Terminal Attractor Research Articles

Robust optimum design of maximum power point tracker for photovoltaic power generation systems

The photovoltaic array’s power-voltage curve usually appears multiple peaks in the presence of partial occlusion and fault conditions, and the habitual maximum power point tracking (MPPT) algorithm is not easy to discover the MPP of the global space, thus falling into the local range, which extremely decreases the efficiency of the photovoltaic power generation system. In this paper, a robust optimum design technology employing an enhanced terminal attractor (ETA) and a monkey-king genetic algorithm (MKGA) are put forward. The proposed technology can be adopted for the maximum power point tracker of the photovoltaic power generation system, therefore yielding fast limited-time convergence and global extremum. The proposed photovoltaic power generation system is also implemented on the basis of Texas Instruments digital signal processor, to achieve the improvement of the tracking speed and good performance in the transient as well as steady-state. Simulation and experimental results have been provided to demonstrate the virtue of the proposed technology.

A Time-Specified Zeroing Neural Network for Quadratic Programming With Its Redundant Manipulator Application

In this article, to solve a time-varying quadratic programming with equation constraint, a new time-specified zeroing neural network (TSZNN) is proposed and analyzed. Unlike the existing methods such as the Zhang neural network with different activation functions and a finite-time neural network, the TSZNN model is incorporated into a terminal attractor, and the convergent error can be guaranteed to reduce to zero in advance (instead of the finite-time property). The main advantage of the TSZNN model is that it is independent of the initial state of the systematic dynamics, which is much astonishing to the finite convergence relying on the initial conditions and comprehensively modifies the convergent performance. Mathematical analyses substantiate the prespecified convergence of the TSZNN model and high convergent precision under the situation of various convergent time settings. The prespecified convergence of the TSZNN model for a quadratic programming problem has been mathematically proved under different convergent constant settings. In addition, simulation applications conducted on a repeatable trajectory planning of the redundant manipulator are studied to demonstrate the validity of the proposed TSZNN model.

Neuro-adaptive fast integral terminal sliding mode control design with variable gain robust exact differentiator for under-actuated quadcopter UAV

In this paper, a robust global fast terminal attractor based full flight trajectory tracking control law has been developed for the available regular form which is operated under matched uncertainties. Based on the hierarchical control principle, the aforesaid model is first subdivided into two subsystems, i.e., a fully-actuated subsystem and an under-actuated subsystem. In other words, the under-actuated subsystem is further transformed into a regular form whereby the under-actuated characteristics are decoupled in terms of control inputs. In the proposed design, the nonlinear drift terms, which certainly varies in full flight, are estimated via functional link neural networks to improve the performance of the controller in full flight. Besides, a variable gain robust exact differentiator (VG-RED) is designed to provide us with estimated flight velocities. It has consequently reduced the noise in system’s velocities and has mapped this controller as a practical one. The finite-time sliding mode enforcement and the states’ convergence are shown, for all flight loops, i.e., forward flight and backward flight, via the Lyapunov approach. All these claims are verified via numerical simulations and experimental implementation of the quadcopter system in a Matlab environment. For a more impressive presentation, the developed simulation results are compared with standard literature.

Robust Optimal Tracking Control of a Full-Bridge DC-AC Converter

This paper develops a full-bridge DC-AC converter, which uses a robust optimal tracking control strategy to procure a high-quality sine output waveshape even in the presence of unpredictable intermissions. The proposed strategy brings out the advantages of non-singular fast convergent terminal attractor (NFCTA) and chaos particle swarm optimization (CPSO). Compared with a typical TA, the NFCTA affords fast convergence within a limited time to the steady-state situation, and keeps away from the possibility of singularity through its sliding surface design. It is worth noting that once the NFCTA-controlled DC-AC converter encounters drastic changes in internal parameters or the influence of external non-linear loads, the trembling with low-control precision will occur and the aggravation of transient and steady-state performance yields. Although the traditional PSO algorithm has the characteristics of simple implementation and fast convergence, the search process lacks diversity and converges prematurely. So, it is impossible to deviate from the local extreme value, resulting in poor solution quality or search stagnation. Thereby, an improved version of traditional PSO called CPSO is used to discover global optimal NFCTA parameters, which can preclude precocious convergence to local solutions, mitigating the tremor as well as enhancing DC-AC converter performance. By using the proposed stable closed-loop full-bridge DC-AC converter with a hybrid strategy integrating NFCTA and CPSO, low total harmonic distortion (THD) output-voltage and fast dynamic load response are generated under nonlinear rectifier-type load situations and during sudden load changes, respectively. Simulation results are done by the Matlab/Simulink environment, and experimental results of a digital signal processor (DSP) controlled full-bridge DC-AC converter prototype confirm the usefulness of the proposed strategy.

Design of rapid exponential integral nonlinear tracking differentiator

ABSTRACT In this paper, a tracking differentiator with novel acceleration function is proposed. An exponential integral function with adjustable slope near the equilibrium point is designed as an acceleration function to construct the tracking differentiator. By adding the linear function and the terminal attractor function to the tracking differentiator, the tracking performance and response speed are improved compared with the existing acceleration function. The frequency domain characteristics are analyzed based on the least squares sweep test and the stability is proved by Lyapunov method. Numerical simulations show that the rapid exponential integral nonlinear tracking differentiator can effectively suppress the high-frequency noise and make the tracking input signal more accurate.

Fixed-time synergetic control for chaos suppression in endocrine glucose–insulin regulatory system

This paper, puts forward the control of chaos phenomena in an important endocrine glucose–insulin metabolic regulatory system. Using a robust fixed-time synergetic controller, which has been designed to regulate the plasma glycemic level in type 1 diabetic patients (T1D). In the view of the recent improvement in synergetic control algorithm, terminal attractor technique and fixed time stability. The introduced technique has the benefit of utilizing a continuous control law. In addition, the proposed control algorithm, other than being without chattering, has the advantage of being converging in finite fixed-time. Lyapunov framework is exploited to ensure the stability of the controlled system. Simulation results of the designed synergetic controller, are exhibiting the ability of the proposed control technique for rapidly achieving normoglycemia in type 1 diabetes patients and stabilizing the biological disorder in a robust manner. These features make it interesting because one of the great matters in the diabetes mellitus treatment is the search of the best controller acting as an artificial pancreas and a safe and efficient control algorithm of the plasma glucose level and control devices enhancement, which relieves the diabetic subjects.

Resilient Secondary Voltage Control of Islanded Microgrids: An ESKBF-Based Distributed Fast Terminal Sliding Mode Control Approach

This paper proposes a distributed secondary voltage control method based on extended state Kalman-Bucy filter (ESKBF) and fast terminal sliding mode (FTSM) control for the resilient operation of an islanded microgrid (MG) with inverter-based distributed generations (DGs). To tackle the co-existence of multiple uncertainties, a unified modelling framework is proposed to represent the set of different types of disturbances, including parameter perturbation, measurement noise, and immeasurably external variables, by an extended state method. Kalman-Bucy filter is then applied to accurately estimate the state information of the extended DG model. In addition, based on the accurate estimation, a fast terminal sliding mode (FTSM) surface with terminal attractors is designed to maintain the system stability and accelerate the convergence of consensus tracking, which significantly improves the performance of secondary voltage control under both normal and plug-and-play operation. Finally, case studies are conducted in both MATLAB/Simulink and an experimental testbed to demonstrate the effectiveness of the proposed method.

Fixed-time terminal synergetic observer for synchronization of fractional-order chaotic systems.

In this paper, a fixed-time terminal synergetic observer for synchronization of fractional-order nonlinear chaotic systems is proposed. First, fixed-time terminal attractors for fractional-order nonlinear systems are introduced on the basis of fixed-time stability of integer-order nonlinear differential equations and on defining particular fractional-order macro-variables. Second, a new synergetic observer dedicated to the synchronization of fractional-order chaotic systems is developed. The proposed observer converges in a predefined fixed-time uniformly bounded with respect to initial conditions. Thanks to the step-by-step procedure, only one communication channel is used to achieve the synchronization. Third, a fixed-time synergetic extended observer with unknown input is constructed to simultaneously estimate the state variables and to recover the unknown input. Finally, computer simulations are performed to illustrate the efficiency of the proposed synchronization method and its application in a secure communication scheme.

Adaptive finite integral non-singular terminal synergetic control of nth-order nonlinear systems

This paper proposed a novel finite synergetic control scheme. This controller utilizes an integral non-singular terminal attractor technique that guarantees the finite-time convergence of the states, solves the singularity problem of a conventional terminal attractor manifold, and provides a chattering-free control signal. The principles of this controller are introduced and demonstrated for second and nth-order nonlinear systems. The stability of the controller is verified using Lyapunov stability theory. In addition, an interval type-2 fuzzy logic system is implemented to improve the overall performance of the controller. Analysis of a robotic manipulator and a servo-hydraulic actuator is used to illustrate the efficacy of the proposed Adaptive Finite Integral Non-singular Terminal Synergetic Control (AFINTSC).

Neuro-controller for antagonistic bi-articular muscle actuation in robotic arms based on terminal attractors

The conventional limitations of the robotic actuation mechanisms have led to many researchers needing to explore novel biomimetic motor mechanisms as the antagonistic human motor system. In this way, it is of interest to understand the inherent adaptive stiffness, or compliance, and modulation, in different alternative actuation architectures such as the antagonistic bi-articular (AbA) system. These novel AbA actuation mechanisms are characterized by resembling the efficient tendon and muscle build-up over our skeletal structure. In this paper, we propose a Cartesian neuro-controller for a robot manipulator actuated by a simplified adaptive viscoelastic linear AbA system. It is shown that the adaptive closed-loop system enforces terminal attractors, induced by a continuous model-free sliding mode control, simultaneously with a learning algorithm to compensate parametric uncertainties of AbA system through a low dimensional neural network. Numerical simulation results exhibit the feasibility of this approach.

FPGA-Based Predictive Speed Control for PMSM System Using Integral Sliding-Mode Disturbance Observer

This article proposes a fast-terminal cost index (FTCI) based continuous control set model-based predictive speed control (CCS-MPSC) to improve the tracking performance of CCS-MPSC. First, an extended surface-mounted permanent magnet synchronous motor model that incorporates parameter mismatches and external interference into a disturbance term is utilized to facilitate the determination of parameters and reduce computational complexity. Second, by introducing a differential feedforward term, an exponential convergence cost index (ECCI) based CCS-MPSC has a smaller lag error than conventional CCS-MPSC when tracking a given speed. Third, an FTCI, which simultaneously includes a terminal attractor, a differential feedforward term, and a lumped disturbance term, is developed to improve the tracking performance of ECCI further. Finally, a fast-terminal integral sliding-mode observer is designed to estimate the lumped disturbance and the differential feedforward term fast and accurately. Experiments of proportional-integral control, conventional CCS-MPSC, ECCI, and FTCI have been carried out on an FPGA-based hardware system, and the experimental results validate the excellent tracking performance of the FTCI strategy.

Experimental Performance Comparison of Various Sliding Modes Controlled PWM Inverters

In this paper, the study of various sliding modes has suggested that integral terminal attractor (ITA) performs satisfactorily in regulating the output during load perturbations. Though all sliding modes are insensitive to system uncertainties, their unique properties are greatly different. The sliding mode control (SMC) and terminal attractor (TA) inherently have steady-state error, but TA assures the good transient response. Though both integral sliding mode control (ISMC) and ITA possess less steady-state error, better transient response is ITA. Experimental results in this study show that the ITA can be regarded as a viable controller for pulse-width modulated (PWM) inverter applications, such as photovoltaic inverter, wind power inverter and fuel cell inverter.

Modified PSO-based nonlinear controllers applied to a DC-DC converter

Control of dc/dc buck converter is a complex task due to the nonlinearity inherent in the converter and introduced by the external changes. A robust synergetic controller for the control of dc/dc buck converter is described in this study. An adaptive terminal synergetic control is developed based on robust synergetic control theory and terminal attractor techniques. The method estimates the boundary of parameter uncertainty and external disturbance by adaptive law. The idea behind this strategy is to use the terminal synergetic control (TSYC) approach to assure finite time convergence of the output voltage error to the equilibrium point, and integrate an adaptive law to handle uncertainties is provided resulting in enhancing robustness as well as a better transient performance compared to the conventional control. In addition, modified PSO algorithms will be used to optimise controller's parameters using an ITAE criterion. The results show and demonstrate the effectiveness and feasibility of the proposed approach.

Study and Application of Intelligent Sliding Mode Control for Voltage Source Inverters

In this paper, an intelligent sliding mode controlled voltage source inverter (VSI) is developed to achieve not only quick transient behavior, but satisfactory steady-state response. The presented approach combines the respective merits of a nonsingular fast terminal attractor (NFTA) as well as an adaptive neuro-fuzzy inference system (ANFIS). The NFTA allows no singularity and error states to be converged to the equilibrium within a finite time, while conventional sliding mode control (SMC) leads to long-term (infinite) convergent behavior. However, there is the likelihood of chattering or steady-state error occurring in NFTA due to the overestimation or underestimation of system uncertainty bound. The ANFIS with accurate estimation and the ease of implementation is employed in NFTA for suppressing the chatter or steady-state error so as to improve the system’s robustness against uncertain disturbances. Simulation results display that this described approach yields low distorted output wave shapes and quick transience in the presence of capacitor input rectifier loading as well as abrupt connection of linear loads. Experimental results conducted on a 1 kW VSI prototype with control algorithm implementation in Texas Instruments DSP (digital signal processor) support the theoretic analysis and reaffirm the robust performance of the developed VSI. Because the proposed VSI yields remarkable benefits over conventional terminal attractor VSIs on the basis of computational quickness and unsophisticated realization, the presented approach is a noteworthy referral to the designers of correlated VSI applications in future, such as DC (direct current) microgrids and AC (alternating current) microgrids, or even hybrid AC/DC microgrids.

An Amplitude‐ and Rate‐Saturated Controller for Linear Plants

AbstractThis paper proposes a new nonlinear controller applicable to single‐input linear systems under bounded disturbance. The controller provides control signals satisfying specified amplitude and rate‐of‐change limitations. This feature is realized by its sliding mode‐like structure comprising a set‐valued function. The controller also employs a state‐dependent parameter to broaden the region of attraction and to shrink the terminal attractor. In addition, this paper provides a discrete‐time implementation of the proposed controller based on a model‐based implicit discretization scheme. Numerical examples show the validity of the proposed controller.

Terminal Synergetic Control for Blood Glucose Regulation in Diabetes Patients

In this paper, a new robust terminal synergetic control scheme is proposed to regulate blood glucose level in diabetic patients (type I diabetes), based on recently developed synergetic control and a terminal attractor technique. The technique presented has the advantage of using a continuous control law. Moreover, the proposed control scheme, besides being chattering free, has the characteristics of finite time convergence. Lyapunov synthesis is adopted to ensure controlled system stability. Simulation results of terminal synergetic control are compared to classic synergetic and second-order sliding mode control (SMC) performance, demonstrating that the proposed control method allows for rapidly achieving normoglycemia in type I diabetes patients.

DSP-based implementation of fast terminal synergetic control for a DC–DC Buck converter

Finite time convergence based on robust synergetic control (SC) theory and terminal attractor techniques is investigated. To this end a fast terminal synergetic control law (FTSC) is applied to drive a DC–DC Buck converter via simulation and through a dSpace based experimental setup to validate the approach. As robust as sliding mode control, the synergetic approach used is chattering free and provides rapid convergence. Efficacy of the proposed fast terminal synergetic controller is tested for step load change and output voltage variation and results compared to classical synergetic and PI control. Experimental validation using dSpace DS1104 confirms the results obtained in simulation showing the soundness of this approach compared to synergetic and PI controllers.

Adaptive neuro-fuzzy nonsingular terminal attractor and its application in high-performance AC power conditioners

This paper describes an adaptive neuro-fuzzy (ANF) nonsingular terminal attractor (NTA) that can control output voltage in an AC power conditioner. The NTA experiences the convergence of the system trajectory to zero within finite time, and it can avoid singularity problems. However, when the estim ated value exceeds or falls short of the uncertain system boundary, chattering or steady-state error problems occur. Therefore, the ANF technique is introduced as a computationally rapid and mathematically simple means of reducing the chatter and steady-state errors occurring in NTA if the estimated value of the uncertain system boundary is not met. The Lyapunov theory proves that the proposed AC power conditioner is stable and convergent. Associating NTA with the ANF technique provides the AC power conditioner with low distortion and fast transience in the presence of various loading. Experimental results from a 1 kW prototype confirm the mathematical analyses and simulations. Because the proposed AC power conditioner offers significant advantages over the classic terminal attractor (TA)-controlled AC power conditioner in terms of complexity and ease of implementation, this paper represents a useful reference for designers of artificial intelligence.

Finite-time synchronization control and parameter identification of uncertain permanent magnet synchronous motor

In this paper, the robust finite-time synchronization control and parameter identification problems for uncertain permanent magnet synchronous motor are discussed in detail. Based on finite-time control theory, by using adaptive control approach, a novel adaptive finite-time control scheme is proposed firstly. Then, a robust adaptive finite-time synchronization control method is developed depending on the terminal attractors with updating tuning parameters, which can not only guarantee the synchronization of permanent magnet synchronous motor with uncertain parameters in a shorter finite time, but also can guarantee these uncertain parameters to be identified effectively simultaneously. Finally, some simulation and comparison results are provided to demonstrate the effectiveness and usefulness of the developed results.

Reaching phase free adaptive fuzzy synergetic power system stabilizer

Abstract In this paper, an adaptive fuzzy power system stabilizer is developed based on robust synergetic control theory and terminal attractor techniques. The main contribution consists in making the dynamic system insensitive to parameters variation. This aim is achieved using a new synergetic controller design such that power system states start, evolve and remain on a designer chosen attractor toward the equilibrium point therefore avoiding transient mode. Rendering the design more robust, fuzzy logic systems are used to approximate the unknown power system dynamic functions without calling upon usual model linearization and simplifications. Based on an indirect adaptive scheme and Lyapunov theory, adaptation laws are developed to make the controller handle parameters variations due to the different operating conditions occurring on the power system and to guarantee stability. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results show the effectiveness and robustness of the proposed stabilizer in damping power system oscillations under various disturbances and better overall performance than classical PSS and some other types of power stabilizers.