Aedes aegypti mosquito population. To assess the effectiveness of the proposed strategy, a numerical performance index is introduced at the end of the abstract. This index justifies the controller’s efficacy by comparing it to other conventional controllers. The inclusion of this quantitative measure reinforces the significance of the proposed strategy in the context of dengue prevention and control efforts.

A New Adaptive Robust Sliding Mode Control for High-Precision PKMs: Design, Stability Analysis, and Experiments

A New Adaptive Robust Sliding Mode Control for High-Precision PKMs: Design, Stability Analysis, and Experiments

Bond-Graph-Based Approach to Teach PID and Sliding Mode Control in Mechatronics

The main contribution of this article is creating synergy between subjects; this means that students use the same graphical tool in several subjects. So far, the bond graph has not been used in control theory, but it is the “native language” of mechatronics engineers, so we would like to introduce it into the teaching of control theory. The bond graph method is proposed as a novel teaching method to teach mechatronics subjects in the paper. The bond graph is a graphical alternative to ordinary differential equations from a mathematical standpoint. Traditionally, control theory employs ordinary differential equations, as they are familiar to control theorists. However, mathematically, both approaches are equivalent but require a slightly different approach in their application. This article highlights the mathematical similarities between the two approaches while emphasizing the distinctions in graphical representation. Another contribution is that the PID and sliding mode controller are represented using the bond graph method. In the meantime, through the use of practical examples, we effectively illustrate how the same problem can be solved using either approach. In the training materials, the PID controller and an adaptive robust sliding mode controller (ARSMC) with the bond graph are utilized as examples to demonstrate synergy in mechatronics. Finally, we present proof that mechatronic engineers achieve superior outcomes when utilizing the bond graph approach, based on test results from undergraduate students.

Research on the Dynamic Control Method of CFETR Multi-Purpose Overload Robot

The CFETR multi-purpose overload robot (CMOR) is a key subsystem of the remote handling system of the China fusion engineering test reactor (CFETR). This paper first establishes the kinematic and dynamic models of CMOR and analyzes the working process in the vacuum chamber. Based on the uncertainty of rigid-flexible coupling, a CMOR adaptive robust sliding mode controller (ARSMC) is designed based on the Hamilton-Jacobi equation to enhance the robustness of the control system. In addition, to compensate the influence of non-geometric factors on position accuracy, an error compensation method is designed. Based on the matrix differentiation method, the CMOR coupling parameter errors are decoupled, and then the gridded workspace principle is used to identify the parameter errors and improve the motion control accuracy. Finally, the CMOR rigid-flexible coupling simulation system is established by ADAMS-MATLAB/Simulink to analyze the dynamic control effect of ARSMC. The simulation results show that the CMOR end position error exceeds 0.1 m for single joint motion. The average value of CMOR end position error is less than 0.025 m after compensation, and the absolute error value is reduced by 4 times, improves the dynamic control accuracy of CMOR.

Fast finite time fractional-order robust-adaptive sliding mode control of nonlinear systems with unknown dynamics

This study investigates the fast finite-time robust-adaptive terminal sliding-mode control of nonlinear affine high order (NAHO) systems based on fractional-order control approach. It is considered in this paper that dynamic parameters of the nonlinear system are completely unknown. To do this, at first a high-order fractional order sliding surface is proposed. Then, considering that there is no any information about dynamics of system due to the large amount of uncertainties and unmolded dynamics, a finite-time fractional-order adaptive sliding-mode controller is devised to achieve finite-time stability with quick convergence of system output to its desired trajectory. A stable adaptive law is also designed to estimate the system’s unknown dynamic parameter vector. This is worth to mention that using the designed control law, finite time convergence is obtained in a more real condition where the dynamic parameters of the system are unknown and because of using a fractional order sliding surface, the chattering phenomena is less than ordinary terminal sliding mode controller. The developed Lyapunov theory has been employed to prove the stability of the closed-loop system. The simulation results are demonstrated on a cable robot with unknown dynamics to validate the effectiveness of the control law.

Adaptive Sliding‐Mode Control for Permanent Magnet Spherical Actuator Based on Trajectory Re‐Planning

This study proposes an adaptive sliding‐mode control strategy based on trajectory re‐planning for a permanent magnet spherical actuator (PMSA) to address the large overshoot caused by substantial initial error while maintaining tracking precision. The proposed re‐planning technique reconstructs a local trajectory without the preceding knowledge of the desired position in the future, which makes this technique more suitable for reconstructing trajectory given online. First, a re‐planning cool‐down term is added to maximize the capability of controller to improve the precision and speed of tracking. Moreover, a smooth switching technique is also applied to further improve the precision of trajectory tracking and stop redundant re‐planning when the system enters satisfactory state. Finally, the proposed trajectory re‐planning strategy is combined with a robust adaptive sliding‐mode controller (RASC), which effectively reduces friction, delay, and external uncertainty disturbances. The effectiveness of the proposed control design is verified by both simulation and experiment on PMSA, which can provide reference for the further engineering application of multi‐degree‐of‐freedom system. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Adaptive Event-Triggered Sliding-Mode Control for Consensus Tracking of Nonlinear Multiagent Systems With Unknown Perturbations.

The adaptive tracking control problem of leader-following nonlinear multiagent systems (MASs) subject to unknown perturbations and limited network bandwidth is investigated by the robust adaptive event-triggered sliding-mode control method. A distributed integral sliding mode is established to realize the finite-time reachability of the states of the leader-following nonlinear MAS. An adaptive triggering control mechanism is then put forward to dynamically adjust the triggering interval, thus reducing the actuator wear and unnecessary network resource consumption. The positions and velocities of the leader-following nonlinear MAS subject to unknown external disturbances are, respectively, driven to the equilibrium point by constructing a distributed event-based robust adaptive sliding-mode protocol. Via the Lyapunov stability theory and Barbalat lemma, sufficient conditions to ensure the adaptive tracking performance are derived for leader-following nonlinear MASs. Three simulation examples to verify the efficacy of the proposed event-based robust adaptive sliding-mode controller design are presented.

Robust adaptive sliding mode control for path tracking of unmanned agricultural vehicles

In this paper, a novel adaptive sliding mode control (ASMC) for path tracking of unmanned agricultural vehicles is proposed. It is shown that the designed ASMC is capable of driving the closed-loop lateral position error dynamics to converge to zero asymptotically, where the sliding mode observer (SMO) is designed to estimate the system state so as to adaptively adjust the uncertain bounds of cornering stiffness. Compared with the classical sliding mode control technique, the proposed ASMC not only guarantees strong robustness with respect to parameter uncertainties, disturbance and varying road conditions, but also requires no prior knowledge of the road information. Simulation and experimental results from a typical agricultural wheeled vehicle are provided to verify the excellent performance of the proposed scheme.

Performance comparison of discrete-time robust adaptive controllers for grid-tied power converters under unbalanced grids

Renewable energy generation systems largely use grid-tied power converters with LCL filters. A well-known issue in this kind of system is the resonance of the LCL filter, whose frequency is straightforwardly affected by grid condition. Variations on grid impedance tends to make the controller performance poor and until turn the closed-loop unstable. Therefore, some controllers are designed for specific operation ranges to ensure performance and global stability. However, a deep study of Model Reference Adaptive-based Controllers for this kind of issue was not conducted yet. The aim of this paper is to fit this research gap. Thus, this paper presents a performance comparison of feasible discrete-time robust adaptive controllers, based on reduced order reference model, for grid-tied power converters with LCL filter under unbalanced grid conditions, providing an assessment of the benefits and drawbacks of each considered control strategy. The evaluated control laws are: Robust Model Reference Adaptive Controller, Robust Model Reference-based Adaptive Super-Twisting Sliding Mode Controller, Robust Adaptive One Sample Ahead Preview Controller, and Robust Adaptive Proportional-Integral Controller. Through performance comparison, it can be defined what controller is more adequate in face of grid condition, taking into consideration grid-injected currents quality and design complexity of controller. Experimental results of grid-injected current control of a grid-tied 5.8 kW Voltage-Source Inverter with LCL filter considering relevant grid voltage unbalance are presented to corroborate the controllers’ performance and discuss their reference tracking response, robustness to the parametric variation, exogenous disturbance rejection, parameters adaptability, and global stability. In evaluated scenarios, Robust Adaptive Proportional-Integral controller presented slower regulation dynamics between compared adaptive structures, but reduced tracking error, as well as lower total harmonics distortion, while Robust Model Reference-based Adaptive Super-Twisting Sliding Mode Controller has the smaller overshoot in the initial transient regime, as well as the shorter transient regime.

A novel robust adaptive sliding mode control using stochastic gradient descent for PEMFC power system

This study represents a comparison of novel robust adaptive sliding mode control using stochastic gradient descent (ASMCSGD) versus the super twisting algorithm (STA) for the proton exchange membrane fuel cell (PEMFC) power system. PEMFC has been constantly encountered with external disturbances such as inlet gas pressures and temperature fluctuations which a novel adaptive control law should be designed to be robust against the mentioned perturbations. The proposed ASMCSGD is based on the conventional sliding mode control (SMC), which guarantees robustness and restraining external disturbances. As is common, the main drawback of conventional SMC is the generation of a chattering phenomenon. Therefore, by using the stochastic gradient descent (SGD), a novel adaptive control law is designed. Hence, the SGD can continuously calculate the adaptive gain and then guarantee robustness besides minimizing the chattering phenomenon. The stability of the PEMFC power system for both controllers ASMCSGD and STA is demonstrated via the Lyapunov theorem. Simulation results have been studied and illustrate the effectiveness of the proposed controller successfully using Matlab/Simulink.

Adaptive optimized fractional order control of doubly‐fed induction generator (DFIG) based wind turbine using disturbance observer

AbstractWind energy systems are pollution free and clean form of the renewable energy production. The dynamic model of a wind turbine system based on a doubly fed induction generator (DFIG) is exposed to external disturbances, uncertainties, and nonlinear dynamics. In this paper to ensure the system robustness against external disturbance and uncertainty in system parameters, a novel optimized fractional order robust adaptive sliding mode controller is proposed by utilizing a disturbance observer. The controller's main goal is to track the maximum power point of the wind turbine. In order to show the superiority of the proposed method, the results under normal conditions and in the presence of disturbance and uncertainty have been compared with the classical sliding mode control (SMC) and adaptive sliding mode control (ASMC). The parameters of all three controllers have been optimized by ant colony optimization (ACO) algorithm. The proposed method does not need the knowledge of the upper bounds of model uncertainty and disturbance. Also by using the fractional order operators in the control signal of the proposed method, its robustness against model uncertainty and disturbance is increased and it can extract the maximum power than the other compared methods.

A Hybrid Robust Adaptive Sliding Mode Controller for partially modelled systems: Discrete-time Lyapunov stability analysis and application

This work presents a new discrete-time Hybrid Robust Adaptive Sliding Mode Controller, developed from the union of a Robust Model Reference Adaptive Controller, an Adaptive Sliding Mode Controller, and an Adaptive One Sample Ahead Preview Controller in an unique control structure. Robust Model Reference Adaptive Controller is an adequate direct adaptive control strategy to control partially known plants, but can present slow closed-loop response to ensure global stability. Therefore, an adaptive One Sample Ahead Preview controller is incorporated to accelerate transient regimes, once it tries to track reference signal in one sample. Furthermore, an adaptive Sliding Mode Controller is also merged in the controller structure to help controller performance in transient regime and it also improves relevantly the steady state response in a scenario of several unmodelled dynamics Stability analysis of this controller using Lyapunov criterion and its robustness proof are provided, considering the plant subjected to unmodelled dynamics, which provides controller design constraints. These proofs show the controller is globally stable, and the tracking error tends to a residual set in steady state, even in the presence of matched and unmatched dynamics. Numerical simulations of the Hybrid Robust Adaptive Sliding Mode Controller applied on an unstable nonminimum-phase plant are presented, where only part of the overall plant is take into consideration for controller design. Results corroborate the feasibility and robustness of the developed control strategy and the performance superiority when compared to an adaptive One Sample Ahead Preview controller, with a 75% tracking error reduction in a scenario of several unmodelled dynamics.

Nonlinear Robust Adaptive Sliding Mode Control Strategy for Innate Immune Response to Influenza Virus

This paper aims to design a nonlinear robust adaptive sliding mode control strategy for a mathematical model describing the innate immune response to influenza virus infection. This model possesses seven state variables (respiratory tract epithelial cells in four possible states, namely: healthy, partially infected, infected, and resistant-to-infection, and interferon (IFN) molecules, natural killers, and viruses). This model is based on resistance-to-infection derived from IFN molecules and the removal of infected cells by natural killers. Two control strategies (vaccination and antiviral treatment) are introduced to eradicate the infection. First, a vaccination strategy is applied to convert healthy cells into resistant-to-infection ones inside the body of the susceptible individual. Second, the infected individual undergoes an antiviral treatment strategy that fights against the spread of the concentration of viruses and converts the healthy cells into resistant-to-infection ones simultaneously. The Lyapunov stability theorem is employed to analyse the stability of the desired strategies. Finally, the simulation results show that the goal is fulfilled satisfactorily.

Performance Analysis of a Robust Controller with Neural Network Algorithm for Compliance Tendon–Sheath Actuation Lower Limb Exoskeleton

Robotic rehabilitation of the lower limb exoskeleton following neurological injury has proven to be an effective rehabilitation technique. Developing assistive control strategies that achieve rehabilitative movements can increase the potential for the recovery of the motor coordination of the participants. In this paper, the innovative contributions are to investigate a robust sliding mode controller (SMC) with radials basis function neural network algorithm (RBFNN) compensator for a novel compliance tendon–sheath actuation lower limb exoskeleton (CLLE) to provide intrinsic thigh and shank rehabilitation training. The controller employing the RBFNN compensator is proposed to reduce the impact of friction from the compliance tendon–sheath actuation system (CTSA). In the design of the compensator, a single parameter is investigated to replace the weight information of the neural network. Our proposed controller is shown to yield fast, stable, and accurate control performance regardless of uncertainties interaction. Two additional algorithms, including a robust adaptive sliding mode controller (RASMC) and a sliding mode proportional-integral controller (SMPIC), are introduced in this paper for comparison. The simulations were presented with MATLAB/SIMULINK to validate the superiority of the performance of the proposed controller.

Adaptive super-twisting sliding mode for DC-AC converters in very weak grids

ABSTRACT This paper presents the design of a direct-type adaptive control structure for current regulation of a single-phase grid-tied Voltage-Source Inverter (VSI) with an LCL filter under very weak grid condition. The initial content of this work was previously published, where the controller was presented and its discrete-time stability proof was provided. Power converters connected to weak grids bring challenges to the control system, since the grid can change abruptly from a strong to a very weak condition , degrading system power quality. This study evaluates the feasibility of a RMRAC-based robust adaptive Super-Twisting Sliding Mode controller for current control of VSI when the grid inductance changes substantially, from 0 mH (strong grid) to 10 mH (very weak grid). To show the benefits of the control strategy and its robustness, hardware-in-the-loop (HIL) simulation results are presented, comparing its performance with two other controllers..

Adaptive Sliding Mode Disturbance Observer Based Robust Control for Robot Manipulators Towards Assembly Assistance

Parameter uncertainties and fluctuated disturbances have brought great difficulties to the smooth and precise control of robot manipulators in some industrial environments. To address these challenges, a robust adaptive sliding mode controller is proposed in this work for accurate control of the robot manipulator. In case of the modeling uncertainties caused by parameters variation during the operational process, the adaptive technique integrated with radial basis function neural networks is utilized. Under such scheme the parameters are automatically adjusted during the tracking process to enhance the system robustness. A new sliding mode disturbance observer is adopted in order to give compensation of the interference and a new variable structure scheme is proposed to effectively suppress the chattering phenomenon. Based on robot assembly and other application verification, the robustness and stability of the proposed approach are demonstrated with superior and smooth tracking performance.

Efficient hierarchical robust adaptive control of mode transition for parallel hybrid electric vehicles

A fast and smooth mode transition process for parallel hybrid electric vehicles (HEVs) can improve vehicle drivability. At the same time, system uncertainty resulting from friction, gearbox, parameter perturbation, production deviations, and external disturbance of the mode transition process makes control design challenges. In this regard, this paper introduces a novel efficient hierarchical robust adaptive mode transition control strategy to achieve an accurate and fast engagement of the clutch of a parallel HEV during mode transition in the case of uncertain system parameters and unmeasurable actual clutch torque in this paper. At the upper level of the controller, a robust adaptive sliding mode control (RASMC) is proposed to calculate the clutch transmitted required torque based on the current states of the electric motor (EM) and engine, and the control parameters are optimized via the particle swarm optimization (PSO) algorithm by compromising the vehicle jerk and the clutch slipping energy loss. Moreover, a fuzzy controller is designed to calculate the expected clutch engaging speed by employing the deviation of the desired and actual clutch torques, and the actual clutch torque is not a measured value but an estimate obtained by a proportional-integral (PI) observer. At the lower level, an adaptive finite-time control (AFTC) scheme is developed to overcome the gear backlash, parameter perturbation, and external disturbance during the clutch position tracking process. The effectiveness and advantage of the proposed control strategy are verified by both MATLAB/Simulink simulations and the hardware-in-loop (HIL) test.