Super-twisting Sliding Mode Algorithm Research Articles

Optimal Observer-Based Power Imbalance Allocation for Frequency Regulation in Shipboard Microgrids

This paper proposes a two-level control strategy based on a super-twisting sliding-mode algorithm (STA) to optimally allocate power imbalances in shipboard microgrids (SMGs) while achieving frequency regulation. The strategy employs an STA observer to estimate the unknown power load demand imbalances in finite time. This estimate is then passed to an online high-level optimal control framework to periodically determine the optimal sequence of power reference values for each energy storage device (ESS), minimising the operational cost of the SMG. The online optimised power reference values are interpolated and passed to the low-level STA control strategy to control the output power of each ESS. The efficacy of the proposed methods is demonstrated through numerical simulations conducted on a prototypical model of an SMG equipped with two ESSs, namely batteries and fuel cells with associated hydrogen storage.

Online parameters estimation schemes to enhance control performance in DC microgrids

This paper addresses the problem of achieving current sharing and voltage balancing in DC microgrids when the filter parasitic resistances of the Distributed Generation Units (DGUs) are unknown and potentially time-varying. Two schemes are proposed for current sharing and voltage balancing, which use only generated current and voltage measurements. The first scheme is a novel distributed adaptive control utilising the principles of back-stepping and passivity-based control design, intended for the case of constant parasitic resistances. The second scheme alters an existing stabilising controller for current sharing and voltage balancing by incorporating the estimation of the unknown, possibly time-varying, parasitic resistance. A Super-Twisting Sliding Mode Algorithm (STA) estimates the parasitic resistance and its bounded variations in finite-time. The simulation results using a DC microgrid composed of 4 DGUs demonstrate the performance of the proposed schemes.

Harmonic current compensator in battery charger using hybrid intelligent controller

This paper proposes a current-controlled hybrid power filter (CCHPF) to compensate source current harmonics produced by battery charger. Current harmonics are compensated by the voltage- and current-controlled active power filter, whereas in the proposed system, current-controlled hybrid power filter is used. CCHPF overcomes the limitation of voltage-controlled harmonic compensator, conventional proportional integral controller and the passive filter which has high power loss and which may deteriorate the system frequency with high harmonic attenuation. In the proposed control system, the super twisting sliding mode algorithm (STSMA) is used with a modified form of active power theory and cognitive emotional controller to compute the three-phase compensating current reference and the DC link current regulation. The compensated reference signal obtained by sliding mode controller is used to generate switching pulses to current-controlled active power filter (CCAPF) switches. The significant contribution of the proposed system includes the development of CCHPF and its control strategy based on PI- and emotional controller-tuned STSMA to reduce the harmonic distortion of the source current used in electric vehicle charging. The passive power filter is designed to eliminate fifth-order harmonic current. A major advantage of the super twisting sliding mode control algorithm is its adaptability and robustness, which will result in improved reference current tracking without causing chattering. CCHPF is widely used for compensating harmonics generated by electric vehicle battery chargers, UPS systems and industrial loads. The control technique is implemented in MATLAB Simulink environment for dynamic load conditions, and the obtained result compensates the harmonic current to a required level (total harmonic distortion of source current is 0.79%) satisfying IEEE 519-1992 standard.

Trajectory tracking control method based on adaptive super-twisting sliding mode

Aiming at the problem of trajectory tracking control in the process of missile network formation flying, based on the optimal nominal trajectory obtained by solving the two-point boundary value problem, combined with the anti-jamming characteristics of the sliding mode controller, a trajectory tracking control method based on the adaptive super-twisting sliding mode is proposed. First, on the basis of the terminal guidance section model, the two-point boundary value problem is solved through the idea of nonlinear programming to obtain the optimal nominal trajectory; Secondly, the tracking controller based on state deviation is designed in combination with the adaptive super-twisting sliding mode algorithm; Finally, the LQR trajectory tracking control method is introduced as a comparison method, and the effectiveness and feasibility of the sliding mode trajectory tracking method in the presence of initial state errors are verified by simulations, and the Monte Carlo simulation shows that the proposed method has good trajectory tracking control effect in the presence of different initial state errors.

Modified grey wolf optimised adaptive super-twisting sliding mode control of rotary inverted pendulum system

This paper proposes a modified grey wolf optimiser-based adaptive super-twisting sliding mode control algorithm for the trajectory tracking and balancing of the rotary inverted pendulum system. The super-twisting sliding mode algorithm severely alleviates the chattering present in the classical sliding mode control. It provides robustness against model uncertainties and external disturbances with the knowledge of the upper bounds of the uncertainties and disturbances. The gains of the super-twisting sliding mode algorithm are selected through adaptive law. Parameters of the adaption law are tuned using a modified grey wolf optimisation algorithm, a meta-heuristic optimisation technique. Lyapunov stability analysis is carried out to analyse the overall control system stability. The performance of the proposed control algorithm is compared with two other sliding mode control strategies present in the literature, therein showing better performance of the proposed control scheme.

SMC-Based Synchronization of Multiple Inertial Measurement Units with Application to Attitude Tracking Control

In this paper, the attitude control of aircraft with multiple inertial measurement units under the influence of unknown gyro zero drift and external disturbance is studied. First of all, the observers are designed to estimate the zero drift biases based on the consensus algorithm. The angular velocity used for aircraft control is obtained by compensating the biases. Then, considering the external disturbance in the aircraft motion, this paper introduces a super-twisting sliding-mode algorithm to design the observer in order to compensate the disturbance. In addition, based on the proposed observers, a controller is designed to realize attitude control of the aircraft with the gyro zero drift and the external disturbance. Finally, the simulation results are given to verify the effectiveness of the proposed control law.

Trade‐off between power extraction maximisation and fatigue reduction in wind farms via second‐order sliding mode control and min–max optimisation

In the last 10 years, with the increase of renewable energies, great attention is devoted to operation and maintenance of wind turbines and wind farms, which are the fundamental objectives to be guaranteed by the design of control systems. This study proposes a trade-off approach between fatigue reduction and power extraction for wind farm scenarios. The focus of this approach is to reduce maintenance costs for fatigue loads. This issue is solved by an optimisation problem, in which the maximum fatigue load among the turbines in the wind farm is minimised, then pitch angle and tip-speed ratio are given as reference points for each turbine in the wind farm. Furthermore, the super-twisting sliding mode algorithm is used for the rotor speed control and it shows that the turbines work at the optimised operating points. Moreover, the sliding mode controller produces continuous torques and improves the wind turbine performance by enhancing energy capture and reducing dynamic loads. The effectiveness of the method is illustrated through simulations of two wind farm scenarios. The results obtained with the proposed approach are compared with the case when each turbine in the wind farm tracks the maximum power operating point.

Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation

The problem of extracting maximum power from a photovoltaic (PV) system with negligible power loss is concerned with the power generating capability of the PV array and nature of the output load. Changing weather conditions and nonlinear behavior of PV systems pose a challenge in tracking of varying maximum power point. A robust nonlinear controller is required to ensure maximum power point tracking (MPPT) by handling nonlinearities of a system and making it robust against changing environmental conditions. Sliding mode controller is robust against disturbances, model uncertainties and parametric variations. It depicts undesirable phenomenon like chattering, inherent in it causing power and heat losses. In this paper, a supertwisting sliding mode algorithm based nonlinear robust controller has been designed for MPPT of a PV system which not only removes the chattering but also enhances the overall system’s dynamic response. Moreover, supertwisting sliding mode controller is robust against changing environmental conditions like change in temperature and irradiance. Noninverting DC-DC Buck-Boost converter has been used as an interface between source and the load. The efficiency of MPPT of a PV system depends upon the accuracy of reference for peak power voltage, therefore an efficient mechanism for reference generation has also been proposed in this work. The reference for peak power voltage has been generated by using a trained artificial neural network, which is to be tracked by proposed nonlinear controllers. Sliding mode controller (SMC) and synergetic controllers have also been designed for MPPT of a PV system in order to compare them with supertwisting sliding mode controller (ST-SMC). Global asymptotic stability of the system has been ensured by using Lyapunov stability criterion. The performance of the proposed nonlinear controllers has been validated in MATLAB/Simulink ODE 45 environment. ST-SMC has also been compared with recently proposed integral backstepping controller and other conventional MPPT controllers given in the literature. The simulation results show the better performance of ST-SMC in terms of best dynamic response and robustness.

DPC Based on ANFIS Super-Twisting Sliding Mode Algorithm of a Doubly-Fed Induction Generator for Wind Energy System

DPC Based on ANFIS Super-Twisting Sliding Mode Algorithm of a Doubly-Fed Induction Generator for Wind Energy System

An adaptive super-twisting sliding mode algorithm for robust control of a biotechnological process

In this paper, a robust super-twisting sliding mode control with adaptive tuning law is developed for a nonlinear biotechnological process, which takes place inside a continuous stirred tank bioreactor. A super-twisting algorithm (STA) is firstly designed to obtain high robustness as well as preserve fast convergence with high accuracy. The benefit of this approach is that its design procedure is independent of the prior knowledge of the bound value of the uncertainties and perturbations. However, the STA has a drawback that provides a chattering in the control loop. In order to overcome this drawback, an adaptive tuning algorithm is developed to adjust the STA control law without frequency switching and alleviate the undesired chattering phenomenon. Then, the robustness can be achieved despite the existence of the unknown uncertainties and external perturbations for the nonlinear process. In addition, a formal proof of the global uniform asymptotic stability based on Lyapunov criterion of the closed-loop process is derived. Several simulation results show that the proposed adaptive super-twisting algorithm guarantees the performance of the STA under external disturbance and parametric uncertainty with less chattering and illustrate the overall performance improvements.

Robust identification of unknown inputs in electrical stimulation of ex-vivo animal models

The non-parametric identification problem aims to estimate a suitable model based on the response produced by a given stimulus on an uncertain model. Complementary, input estimation considers a different problem where the model and the output are known. However, if neither model nor input is known, the identification problem seems to be more complicated. This is a major challenge in electrophysiological systems where the output signal can be measured, but the biological system is uncertain and the stimuli are unknown. The aim of this study was to introduce a novel methodology that may estimate the uncertain input in this more complex situation where the biological model of the system under analysis is uncertain and the input stimuli must be estimated accurately. A two step non-parametric identification scheme based on differential neural networks (DifNN) and the second order super-twisting sliding mode algorithm (STA) identified the input stimulus. The STA makes a robust exact estimation of the output signal. The DifNN produces an internal non-parametric mathematical modeling of the input-output relationship executed without the input information. Learning laws for both levels of DifNN modeling were presented as part of the uncertain input identification process. The estimation of uncertain visual stimulus applied over the retina in an ex-vivo avian model (EVAM) served to test the method proposed in this study. The output information corresponded to the electrophysiological voltage variation in the optical nerve.

Application of Super-Twisting Sliding Mode Controllers in Direct Field-Oriented Control System of Six-Phase Induction Motor: Experimental Studies

Abstract This paper presents the direct field-oriented control (DFOC) method of six-phase induction motor with the application of super-twisting sliding mode algorithm. The mathematical model of the six-phase induction motor has been described. The descriptions of the space vector modulation (SVM) method and the super-twisting algorithm have been presented. The DFOC method with super-twisting sliding mode controllers has been described. The experimental studies of the DFOC method with super-twisting controllers have been performed, and the results of these studies are presented and discussed.

Accelerated adaptive super twisting sliding mode observer‐based drive shaft torque estimation for electric vehicle with automated manual transmission

The suddenly released torque that accumulated in the elastic drive shaft will bring torsional vibration and jerking feel at the shifting moment. A novel sliding mode observer is proposed to estimate the torque in drive shaft for a motor-transmission integrated powertrain system. Non-linear external characteristics of a driving motor and non-linear drag torque are considered in the electric powertrain system. In order to attenuate the chatting problem, the second-order super twisting sliding mode algorithm with an adaptive gain is adopted. Furthermore, a term `system damping' is introduced to accelerate the estimation error convergence. The proposed estimation algorithm is tested on test rig for typical operating conditions. The results show that the torque in drive shaft can be estimated satisfactorily and the tracking error converges to 0 in a short time.

Robust sensorless observer-based adaptive sliding modes control of synchronous motors

Using a nonlinear complete order model of a synchronous motor, a robust second order sliding mode observer based control scheme is proposed. For that, a generalized super-twisting 3rd order observer is proposed for nonlinear systems. Based on the proposed observer scheme, a robust rotor flux observer is designed, then, a stator current observer is proposed using a classical super-twisting algorithm for extracting information of the rotor speed by means of the equivalent control method. The control design for the output tracking of a desired reference signal for the rotor speed is carried out with a classical super-twisting sliding mode algorithm and adaptive backstepping techniques. Due to the number of inputs, the flux in the excitation winding, and the direct component of the stator currents are also regulated. Numeric simulations predict a good performance of the closed-loop synchronous motor with parameter variations.

Robust Voltage Control of Floating Interleaved Boost Converter for Fuel Cell Systems

In this paper, a robust voltage control is designed for a floating interleaved boost converter with high voltage gain. The proposed controller has an inner loop based on super-twisting sliding mode algorithm, which has continuous control signal and the sliding surface is defined for the inductor current. The reference current value is generated by active disturbance rejection control (ADRC) algorithm in the outer loop, based on the output voltage error. The stability of the sliding mode inner loop and the ADRC outer loop are proven using Lyapunov stability theorem and Routh–Hurwitz criteria, respectively. The robustness of the proposed controller is analyzed in depth, and validated by the simulations and experimental results obtained with a 100 W prototype converter.

A Super-Twisting-Like Algorithm and Its Application to Train Operation Control With Optimal Utilization of Adhesion Force

The friction between wheel and track is usually called adhesion force, and it is the critical factor for the movement of trains. On one hand, excessive driving force of a train may lead to insufficient utilization of the adhesion effect and cause wasted energy; on the other hand, insufficient driving force of a train brings inefficient train operation. To balance the issues of energy consumption, operational efficiency, and security, it is necessary to control a train to obtain its maximal adhesion force, particularly in the cases of fast acceleration and emergency braking. However, since engineering experiments indicate a complex nonlinear relationship between the adhesion force and the slip ratio of a train, such a control problem is difficult and challenging, particularly when the optimal slip ratio is unknown. Facing this problem, this paper proposes a novel control method based on the modification of the famous super-twisting sliding mode algorithm, and rigorous mathematical analysis is given to guarantee the ultimate boundedness of the proposed algorithm. Furthermore, by considering four different control scenarios, detailed control and estimation algorithms are both proposed. Simulation result verifies that the proposed control strategy can control the train to obtain its maximum adhesion force.

On speed estimation of permanent magnet synchronous motor using adaptive robust position observer and differentiator

This article concerns a novel method for sensorless speed control of Permanent Magnet Synchrounous Motor based on adaptive supertwisting sliding mode algorithm. This algorithm is used for motor position observation and for the derivation of the position to deduce the speed. The stability is deduced based on the Lyapunov theory. The Experimental tests, carried out on a test bench, show the pertinence of the proposed scheme.

Stability preserving maps for finite-time convergence: Super-twisting sliding-mode algorithm

The super-twisting algorithm (STA) has become the prototype of second-order sliding mode algorithm. It achieves finite time convergence by means of a continuous action, without using information about derivatives of the sliding constraint. Thus, chattering associated to traditional sliding-mode observers and controllers is reduced. The stability and finite-time convergence analysis have been jointly addressed from different points of view, most of them based on the use of scaling symmetries (homogeneity), or non-smooth Lyapunov functions. Departing from these approaches, in this contribution we decouple the stability analysis problem from that of finite-time convergence. A nonlinear change of coordinates and a time-scaling are used. In the new coordinates and time–space, the transformed system is stabilized using any appropriate standard design method. Conditions under which the combination of the nonlinear coordinates transformation and the time-scaling is a stability preserving map are given. Provided convergence in the transformed space is faster than O(1/τ)—where τ is the transformed time—convergence of the original system takes place in finite-time. The method is illustrated by designing a generalized super-twisting observer able to cope with a broad class of perturbations.