Model-free Sliding Mode Control Research Articles

U-control – a universal platform for control system design with inversion/cancellation of nonlinearity, dynamic and coupling through model-based to model-free procedures

This paper presents the key components in the Universal(U)-control framework for designing dynamic control systems from model-based to model-free paradigms, in which the pillars include U-control system configuration, open-loop dynamic inversion, closed-loop dynamic inversion, general model-free cancellation of nonlinear-dynamic-coupling effect, model-free-sliding-mode control (MFSMC) and model-free composite nonlinear feedback (CNF) control. This paper is devoted to delivering intuitive and easy-to-understand explanations for the involved approaches with a series of schematic diagrams. Readers can refer to the corresponding publications for more details on the theoretical analyses. In addition, some showcase examples are provided in this paper to facilitate the understanding of certain important concepts.

Model free sliding mode control for serial robot manipulator: rigid and elastic joint robot

Model free sliding mode control for serial robot manipulator: rigid and elastic joint robot

Improved model-free synergetic control of permanent magnet synchronous motor using unknown input observer

Parametric uncertainties and inverter nonlinearity exist in the permanent magnet synchronous motor (PMSM) drive system, which may lead to performance degradation or failure of model-based control. This paper presents an enhanced model-free synergetic control method for PMSMs, addressing the limitations of relying on accurate motor models in existing speed control strategies. The proposed approach aims to achieve robust control performance in the presence of parameter perturbations without the need for an accurate motor model. Firstly, a new ultra-local model of the speed loop for the PMSM is established based on a newly developed ultra-local theory. Subsequently, a model-free synergetic controller is designed using the principles of synergetic control theory. To address the issue of output chattering in the sliding mode observer used to estimate the unknown part of the ultra-local model, a synergetic observer method is proposed. Simulation results are presented and compared with those obtained using a proportional-integral (PI) controller and a conventional model-free sliding mode controller. The results demonstrate that the model-free synergetic controller exhibits robust performance and provides accurate estimation of the unknown part without output chattering.

Improved Model-Free Sliding Mode Control of Linear Motor Based on Time-Varying Gain Model-Assisted Linear Extended State Observer

Improved Model-Free Sliding Mode Control of Linear Motor Based on Time-Varying Gain Model-Assisted Linear Extended State Observer

Model-Free Sliding Mode Control for a Nonlinear Teleoperation System with Actuator Dynamics

Teleoperation robotic systems control, which enables humans to perform activities in remote situations, has become an extremely challenging field in recent decades. In this paper, a Model Free Proportional-Derivative Sliding Mode Controller (MFPDSMC) is devoted to the synchronization problem of teleoperation systems subject to actuator dynamics, time-varying delay, model uncertainty, and input interaction forces. For the first time, the teleoperation model used in this study combines actuator dynamics and manipulator models into a single equation, which improves model accuracy and brings it closer to the actual system than in prior studies. Further, the proposed control approach, called Free, involves the simple measurement of inputs and outputs to enhance the system's performance without relying on any knowledge from the mathematical model. In addition, our strategy includes a Sliding Mode term with the MFPD term to increase system stability and attain excellent performance against external disturbances. Finally, using the Lyapunov function under specified conditions, asymptotic stability is established, and simulation results are compared and provided to demonstrate the efficacy of the proposed strategy.

Model-Free Sliding Mode Control for a Nonlinear Teleoperation System with Actuator Dynamics

Teleoperation robotic systems control, which enables humans to perform activities in remote situations, has become an extremely challenging field in recent decades. In this paper, a Model Free Proportional-Derivative Sliding Mode Controller (MFPDSMC) is devoted to the synchronization problem of teleoperation systems subject to actuator dynamics, time-varying delay, model uncertainty, and input interaction forces. For the first time, the teleoperation model used in this study combines actuator dynamics and manipulator models into a single equation, which improves model accuracy and brings it closer to the actual system than in prior studies. Further, the proposed control approach, called Free, involves the simple measurement of inputs and outputs to enhance the system's performance without relying on any knowledge from the mathematical model. In addition, our strategy includes a Sliding Mode term with the MFPD term to increase system stability and attain excellent performance against external disturbances. Finally, using the Lyapunov function under specified conditions, asymptotic stability is established, and simulation results are compared and provided to demonstrate the efficacy of the proposed strategy.

Model-Free Sliding Mode Control for a Nonlinear Teleoperation System with Actuator Dynamics

Teleoperation robotic systems control, which enables humans to perform activities in remote situations, has become an extremely challenging field in recent decades. In this paper, a Model Free Proportional-Derivative Sliding Mode Controller (MFPDSMC) is devoted to the synchronization problem of teleoperation systems subject to actuator dynamics, time-varying delay, model uncertainty, and input interaction forces. For the first time, the teleoperation model used in this study combines actuator dynamics and manipulator models into a single equation, which improves model accuracy and brings it closer to the actual system than in prior studies. Further, the proposed control approach, called Free, involves the simple measurement of inputs and outputs to enhance the system's performance without relying on any knowledge from the mathematical model. In addition, our strategy includes a Sliding Mode term with the MFPD term to increase system stability and attain excellent performance against external disturbances. Finally, using the Lyapunov function under specified conditions, asymptotic stability is established, and simulation results are compared and provided to demonstrate the efficacy of the proposed strategy.

Model-free fast integral terminal sliding-mode control method based on improved fast terminal sliding-mode observer for PMSM with unknown disturbances

This paper presents a novel model-free fast integral terminal sliding-mode control (MFFITSMC) method based on an improved fast terminal sliding-mode observer (IFTSMO) for permanent magnet synchronous motor (PMSM) drive system, which can effectively eliminate the impact caused by unknown disturbances, such as parameter perturbations and external disturbances. The PMSM mathematical model with unknown disturbances is first established, and the ultra-local model (ULM) of the PMSM speed loop is constructed. Next, the model-free fast integral terminal sliding-mode controller is designed in the speed loop based on the ULM. Then, the IFTSMO is designed to precisely estimate the unknown term of the ULM, and the estimated unknown term is fed back to the MFFITSMC controller to perform compensation for unknown disturbances in real time. Finally, compared with the proportional–integral (PI) control method and the conventional model-free sliding-mode control (MFSMC) method, the results of simulations and experiments demonstrate that the presented MFFITSMC method reduces the dependence on the precise model and achieves the purpose of anti-disturbance control of the PMSM drive system.

Model-free robust decoupling control of nonlinear nonaffine dynamic systems

This study presents a model-free robust input-output decoupling control with Nonlinear-Dynamic-Coupling Inversion/Inverter (NDCI) in a U-control framework. Regarding the decoupling, an input/output (I/O) coupling matrix function is proposed to derive two decouplers (U-decoupler/functional inversion and D-decoupler/static matrix inversion). A general existing theorem is proved for model-free sliding mode control (MFSMC) to lay the foundation for the NDCI, which takes the Lyapunov differential inequality for its derivative rather than the semi-define Lyapunov derivative. Accordingly, a multi-input and multi-output (MIMO) model-free decoupling U-control (MFDUC) platform is established to integrate the functionalities into a double closed-loop system framework. To validate the functionalities and configurations, this study presents transparent and comparative simulated bench tests, which also could be treated as user guidance for further study and ad hoc applications.

Smart Extreme Fast Portable Charger for Electric Vehicles-Based Artificial Intelligence

Due to the increase in the number of electric vehicles (EVs) in the world, there is a need to design superior performance and lower operating costs of charging infrastructures to serve these vehicles. Merging extreme fast charging technology with the portable feature can provide a user-friendly and cost-effective structure for charging EVs. This brief proposes a Smart Extreme Fast Portable Charger (SEFPC) for Electric Vehicles which have several input ports (e.g., the power grid or Renewable Energy Sources (RESs)/Energy Storage Systems (ESSs)) and an output port with an optimal charging operation mode based on considering the condition of the available power sources and battery of EVs for saving the energy and time charging. Moreover, a model-free sliding mode controller-based machine learning algorithm is applied to find optimal charging operation mode based on the battery state and power of sources condition to increase battery life and overall system efficiency. Finally, real-time results based on the OPAL-RT are provided to validate the efficacy and feasibility of the proposed SEFPC and model-free sliding mode controller.

Decreasing control signal vibrations in the anti-noise model-free sliding mode control algorithm

Decreasing control signal vibrations in the anti-noise model-free sliding mode control algorithm

An Improved Model-free Sliding Mode Control Algorithm of Super-twisting for SPMSM

An Improved Model-free Sliding Mode Control Algorithm of Super-twisting for SPMSM

Decreasing control signal vibrations in the anti-noise model-free sliding mode control algorithm

Decreasing control signal vibrations in the anti-noise model-free sliding mode control algorithm

Reducing Impact of Constant Power Loads on DC Energy Systems by Artificial Intelligence

Due to the negative impedance potential of constant power loads (CPLs), the stability of power electronic converters-based electrical distribution networks is prone to instability. This brief proposes a robust control technique based on deep reinforcement learning to stabilize a DC microgrid (MG) with parallel boost converters when feeding CPLs. For this purpose, a model-free sliding mode controller (MFSMC) has been applied to the DC MG. The MFSMC controller does not need any identification of converters in the DC MG system while ensuring the efficiency and the stability of the control synthesis. The key control coefficients are designed by Proximal Policy Optimization (PPO) Reinforcement Learning (RL). The PPO is made from two deep neural networks (NNs) (actor NN and critic NN) which are trained to adjust the coefficients of the MFSMC controller. The MFSMC controller designed by the PPO with actor-critic architecture is applied to the DC MG system feeding CPL in an OPAL-RT setup to verify the efficiency of the proposed controller through Hardware-in-the-Loop (HiL) simulations.

No-chatter model-free sliding mode control for synchronization of chaotic fractional-order systems with application in image encryption

No-chatter model-free sliding mode control for synchronization of chaotic fractional-order systems with application in image encryption

Model-Free Sliding Mode Control for PMSM Drive System Based on Ultra-Local Model

This paper presents a novel model-free sliding mode control (MFSMC) method to improve the speed response of permanent magnet synchronous machine (PMSM) drive system. The ultra-local model (ULM) is first derived based on the input and the output of the PMSM. Then, the novel MFSMC method is presented, and the controller is designed based on ULM and MFSMC. A sliding mode observer (SMO) is constructed to estimate the unknown part of the ULM. The estimated unknown part is feedbacked to MFSMC controller to perform compensation for parameter perturbations and external disturbances. Compared with the sliding mode control (SMC) method, the results of simulation and experiment demonstrate that the presented MFSMC method improves the dynamic response and robustness of the PMSM drive system.

Complete model-free sliding mode control (CMFSMC)

This study presents a complete model-free sliding mode control (CMFSMC) framework for the control of continuous-time non-affine nonlinear dynamic systems with unknown models. The novelty lies in the introduction of two equalities to assign the derivative of the sliding functions, which generally bridges the designs of those model-based SMC and model-free SMC. The study includes a double SMC (DSMC) design, state observer design, and desired reference state vector design (whole system performance), which all do not require plant nominal models. The preconditions required in the CMFSMC are the plant dynamic order and the boundedness of plant and disturbances. U-model based control (U-control) is incorporated to configure the whole control system, that is (1) taking model-free double SMC as a robust dynamic inverter to cancel simultaneously both nonlinearity and dynamics of the underlying plants, (2) taking a model-free state observer to estimate the state vector, (3) taking invariant controller to specify the whole control system performance in a linear output feedback control and to provide desired reference state vector. The related properties are studied to support the concept/configuration development and the analytical formulations. Simulated case studies demonstrate the developed framework and show off the transparent design procedure for applications and expansions.

Improved Model-Free Sliding Mode Control Algorithm for Control Non-Linear Systems

When testing the performance of the model-free sliding mode control algorithm, it was found that it could not maintain good performance when the system was exposed to noise. this research suggests designing a noise-resistant model-free sliding mode control algorithm. The importance of this algorithm is that it takes into account the effect of the noise, where the noise value is implemented in the model-free algorithm. The sliding surface of the controller is designed based on the improved relationship and to ensure the stability of the system in the closed-loop the control signal was derived based on the direct Lyapunov method. To minimize the effects of chattering in the control signal, the control law was reconfigured using a boundary layer. The improved algorithm was implemented to a second-order non-linear system and the simulation results showed the system's ability to track the desired signal in spite of the presence of the noise as well as its ability to maintain the stability of the controlled system

Improved Model-Free Sliding Mode Control Algorithm for Control Non-Linear Systems

When testing the performance of the model-free sliding mode control algorithm, it was found that it could not maintain good performance when the system was exposed to noise. this research suggests designing a noise-resistant model-free sliding mode control algorithm. The importance of this algorithm is that it takes into account the effect of the noise, where the noise value is implemented in the model-free algorithm. The sliding surface of the controller is designed based on the improved relationship and to ensure the stability of the system in the closed-loop the control signal was derived based on the direct Lyapunov method. To minimize the effects of chattering in the control signal, the control law was reconfigured using a boundary layer. The improved algorithm was implemented to a second-order non-linear system and the simulation results showed the system's ability to track the desired signal in spite of the presence of the noise as well as its ability to maintain the stability of the controlled system

Sliding Mode Control Algorithms for Anti-Lock Braking Systems with Performance Comparisons

This paper presents the performance of two sliding mode control algorithms, based on the Lyapunov-based sliding mode controller (LSMC) and reaching-law-based sliding mode controller (RSMC), with their novel variants designed and applied to the anti-lock braking system (ABS), which is known to be a strongly nonlinear system. The goal is to prove their superior performance over existing control approaches, in the sense that the LSMC and RSMC do not bring additional computational complexity, as they rely on a reduced number of tuning parameters. The performance of LSMC and RSMC solves the uncertainty in the process model which comes from unmodeled dynamics and a simplification of the actuator dynamics, leading to a reduced second order process. The contribution adds complete design details and stability analysis is provided. Additionally, performance comparisons with several adaptive, neural networks-based and model-free sliding mode control algorithms reveal the robustness of the proposed LSMC and RSMC controllers, in spite of the reduced number of tuning parameters. The robustness and reduced computational burden of the controllers validated on the real-world complex ABS make it an attractive solution for practical industrial implementations.