Integral Sliding Mode Observer Research Articles

Fixed-Time Consensus Multi-Agent-Systems-Based Speed Cooperative Control for Multiple Permanent Magnet Synchronous Motors with Complementary Sliding Mode Control

To improve the tracking performance and robustness of traditional multi-motor speed cooperative control, this paper proposes a speed cooperative control method for multiple permanent magnet synchronous motors (multi-PMSMs) based on the fixed-time consensus protocol for multi-agent systems (MASs) combined with CSMC. Firstly, the speed regulation system of multi-PMSMs is regarded as a MAS. By designing a distributed consensus protocol based on an undirected communication topology, the system achieves fixed-time consensus convergence. Then, a terminal integral sliding mode observer (TISMO) is designed to estimate disturbances, and feedforward compensation is introduced into the consensus protocol to obtain the desired q-axis current. Furthermore, within the framework of the vector control speed cooperative system of PMSMs, a CSMC is designed to track the q-axis reference current. Meanwhile, the stability of the above controllers and observers is theoretically proven using the Lyapunov functions. Finally, comparative experiments are conducted on a multi-PMSM speed regulation experimental platform to verify the proposed control method against the traditional deviation coupling control (DCC) method. The results indicate that under the new control method proposed in this paper, the chattering phenomenon is reduced by about 2 r/min compared to the traditional DCC method. During sudden load and sudden relief load conditions, the speed fluctuation is reduced by approximately 4%, demonstrating good tracking performance and strong robustness.

Apply Honey Barger Algorithm to double integral sliding mode observer for stable load frequency in multi-area power system

A randomness of load demand and plant uncertainties causes frequency deviation in linked power plants. Based on the double-integral sliding mode observer (DISMO) integrated Honey Badger algorithm (HBA), a novel LFC for a multi-area power plant (MAPP) is introduced in this article. The main aim is to minimize frequency offset, finite time, and aggregate instability of controllable loads subject to the balance of the power on the MAPP. Firstly, the suggested observer is developed to ensure an exact estimation of the actual signal for the controller input signal. Secondly, a double-integral sliding surface (DISS) is introduced based on estimated signals the designed observer gains. Then, a new SMC rule is designed to ensure finite time reachability and eliminate the chattering and oscillating troubles. Thirdly, in this regard, the proposed sliding surface is designed with four selected parameters. Regarding this, to maximize the ability of the suggested SMCer, the optimized controller parameters are determined by HBA. The robustness of the suggested scheme is examined by various case studies on the LFC models in a single-area power plant (SAPP) and MAPP under different load disturbances and parameter uncertainties.

Robust Hybrid Visual Servoing of Omnidirectional Mobile Manipulator With Kinematic Uncertainties Using a Single Camera.

A robust hybrid visual servoing (HVS) method for a single camera-mounted omnidirectional mobile manipulator (OMM) with kinematic uncertainties caused by slipping is proposed herein. Most existing studies pertaining to the visual servoing of mobile manipulators do not consider the kinematic uncertainties and the singularity of the manipulator that can occur during actual operations; furthermore, they required external sensors other than a single camera. In this study, the kinematics of an OMM are modeled considering kinematic uncertainties. Accordingly, an integral sliding-mode observer (ISMO) is designed to estimate the kinematic uncertainties. Subsequently, an integral sliding-mode control (ISMC) law is proposed to achieve robust visual servoing using the estimates of the ISMO. Additionally, an ISMO-ISMC-based HVS method is proposed to address the singularity issue of the manipulator; this method guarantees both robustness and finite-time stability in the presence of kinematic uncertainties. Overall, the entire visual servoing task is performed using only a single camera attached to the end effector without any other external sensors, unlike in previous studies. The stability and performance of the proposed method are verified numerically and experimentally in a slippery environment that generates kinematic uncertainties.

Privacy-Preserving Consensus Strategy for Secondary Control in Microgrids Against Multilink False Data Injection Attacks

Privacy issues and the cyber-attacks are two typical threats in network operation, but the problem considering both of them has not been properly addressed. To fill this gap, this paper investigates the privacy preserving consensus strategy against the false data injection attacks (FDIAs) for secondary control of microgrids. An integral sliding mode observer and its supporting controller are developed to against the FDIAs on local units. Since the sliding motion is independent of the controller signal, the proposed strategy has natural advantage for the FDIAs on the controller command. Furthermore, an observer-based resilient control strategy is proposed to against the FDIAs on the communication lines. It is worth mentioning that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance can be got by regarding the consensus errors as disturbance. Moreover, the edge-based privacy preserving algorithm along with the observed-based resilient strategy is proposed. Lastly, some simulation examples are used to verify the theoretical results.

Predictive Torque Control of Induction Motor Based on a Robust Integral Sliding Mode Observer

The parameter estimators and the disturbance observers are two widely used methods for the robustness improvement of the model predictive control schemes. This article presents a hybrid solution to improve the robustness of predictive torque control (PTC) for induction motor (IM) drive. A novel integral sliding mode observer (ISMO)-based ultralocal model and an adaptive observer are combined in the proposed method to establish a robust prediction model for the PTC. The stator current prediction model of the conventional PTC contains different parameters and variables of the IM that increase the sensitivity of the method. The proposed method solves this problem by replacing the conventional stator current prediction model with the ISMO-based ultralocal model, which does not require the IM’s parameters. On the other hand, the stator flux prediction model of the PTC just depends on the stator resistance. So, an adaptive Luenberger observer is utilized to cancel the effect of resistance variation from the stator flux prediction model. The proposed ISMO and the Luenberger observer are constructed based on the Lyapunov theory to guarantee the stability of the proposed control method. The experimental validation of the proposed method is performed. Also, the robustness of this method has been validated experimentally.

Direct yaw moment control and state observer design for four in-wheel motor drive electric vehicles based on super-twisting algorithm

In order to improve the handling stability for four in-wheel motor drive electric vehicles (4IWMD EVs), a novel direct yaw moment control (DYC) strategy is proposed in this paper. To begin with, the problem formulation is stated by considering modeling errors and external disturbance. To obtain direct yaw moment output, a direct yaw moment controller is given by using conventional sliding mode theory at first. In order to deal with large gains of sign function, which brings severe chattering problem in conventional first-order sling mode (FOSM) control, an improved adaptive second-order sliding mode control strategy is proposed based on super-twisting algorithm (STA) to estimate vehicle model errors and external lumped disturbance. In addition, taking the cost problem in practice into account, an integral sliding mode observer (ISMO) is constructed to estimate and calculate the actual values of sideslip angle indirectly on the basis of STA. Furthermore, a composite control strategy is proposed according to stability analysis, combining adaptive second-order sliding mode controller with ISMO. Eventually, comparative simulations are conducted in Carsim and Simulink. The results show that proposed control strategy can achieve superior tracking performance under the premise of tackling chattering phenomenon, and the response curves are smoother. Meanwhile, the influence of uncertain disturbance proves to be weakened significantly with STA-based scheme while performances of other control strategies are seriously affected, which verifies the robustness and effectiveness of proposed control strategy.

Disturbance estimation for robotic systems using continuous integral sliding mode observer

AbstractThis article presents a novel force‐sensor‐less method for the estimation of external forces for a general class of second‐order robotic systems. The method is based on the integral sliding mode observer (ISMO) which serves as a second‐order differentiator for the position measurement of the system. As a result, the system states and disturbance are estimated without explicitly using force and velocity measurements. To apply the ISMO to the general second‐order systems, a proper assumption is proposed to address their nonlinearity and discontinuity. The boundary‐layer method is applied to ensure that the virtual inputs of the observer are continuous such that the chattering phenomenon is attenuated. A Lyapunov‐based method is used to analyze the influence of the boundary layers on the convergence of the state‐ and disturbance‐estimation errors. This influence, mainly determined by the boundary‐layer scalars, is given in analytical forms as a reference for parameter selection. The method is evaluated by numerical simulation on a robot manipulator system and compared with a conventional sliding mode observer (SMO). The validation of the performance of the continuous ISMO indicates its generalizability to general second‐order robotic systems. Also, the advantage of continuous ISMO over the conventional SMO is reflected by its small estimation errors and superior responsiveness. In general, the proposed method in this paper may interest those who are seeking solutions for haptic robotic tasks without using force sensors.

Integral Sliding Mode Observer-Based Ultralocal Model for Finite-Set Model Predictive Current Control of Induction Motor

The ultralocal model of a plant expresses the behavior of the output variable in relation to the input without requiring any specific information about the plant. Replacing the conventional model of the system with an ultralocal model can effectively improve the robustness of the model predictive control (MPC) because all types of uncertainties that correlate with the system modeling are removed in the ultralocal model. This article proposes an integral sliding mode observer (ISMO)-based ultralocal model for the finite-set model predictive current control (FS-MPCC) of an induction motor (IM). The ISMO is constructed based on the Lyapunov theory to guarantee the stability of the proposed control method. Moreover, an analytical comparison is made between the proposed ISMO and the conventional extended state observer (ESO) in the application of the ultralocal model. This comparison shows that the stability of ESO depends on the unknown term of the ultralocal model. In contrast, the stability of the proposed ISMO does not rely on any terms of the ultralocal model. Experimental tests confirm that the proposed FS-MPCC method is practically applicable to the IM drive system. Also, it improves the robustness of the predictive method against model uncertainty.

Fuzzy Integral Sliding Mode Observer-Based Formation Control of Mobile Robots With Kinematic Disturbance and Unknown Leader and Follower Velocities

Fuzzy integral sliding mode observer (FISMO) based leader-follower formation control with the use of ceiling-mounted camera information is proposed for mobile robots with kinematic disturbance and no information of the velocities of leader and follower robots. Using only the posture information of both the leader and follower robots obtained from the camera sensor, the follower robot is made to follow the trajectory of a target robot that is introduced to render the leader-follower formation control more efficient. As a result, a kinematic tracking error model is proposed in terms of both the posture error between the target and follower robots and the posture error between the origin of the inertial reference frame and follower robot. Because the kinematic disturbance and the velocities of the leader and follower robots are not available, an FISMO is designed to simultaneously estimate the kinematic disturbance as well as the linear and angular velocities of the leader and follower robots. FISMO-based leader-follower output feedback formation control is then proposed based on the estimates of the kinematic disturbance and velocities and the backstepping-like feedback linearization tracking control. The experimental results comparing the proposed FISMO-based method with the robust distance-based tracking control method and integral sliding mode observer (ISMO)-based method are provided to verify the effectiveness of the proposed method.

Fuzzy Integral Sliding Mode Observer-Based Structure and Motion Estimation of Multiple Objects Using a Monocular Camera

This paper proposes a method to estimate the structure and motion (SaM) of multiple objects using a monocular camera. Most of the existing methods for SaM estimation are restricted to a single object by constraining the camera and object motions. In this study, SaM estimation is extended for the case of multiple (or simply two) objects and the restrictions on the camera and object motions and the necessity for the information related to the camera velocity are relaxed by using the relative information of the two objects. The relative motion dynamics of partially measurable states of multiple objects are first introduced, based on which the ranges (i.e., depths) of the two objects with respect to the camera, relative velocity between the objects, relative velocity between each object and the camera, and velocities of both the camera and objects are then estimated by using the proposed fuzzy integral sliding mode observer (FISMO). The following cases for the camera and objects-that have either static (stationary), dynamic (moving), or general (i.e., either static or dynamic) motions-are studied: i) two objects are at the same range with respect to the camera; and ii) two objects are at different ranges with respect to the camera. Several situations- in which the ranges of the two objects with respect to the monocular camera, relative velocity between the two objects, and velocities of the camera and objects can be estimated-are described in detail. Simulations and experimental results demonstrate the validity of the proposed method.

Sensorless Field Oriented SMCC Based Integral Sliding Mode for Solar PV Based Induction Motor Drive for Water Pumping

This article focuses on an efficient and robust speed sensorless control for a solar water pumping system consisting of solar photovoltaic (PV) array fed submersible induction motor drive (SIMD). The speed estimation and sensorless control of SIMD are quite demanding tasks. Additionally, the motor parameter variations lead to deteriorate sensorless control. Therefore, a sensorless control of submersible induction motor (SIM) requires accurate and robust stator current control. The sliding mode based dc link voltage regulator is adopted for regulating the motor power and the speed for single stage solar PV topology. The integral sliding mode observer based speed estimation of SIM and sliding mode current control (SMCC) for decoupling of current provide a robust control against motor parameters variation. The proportional integral controller based speed sensorless control is used, and its performance is verified with a developed sliding mode based control for motor parameter variation. The ISM-based sensorless control with SMCC for submersible motor serves several benefits in water pumping for agricultural purposes in the presence of uncertainities. In addition, it gives fast response during dynamics. The precise estimation of rotor speed is achieved for a wider range of speed under various dynamic conditions. The developed system with a single-stage PV topology and three-phase voltage source inverter fed IMD feeding a pump as a load is designed and implemented in MATLAB/Simulink. The suitability of the system is validated through experimentation in the laboratory.

Robust observer design for nonlinear strict feedback systems (Proposal of integral sliding mode observer)

Robust observer design for nonlinear strict feedback systems (Proposal of integral sliding mode observer)

Integral-Sliding-Mode-Observer-Based Structure and Motion Estimation of a Single Object in General Motion Using a Monocular Dynamic Camera

An integral sliding mode observer (ISMO)-based method for estimating the structure and motion (SaM) of an object in general motion is proposed using a monocular dynamic (moving) camera. As the unknown range and object velocity can be considered as disturbances and should be quickly estimated, it is necessary to maintain the robustness against these disturbances from the start by eliminating the reaching mode. Therefore, the ISMO-based method is proposed on the basis of a relative camera-object motion model. By formulating the relative motion model with three-dimensional measurable state variables, three unknown components among the range, unknown object velocity components, and unknown camera velocity components can be estimated by the proposed method in the following cases: i) the camera is in dynamic motion and the object is in semigeneral motion [i.e., initially static (stationary) and then in general (static or dynamic) motion], ii) the camera is in dynamic motion and the object is in general motion within a constrained space, and iii) both the object and camera are in general motion within a less constrained space when the range information is available. Simulation and experimental results demonstrate that the range and object velocity can be estimated with a satisfactory transient response using a monocular dynamic camera.

Integral Sliding-Mode Observer-Based Disturbance Estimation for Euler–Lagrangian Systems

In this article, a novel integral sliding-mode observer is proposed to estimate the external disturbance and velocity of Euler-Lagrangian systems. This method provides high bandwidth and precise estimation with only commanded input and position measurement. A system velocity measurement is not required to construct the sliding-mode manifold. The convergence of the estimation error to zero is theoretically in finite time, which is proven by a direct Lyapunov method utilizing the passivity property of Euler-Lagrangian systems. An integral sliding manifold is designed to reduce the reaching phase, such that the robustness of the estimation is enhanced. The method has been applied to a robot manipulator to estimate the joint velocity and external contact forces in a physical human-robot task. Simulations and experiments reveal that this novel method provides fast, precise, and robust estimation results and can be used to replace the measurement of an external force sensor. The successful application of this observer to a force-sensor-less admittance controller for a manipulator contributes to the implementation of a sensor-free safety framework for human-robot collaboration (HRC).

Second order integral sliding mode observer and controller for a nuclear reactor

Abstract This paper presents an observer-based chattering free robust optimal control scheme to regulate the total power of a nuclear reactor. The non-linear model of nuclear reactor is linearized around a steady state operating point to obtain a linear model for which an optimal second order integral sliding mode controller is designed. A second order integral sliding mode observer is also designed to estimate the unmeasurable states. In order to avoid the chattering effect, the discontinuous input of both observer and controller are designed using the super-twisting algorithm. The proposed controller is realized by combining an optimal linear tracking controller with a second order integral sliding mode controller to ensure minimum control effort and robustness of the closed-loop system in the presence of uncertainties. The condition for the selection of gains of discontinuous control based on the super-twisting algorithm is derived using a strict Lyapunov function. Performance of the proposed observer based control scheme is demonstrated through non-linear simulation studies.

An integral sliding mode observer for CPS cyber security attack detection.

Cyber-physical system (CPS) is a next-generation intelligent system integrating computing, communication, and control. As a unit of computing process and physical process, CPS is a new research field, where cooperative adaptive cruise control (CACC) is not only a microcosm of CPS but also a prerequisite for unmanned systems. CACC relies on the wireless communication network technology to achieve cooperative platooning control through vehicle-to-vehicle collaborative control methods. It can improve traffic efficiency and ensure safe driving. Once the wireless network is introduced by each vehicle to exchange information, it is vulnerable to cyber attacks, making attack detection necessary. In this paper, an integral sliding mode observer is designed to monitor malicious attacks. The proposed sliding mode observer not only retains the performance of traditional sliding mode control but also realizes the finite-time observation, avoiding the singular phenomenon that may occur in the traditional terminal sliding mode.

Robust Fault Identification of Turbofan Engines Sensors Based on Fractional-Order Integral Sliding Mode Observer

Abstract This paper presents a robust fault identification scheme based on fractional-order integral sliding mode observer (FOISMO) for turbofan engine sensors with uncertainties. The equilibrium manifold expansion (EME) model is introduced due to its simplicity and accuracy for nonlinear system. A fractional-order integral sliding mode observer is designed to reconstruct faults on sensors, in which the fractional-order integral sliding surface guarantees the fast convergence of reconstruction. The observer parameters is selected according to L2 gain theory in order to minimize the effect of uncertainties on the fault reconstruction signal. Simulations in Matlab/Simulink show high reconstruction accuracy of the proposed method despite the present of uncertainties.

Correction to: State and Faults Estimation Based on Proportional Integral Sliding Mode Observer for Uncertain Takagi–Sugeno Fuzzy Systems and Its Application to a Turbo-Reactor

Correction to: State and Faults Estimation Based on Proportional Integral Sliding Mode Observer for Uncertain Takagi–Sugeno Fuzzy Systems and Its Application to a Turbo-Reactor

Design of a proportional integral observer based on sliding mode principle for uncertain Takagi-Sugeno fuzzy systems: applications to a turbo-reactor

This work deals with the problem of state and fault estimation for systems described by Takagi-Sugeno fuzzy systems. The state and fault estimation is made using a proportional integral observer based on the sliding mode principle. Only sensor faults are considered in this work. In order to estimate this kind of fault, a particulate mathematical transformation is used. The application of this mathematical transformation to the initial system output allows to conceive an augmented system where the initial sensor fault appears as an unknown input. An adaptive mathematical form is used for the sign function to facilitate the determination of the proportional gains of the conceived observer. The observer convergence conditions are formulated in the form of linear matrix inequalities (LMI) allowing computing the observer gains and the Lyapunov theory is used to guarantee the system stability with faults. The proposed proportional integral sliding mode observer is applied to turbo-reactor showing the efficiency of the fault and state estimation.

State and Faults Estimation Based on Proportional Integral Sliding Mode Observer for Uncertain Takagi–Sugeno Fuzzy Systems and its Application to a Turbo-Reactor

This paper deals with the problem of state and faults estimation for nonlinear uncertain systems described by Takagi–Sugeno fuzzy structures (called also multiple models). In this work, actuator faults are considered as unknown inputs. The state and faults estimation is made using a structure of sliding mode observer where an integral term is added. This new structure of observer is called proportional integral sliding mode observer. The added integral term permits the unknown input estimation. For the sensor faults estimation, a mathematical transformation is used. The application of this mathematical transformation to the initial system output let to conceive an augmented system where the initial sensor fault appears as an unknown input. The observer convergence conditions are formulated in the form of Linear Matrix Inequalities allowing computing the observer gains. The proposed proportional integral sliding mode observer is applied to a numerical example showing the efficiency of the fault and the state estimation. In order to show the efficiency of the proposed method, it is applied to a turbo-reactor system.