This paper proposes a novel dual three-phase SVM for six-phase multilevel inverter to control a six-phase induction machine (SPIM). The main idea is to control the six-phase multilevel Inverter as two three-phase multilevel inverters separately by space vector modulation of the N-level three-phase separate DC source (SDCS) inverter. This enables a great simplification of the algorithm control for six-phase multilevel (N level) inverter drive. In six-phase SVM, N degrees vectors ( N = 2 two level, 3 level or 4 level, implies 64 vectors, 729 vectors, 15625 vectors respectively) were used. However, in a proposed dual three-phase SVM, we use N-3 vectors N = 2 level, 3 level or 4 level implies 8 vectors, 27 vectors, 125 vectors respectively) to control the six-phase multilevel inverters as two three-phase multilevel inverters with the same three-phase multilevel SVM. Whereas the first three-phase Inverter is composed of 1,9, and 5 phases, and the second three pimseiscomposed of 24, and opimses. The simulationresults of the indirect field-oriented control (IFOC) of six-phase induction machine drive fed by stacked multilevel inverters are given to highlight the performance of the proposed dual three-phase SVM. Moreover, multilevel topologies are used to reduce the current THD distortion as well as the total semiconductor losses.

The key goal of this effort is to develop an efficient control system for a three-phase cascaded H-bridge multilevel inverter powered by the photovoltaic (PV) system. The power for the system is generated through the use of PV modules, which serve as DC inputs for the cascaded H-bridge multilevel inverter. The authors aim to achieve a nearly sinusoidal signal at the voltage level and are specifically focused on minimizing the total harmonic distortion (THD) to the smallest possible value. Hence, an advanced N-level space vector modulation (SVM) is developed to ensure an appropriate control for the cascaded inverter. The aim is to design an effective control strategy to increase inverter efficacy and, thus, supply the best output quality. In addition, a robust approach to the maximum power point (MPP) tracking (MPPT) technique is developed based on an adaptive perturb and observe (P&O) algorithm to ensure superior tracking of the MPP. The developed algorithm eliminates 90% of the power curve area in the search space process and only maintains 10% of the area that includes the MPP. Each PV system employs its own improved MPPT control. The numerical results confirm that the enhanced P&O algorithm attains a precise response with superior efficiency and a fast response under the fast alteration of environmental conditions. Hence, the energy loss is reduced. The simulation results validate the effectiveness of this study, highlighting the high efficiency of the control strategy and the enhanced performance of the proposed scheme with lesser THD values.

This work presents the control of a three phase cascaded H-Bridge Multi-Level Inverter supplied by the photovoltaic system. In order to obtain a nearly sinusoidal signal at the voltage level, we are interested in obtaining the smallest value of THD. Hence, a N-level space vector modulation (SVM) is used to control this inverter. The power source is produced from photovoltaic modules utilized as DC inputs for the cascaded H-Bridge Multilevel Inverter. The objective of this work aims to design a control strategy to supply the best output quality. The algorithm P&O is applied to extract from the panels the maximum energy. Each PV system employs with its own MPPT control. The PV system outputs are investigated as entries to the cascaded H-Bridge Multi-Level Inverter for achieving a staircase waveform output. A matlab/simulink is used to validate the system performance. The simulation results prove the efficiency of this work, which indicates the high effectiveness of the control and the superior performance of proposed scheme.

This work suggests a dual three-phase Space Vector Modulation (SVM) for the Diode Clamped Multilevel Inverter (DCMI) to ensure a robust control of Dual Star Induction Motor (DSIM). The principal scheme is investigated to apply the same control of the six-phase multilevel inverter by two three-phase multilevel inverter to drive the DSIM. The use of classic SVM control offers significant simplifications for controlling a six phase five levels inverter. The proposed control approach employs according to the hybridization of various conversion functions to establish the modulation strategy for each voltage vector and its placement in the plane of voltage modulation in distinctly and easy manner. A numerical simulation under MATLAB/Simulink is carried out to evaluate the Indirect Field Oriented Control (IFOC) of DSIM drive fed by multilevel inverters. The simulation outcomes clearly reveal good performance of the designed control strategy in terms of THD and control efficiency.

This work consists in developing a method for the non-linear control of a variable speed wind turbine system using a permanent magnet synchronous generator (PMSG) for power generation injected in grid. Objective of backstepping control is to improve performance of conversion system with choice of appropriate regulated variables. PMSG is connected to grid via back-to-back voltage sources converters (BtB VSCs), consisting of a two-stage rectifier and a five-stage NPC inverter, operated by Space Vector Pulse Width Modulation (SVPWM). In control system of generator-side converters Maximum Power Point Tracking (MPPT) algorithm has been employed. Firstly, modelling of wind turbine system has been discussed. Thereafter, the control schemes detailed description based on Backstepping Control (BSC) for control of generator and grid-side converters based on Lyapunov stability technique has been described. Furthermore, the dc-bus is regulated using BSC. A prototype is tested under varying wind conditions. The present work has been treated using the Matlab/Simulink. Simulation results proved good performance of proposed methods of BSC.

Unmanned aerial vehicles are used today in many real-world applications. In all these applications, the vehicle endurance (flight time) is an important constraint that affects mission success. This study investigates the limitations of embedded energy for a quadrotor aerial vehicle. We consider a quadrotor simple tasked to travel from an initial hover configuration to a final hover configuration. In order to have a precise approximation of the consumed energy, we propose a power consumption model with battery dynamic, motor dynamic, and rotor efficiency function. We then introduce an optimization algorithm to minimize the energy consumption during quadrotor aerial vehicle mission. The proposed algorithm is based on an optimal control problem formulated for the quadrotor model and solved using nonlinear programming. In the optimal control problem, we seek to find control inputs (rotor velocity) and vehicle trajectory between initial and final configurations that minimize the consumed energy during a point-to-point mission. We extensively test in simulation experiments the proposed algorithm under normal and windy weather conditions. We compare the proposed optimization method with a nonlinear adaptive control approach to highlight the saved amount of energy.

This article proposes a new six-phase multi-level space vector modulation (SVM) method to control a six-phase induction machine (SPIM) driven by a multi-level separate direct current (DC) source (SDCS) inverter. The main idea is to control the six-phase multilevel inverter by extending the SVM algorithm developed for the three-phase multi-level inverters. This allows one to have a new modulation technique for a six-phase multi-level inverter and a great simplification of the classical six-phase SVM control algorithm. The Park's transformation is used to obtain two decoupled space vectors (d-q frame and x-y frame) of the six-phase inverter. The components (d-q) being responsible for the electromechanical energy conversion in the SPIM are employed to implement an appropriate SVM technique for six-phase inverters to obtain six-phase components of the output voltage vectors, so the components (x-y) are eliminated. For that, the d-q frame is used to obtain a simple and general algorithm for this six-phase multi-level inverter. Some appropriate numerical simulations that involve an SPIM drive with a multi-level SDCS inverter are conducted to highlight the performance of the proposed six-phase SVM and to verify the accuracy of the proposed algorithm. The simulation results show clearly that the proposed multi-level topologies can adequately reduce the total harmonic distortion (THD) distortion of the currents as well as the total losses of the semiconductors.

In this study, a novel adaptive hybrid fuzzy system-wavelet neural network (FS-WNN) approximator is developed for control of uncertain non-linear multiple-inputs and multiple-outputs systems. The main idea is to use two different universal approximators to approximate each unknown non-linear function of the dynamic system. The first approximator is a FS and the second approximator is a WNN. Each approximator approximates the system's unknown functions independently. Then, the two approximations of the same function are combined using a modulation technique. The used modulation attributes to the first approximation a scaling factor and attributes its complementary factor to the second approximation to synthesise a hybrid optimal approximation of the function. Thus, system unknown functions are accurately approximated using the novel adaptive hybrid approximator that gathers the advantages of both approximators. The adaptation laws of the developed hybrid approximator are derived using Lyapunov's direct method to ensure the stability of the closed loop system. A simulation study is given to evaluate the performance of the proposed hybrid FS-WNN approximator in comparison with the FS and WNN approximators in indirect adaptive control of a two-link robot manipulator.

This paper proposes a novel hybrid intelligent backstepping controller (HIBC) for control of uncertain nonlinear chaotic systems. The novel HIBC uses a hybrid Fuzzy System-Wavelet Neural Network identifier (FS-WNN) to identify the system unknown dynamics. The hybrid FS-WNN identifier is a combination of a Fuzzy System approximator (FS) and a Wavelet Neural Network approximator (WNN). Each approximator approximates the unknown functions of the system separately. Then, the different approximations of the same function are combined using modulation technique. Since the hybrid FS-WNN uses wavelet neural networks and fuzzy systems, its approximation accuracy and generalization capability are superior to those of conventional individual wavelet neural network and fuzzy system for system identification. The adaptation laws of the control system are obtained using Lyapunov's method to guarantee the asymptotic stability of the closed loop system. The proposed HIBC is applied to control a chaotic uncertain nonlinear system. Simulation results prove that the proposed HIBC can achieve a superior tracking performance by incorporation of WNN identifier, FS identifier, adaptive backstepping control technique, and Lyapunov's theory of stability. A comparison of the HIBC performance with further models in literature is given to highlight the efficiency of the proposed control scheme.

This paper describes a new approach to adaptive control of uncertain nonlinear systems. A fuzzy logic controller is used to combine both direct and indirect methods. Based on the fuzzy neural networks, the plant unknown nonlinear functions are estimated, and then combined to form the indirect control law. In parallel, another fuzzy neural network approximates the direct adaptive control. According to the modelling error and its derivatives, the fuzzy logic controller modulates between direct and indirect adaptive controllers. The global stability of the overall system is shown by constructing a Lyapunov function. The simulation results show that within this scheme, the control objectives can be achieved with a fast convergence and optimal control for different dynamic regimes.

The development of hybrid and electric vehicles in recent years has increased the use of batteries. However The failure of one cell of the battery involves the non-availability of the embedded battery. To resolve this problem, one can use modular battery associated with specific converter. This method allows the system functioning in the case of cells failure. In this article, a strategy to control a five-level diode inverter associated with a storage system (four batteries modules) is presented. The equation formulation for this control method is based on the combination of different conversion functions. This control makes it possible to determine the modulation for each voltage reference vector of this strategy and its position in the voltage modulation plane in an easy way because this spatial vector modulation does not use the park transform. In order to ensure the state of charge (SOC) balancing between the four battery modules we use the different redundant vectors with closed-loop control. Simulation results demonstrate the good performance of the proposed system.

The techniques and theories used for identification and control of uncertain dynamic systems based on the universal approximators are diverse, but can mainly be divided into two categories, some are basic universal approximators as fuzzy systems (FS), neural networks (NN) and wavelet neural networks (WNN). And other hybrid approximators which are combinations between two basic approximators as fuzzy neural wavelet networks (FWNN) and petri fuzzy systems (PFS). These approximators can be used to estimate the dynamic of system in an aim to construct a controller, as we can use it directly to estimate the control law using specific learning algorithms. In this paper, a hybrid structure between type 2 fuzzy logic and petri networks (PT2FNN) is proposed as a new approximator to alleviate the problem of uncertainties with an optimal cost. By incorporating Petri layers, the number of rules is optimized. Moreover, the time consumed is reduced using a new inference type 2 method. The parameter update algorithms are derived based on the gradient method. This hybrid structure is tested for the identification and the control of uncertain dynamic plants. The simulation results showed that the proposed structure performs better compared with other approximators.

This paper deals with the body-rate stabilization of quadrotors. The main idea is the design of a robust intelligent self-tuning PID controller for the quadrotor system. The key to successfully control a quadrotor with a PID controller is modeling the quadrotor dynamics accurately, then calculate the PID parameters based on the quadrotor identified model. However, the quadrotor is an uncertain underactuated system. Finding an accurate quadrotor model is a difficult task. Therefore, unlike the classical PID controller that depends on the system model, we propose an intelligent PID controller that employs an adaptive Wavelet Neural Network (WNN) to online estimate the optimal proportional, integral and derivative parameters. The adaptation laws of the PID parameters are derived using Lyapunov's stability method to guarantee the stability of the closed-loop system. The proposed intelligent controller does not require prior knowledge of the quadrotor dynamics. The proposed controller has a simple architecture that generates the body-rate commands to stabilize the quadrotor. The simulation results of the proposed intelligent self-tuning PID controller compared to the classical PID controller in stabilization of the quadrotor system prove the efficiency of the proposed scheme.

In this work, we consider the application of an Intelligent Petri Type 2 Fuzzy Direct Adaptive Control for a class of single input single output nonlinear systems. Within this scheme, the Petri Type 2 Fuzzy Neural Networks (PT2FNN) are employed to approximate an unknown ideal controller, that can achieve control objectives in the presence of external disturbance with high accuracy, minimum cost and global stability. The adjusted parameters of PT2FNN are updated online with a stable adaptation mechanism designed to minimize the tracking error. Stability of the proposed control scheme is shown based on Lyapunov theory. The simulation results showed that the developed controller which is based on Petri Type 2 Fuzzy Neural Networks (PT2FNN) performs successfully. Compared with type 1 and type 2 fuzzy adaptive controllers, the suggested Approximator can be used to design an efficient robust direct adaptive controller.

This paper presents a new systematic controller design approach for disturbed nonlinear time-varying systems subject to input and state constraints in discrete-time case. The Takagi–Sugeno (TS) formalism and fuzzy Lyapunov framework are used to handle the time varying parameters and control input saturation. Moreover, the proposed control method is based on L2 criterion which results in two different control design procedures. Both non-parallel distributed compensation (non-PDC) static state feedback control law and dynamics output feedback controller are proposed. To ensures the closed-loop system stability with respect to the given saturation constraints on the control input different optimization problem are also formulated in terms of linear matrix inequality conditions which can be solved efficiently with available solvers. The proposed techniques are illustrated through numerical examples.

Received: 4 April 2019 Accepted: 17 September 2019 This paper proposes a novel dual three-phase Space Vector Modulation (SVM) for sixphase multilevel inverter to control a Six Phase Induction Machine (SPIM). The main idea is to control the six-phase multilevel inverter as two three-phase (1, 3 and 5 phases for the first one and 2, 4 and 6 phases for the second one) multilevel inverters separately by SVM of the N-level three-phase Separate DC Source (SDCS) inverter. This enables a great the simplification of the control algorithm for six phase multilevel (N level) inverter drive. In Six-Phase SVM, N6 vectors are used so if two level N=2, 3 level or 4 level, implies 64 vectors, 729 vectors, 15625 vectors are used respectively. However, in a proposed dual three-phase SVM, we use N3 vectors so if 2 level, 3 level or 4 level implies 8 vectors, 27 vectors, 125 vectors are used respectively to control the six-phase multilevel inverters as two three-phase multilevel inverter with the same three-phase multilevel SVM. Whereas the first three-phase inverter is composed by 1, 3, and 5 phases and the second three-phase is composed by 2, 4 and 6 phases. This allows to have a new modulation technique for six-phase multi-level inverter and a great simplification of the classical six-phase SVM control algorithm. The simulation results of the Indirect Field Oriented Control (IFOC) of six-phase induction machine drive fed by stacked multilevel inverters are given to highlight the performance of the proposed control structure.

The problem of constrained fault tolerant control (CFTC) with multiobjective requirement is investigated for Takagi-Sugeno systems subject to actuator faults. An integrated FTC strategy is adopted by combining a descriptor approach with the proportional integral observer (PIO) to guarantee the closed-loop system stability of the faulty system. Meanwhile, it can ensure the given saturation constraints on control input and the trajectory tracking performance. Based on Lyapunov theory, the optimization problem is formulated in terms of linear matrix inequalities (LMI). Motorcycle simulation results show the effectiveness of the proposed CFTC approach

In this paper, a disturbance observer based robust adaptive fuzzy tracking control algorithm is developed. The problem of trajectory tracking and wind disturbance rejection for a quadrotor unmanned aerial vehicle are investigated. An adaptive fuzzy controller is used to provide good tracking performances for the quadrotor vehicle. While, a state and disturbance observer is employed in order to estimate the immeasurable states and the unknown wind disturbances. The stability analysis of the global controller/observer system is proved using Lyapunov theory. It is shown that all signals in the closed-loop system are uniformly ultimately bounded (UUB). The proposed design can guarantees the desired tracking performances and external wind disturbance rejection. Simulation studies are presented to highlight the efficiency of the proposed control scheme.

In this paper, a comparative study of wavelet functions performances in intelligent direct adaptive control is developed. The main idea is to investigate and compare the tracking accuracy of different wavelet activation functions in intelligent control of uncertain nonlinear systems. The most commonly used wavelet families such as Mexican hat, Morlet and Gaussian wavelet are subject of this study. Lyapunov's direct method is used to prove the stability and to derive the Wavelet Neural Networks (WNNs) parameters adaptation laws. The simulation results are presented to classify the wavelet functions under study.