Showing papers in "Isa Transactions in 2013"
TL;DR: A new PID controller for resistant differential control against load disturbance is introduced that can be used for load frequency control (LFC) application and a comparison between this controller and two other prevalent PI controllers, optimized by GA and Neural Networks, has been done which represents advantages of this controller over others.
Abstract: A new PID controller for resistant differential control against load disturbance is introduced that can be used for load frequency control (LFC) application. Parameters of the controller have been specified by using imperialist competitive algorithm (ICA). Load disturbance, which is due to continuous and rapid changes of small loads, is always a problem for load frequency control of power systems. This paper introduces a new method to overcome this problem that is based on filtering technique which eliminates the effect of this kind of disturbance. The object is frequency regulation in each area of the power system and decreasing of power transfer between control areas, so the parameters of the proposed controller have been specified in a wide range of load changes by means of ICA to achieve the best dynamic response of frequency. To evaluate the effectiveness of the proposed controller, a three-area power system is simulated in MATLAB/SIMULINK. Each area has different generation units, so utilizes controllers with different parameters. Finally a comparison between the proposed controller and two other prevalent PI controllers, optimized by GA and Neural Networks, has been done which represents advantages of this controller over others.
287 citations
TL;DR: Under the proposed terminal sliding mode tracking control techniques, the finite time convergence of all closed-loop signals are guaranteed via Lyapunov analysis.
Abstract: In this paper, the terminal sliding mode tracking control is proposed for the uncertain single-input and single-output (SISO) nonlinear system with unknown external disturbance. For the unmeasured disturbance of nonlinear systems, terminal sliding mode disturbance observer is presented. The developed disturbance observer can guarantee the disturbance approximation error to converge to zero in the finite time. Based on the output of designed disturbance observer, the terminal sliding mode tracking control is presented for uncertain SISO nonlinear systems. Subsequently, terminal sliding mode tracking control is developed using disturbance observer technique for the uncertain SISO nonlinear system with control singularity and unknown non-symmetric input saturation. The effects of the control singularity and unknown input saturation are combined with the external disturbance which is approximated using the disturbance observer. Under the proposed terminal sliding mode tracking control techniques, the finite time convergence of all closed-loop signals are guaranteed via Lyapunov analysis. Numerical simulation results are given to illustrate the effectiveness of the proposed terminal sliding mode tracking control.
248 citations
TL;DR: The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems and demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters.
Abstract: In this paper a new meta-heuristic search method, called Cat Swarm Optimization (CSO) algorithm is applied to determine the best optimal impulse response coefficients of FIR low pass, high pass, band pass and band stop filters, trying to meet the respective ideal frequency response characteristics CSO is generated by observing the behaviour of cats and composed of two sub-models In CSO, one can decide how many cats are used in the iteration Every cat has its' own position composed of M dimensions, velocities for each dimension, a fitness value which represents the accommodation of the cat to the fitness function, and a flag to identify whether the cat is in seeking mode or tracing mode The final solution would be the best position of one of the cats CSO keeps the best solution until it reaches the end of the iteration The results of the proposed CSO based approach have been compared to those of other well-known optimization methods such as Real Coded Genetic Algorithm (RGA), standard Particle Swarm Optimization (PSO) and Differential Evolution (DE) The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems The performances of the CSO based designed FIR filters have proven to be superior as compared to those obtained by RGA, conventional PSO and DE The simulation results also demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters
160 citations
TL;DR: A new control structure with a tuning method to design a PID load frequency controller for power systems and the proposed method improves the load disturbance rejection performance significantly even in the presence of the uncertainties in plant parameters.
Abstract: A new control structure with a tuning method to design a PID load frequency controller for power systems is presented. Initially, the controller is designed for single area power system, then it is extended to multi-area case. The controller parameters are obtained by expanding controller transfer function using Laurent series. Relay based identification technique is adopted to estimate power system dynamics. Robustness studies on stability and performance are provided, with respect to uncertainties in the plant parameters. The proposed scheme ensures that overall system remains asymptotically stable for all bounded uncertainties and for system oscillations. Simulation results show the feasibility of the approach and the proposed method improves the load disturbance rejection performance significantly even in the presence of the uncertainties in plant parameters.
152 citations
TL;DR: Simulation results show that the proposed integral backstepping slide mode controller is able to reject both matched and mismatched uncertainties with a chattering free control law, while utilizing less control effort than the sliding mode controller.
Abstract: In this paper an integral backstepping sliding mode controller is proposed for controlling underactuated systems. A feedback control law is designed based on backstepping algorithm and a sliding surface is introduced in the final stage of the algorithm. The backstepping algorithm makes the controller immune to matched and mismatched uncertainties and the sliding mode control provides robustness. The proposed controller ensures asymptotic stability. The effectiveness of the proposed controller is compared against a coupled sliding mode controller for swing-up and stabilization of the Cart–Pendulum System. Simulation results show that the proposed integral backstepping sliding mode controller is able to reject both matched and mismatched uncertainties with a chattering free control law, while utilizing less control effort than the sliding mode controller.
129 citations
TL;DR: A new Lyapunov-Krasovskii functional is constructed, and based on the derived condition, the reliable H∞ control problem is solved, and the system trajectory stays within a prescribed bound during a specified time interval.
Abstract: This paper is concerned with the problem of finite-time H ∞ control for a class of Markovian jump systems with mode-dependent time-varying delays via new Lyapunov functionals. In order to reduce conservatism, a new Lyapunov–Krasovskii functional is constructed. Based on the derived condition, the reliable H ∞ control problem is solved, and the system trajectory stays within a prescribed bound during a specified time interval. Finally, numerical examples are given to demonstrate the proposed approach is more effective than some existing ones.
124 citations
TL;DR: Experiments show that the Teager domain features outperform those based on the temporal or AM signal and feature it with statistical and energy-based measures.
Abstract: Condition monitoring of rotating machines is important in the prevention of failures. As most machine malfunctions are related to bearing failures, several bearing diagnosis techniques have been developed. Some of them feature the bearing vibration signal with statistical measures and others extract the bearing fault characteristic frequency from the AM component of the vibration signal. In this paper, we propose to transform the vibration signal to the Teager–Kaiser domain and feature it with statistical and energy-based measures. A bearing database with normal and faulty bearings is used. The diagnosis is performed with two classifiers: a neural network classifier and a LS-SVM classifier. Experiments show that the Teager domain features outperform those based on the temporal or AM signal.
105 citations
TL;DR: Comparisons were made among the four FOMRAC designs, a fractional order PID, a classical PID, and four Integer Order Model Reference Adaptive Controllers, showing that the FomRAC can improve the controlled system behavior and its robustness with respect to model uncertainties.
Abstract: This paper presents the application of ad irect Fractional Order Model Reference Adaptive Controller (FOMRAC) to an Automatic Voltage Regulator (AVR). A direct FOMRAC is a direct Model Reference Adaptive Control (MRAC), whose controller parameters are adjusted using fractional order differential equations. Four realizations of the FOMRAC were designed in this work, each one considering different orders for the plant model. The design procedure consisted of determining the optimal values of the fractional order and the adaptive gains for each adaptive law, using Genetic algorithm optimization. Comparisons were made among the four FOMRAC designs, a fractional order PID (FOPID), a classical PID, and four Integer Order Model Reference Adaptive Controllers (IOMRAC), showing that the FOMRAC can improve the controlled system behavior and its robustness with respect to model uncertainties. Finally, some performance indices are presented here for the controlled schemes, in order to show the advantages and disadvantages of the FOMRAC.
103 citations
TL;DR: Multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal trade-offs between the set point tracking and control signal, and the set points tracking and load disturbance performance for each of the controller structure to handle the three different types of processes.
Abstract: Fuzzy logic based PID controllers have been studied in this paper, considering several combinations of hybrid controllers by grouping the proportional, integral and derivative actions with fuzzy inferencing in different forms. Fractional order (FO) rate of error signal and FO integral of control signal have been used in the design of a family of decomposed hybrid FO fuzzy PID controllers. The input and output scaling factors (SF) along with the integro-differential operators are tuned with real coded genetic algorithm (GA) to produce optimum closed loop performance by simultaneous consideration of the control loop error index and the control signal. Three different classes of fractional order oscillatory processes with various levels of relative dominance between time constant and time delay have been used to test the comparative merits of the proposed family of hybrid fractional order fuzzy PID controllers. Performance comparison of the different FO fuzzy PID controller structures has been done in terms of optimal set-point tracking, load disturbance rejection and minimal variation of manipulated variable or smaller actuator requirement etc. In addition, multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal trade-offs between the set point tracking and control signal, and the set point tracking and load disturbance performance for each of the controller structure to handle the three different types of processes.
101 citations
TL;DR: A simple integer-order control scheme for fractional-order system based on active disturbance rejection method is proposed and the ADRC stability of rational-order model is analyzed.
Abstract: Fractional-order proportional–integral (PI) and proportional–integral–derivative (PID) controllers are the most commonly used controllers in fractional-order systems. However, this paper proposes a simple integer-order control scheme for fractional-order system based on active disturbance rejection method. By treating the fractional-order dynamics as a common disturbance and actively rejecting it, active disturbance rejection control (ADRC) can achieve the desired response. External disturbance, sensor noise, and parameter disturbance are also estimated using extended state observer. The ADRC stability of rational-order model is analyzed. Simulation results on three typical fractional-order systems are provided to demonstrate the effectiveness of the proposed method.
101 citations
TL;DR: The comparison of results shows the efficient and robust prediction capability of D-ADALINE sensor and hence DADIC proves to be the best controller.
Abstract: The present work is aimed at the design of Levenberg–Marquardt (LM) and adaptive linear network (ADALINE) based soft sensors and their application in inferential control of a multicomponent distillation process. Further the ADALINE sensor is trained online using past measurements, to adapt the changes in the inputs and is termed as dynamic ADALINE (D-ADALINE) sensor. The soft sensors are then used in the control loop to obtain LM based inferential controller (LMIC), ADALINE based inferential controller (ADIC) and D-ADALINE based inferential controller (DADIC) for the process. The performance of dynamic controller is also analyzed for different inputs and sampling intervals. The comparison of results shows the efficient and robust prediction capability of D-ADALINE sensor and hence DADIC proves to be the best controller.
TL;DR: A novel robust nonlinear control design under the constraints of assigned velocity and actuator torque is investigated for attitude stabilization of a rigid spacecraft by explicitly taking into account the constraints on individual angular velocity components as well as external disturbances.
Abstract: A novel robust nonlinear control design under the constraints of assigned velocity and actuator torque is investigated for attitude stabilization of a rigid spacecraft. More specifically, a nonlinear feedback control is firstly developed by explicitly taking into account the constraints on individual angular velocity components as well as external disturbances. Considering further the actuator misalignments and magnitude deviation, a modified robust least-squares based control allocator is employed to deal with the problem of distributing the previously designed three-axis moments over the available actuators, in which the focus of this control allocation is to find the optimal control vector of actuators by minimizing the worst-case residual error using programming algorithms. The attitude control performance using the controller structure is evaluated through a numerical example.
TL;DR: This paper proposes two higher order spectra techniques, namely the power spectrum and the slices of bi-spectrum used for the analysis of induction machine stator current leading to the detection of electrical failures within the rotor cage.
Abstract: Detection and identification of induction machine faults through the stator current signal using higher order spectra analysis is presented. This technique is known as motor current signature analysis (MCSA). This paper proposes two higher order spectra techniques, namely the power spectrum and the slices of bi-spectrum used for the analysis of induction machine stator current leading to the detection of electrical failures within the rotor cage. The method has been tested by using both healthy and broken rotor bars cases for an 18.5 kW-220 V/380 V-50 Hz-2 pair of poles induction motor under different load conditions. Experimental signals have been analyzed highlighting that bi-spectrum results show their superiority in the accurate detection of rotor broken bars. Even when the induction machine is rotating at a low level of shaft load (no-load condition), the rotor fault detection is efficient. We will also demonstrate through the analysis and experimental verification, that our proposed proposed-method has better detection performance in terms of receiver operation characteristics (ROC) curves and precision-recall graph.
TL;DR: A chattering free adaptive sliding mode controller (SMC) is proposed for stabilizing a class of multi-input multi-output (MIMO) systems affected by both matched and mismatched types of uncertainties.
Abstract: In this paper, a chattering free adaptive sliding mode controller (SMC) is proposed for stabilizing a class of multi-input multi-output (MIMO) systems affected by both matched and mismatched types of uncertainties. The proposed controller uses a proportional plus integral sliding surface whose gain is adaptively tuned to prevent overestimation. A vertical take-off and landing (VTOL) aircraft system is simulated to demonstrate the effectiveness of the proposed control scheme.
TL;DR: A new design method of fractional-order proportional-integral controllers is proposed based on fractional calculus and Bode's ideal transfer function for a first-order-plus-dead-time process model, which consistently showed improved performance over other similar controllers and established integer PI controllers.
Abstract: A new design method of fractional-order proportional-integral controllers is proposed based on fractional calculus and Bode's ideal transfer function for a first-order-plus-dead-time process model. It can be extended to be applied to various dynamic models. Tuning rules were analytically derived to cope with both set-point tracking and disturbance rejection problems. Simulations of a broad range of processes are reported, with each simulated controller being tuned to have a similar degree of robustness in terms of resonant peak to other reported controllers. The proposed controller consistently showed improved performance over other similar controllers and established integer PI controllers.
TL;DR: A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs) by applying sector bound approach.
Abstract: This paper studies the approach of model predictive control (MPC) for the non-linear systems under networked environment where both data quantization and packet loss may occur. The non-linear controlled plant in the networked control system (NCS) is represented by a Tagaki-Sugeno (T-S) model. The sensed data and control signal are quantized in both links and described as sector bound uncertainties by applying sector bound approach. Then, the quantized data are transmitted in the communication networks and may suffer from the effect of packet losses, which are modeled as Bernoulli process. A fuzzy predictive controller which guarantees the stability of the closed-loop system is obtained by solving a set of linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.
TL;DR: A stable adaptive PI control strategy based on the improved just-in-time learning (IJITL) technique is proposed for permanent magnet synchronous motor (PMSM) drive which is highly real-time compared to traditional JITL.
Abstract: In this paper, a stable adaptive PI control strategy based on the improved just-in-time learning (IJITL) technique is proposed for permanent magnet synchronous motor (PMSM) drive. Firstly, the traditional JITL technique is improved. The new IJITL technique has less computational burden and is more suitable for online identification of the PMSM drive system which is highly real-time compared to traditional JITL. In this way, the PMSM drive system is identified by IJITL technique, which provides information to an adaptive PI controller. Secondly, the adaptive PI controller is designed in discrete time domain which is composed of a PI controller and a supervisory controller. The PI controller is capable of automatically online tuning the control gains based on the gradient descent method and the supervisory controller is developed to eliminate the effect of the approximation error introduced by the PI controller upon the system stability in the Lyapunov sense. Finally, experimental results on the PMSM drive system show accurate identification and favorable tracking performance.
TL;DR: The proposed discrete time sliding mode controller is applicable to HOPDT processes with oscillatory and integrating behavior, open loop instability or non-minimum phase characteristics and works satisfactory under the effect of parametric uncertainty.
Abstract: In this paper, a discrete time sliding mode controller (DSMC) is proposed for higher order plus delay time (HOPDT) processes. A sliding mode surface is selected as a function of system states and error and the tuning parameters of sliding mode controller are determined using dominant pole placement strategy. The condition for the existence of stable sliding mode is obtained by using Lyapunov function. The proposed method is applicable to HOPDT processes with oscillatory and integrating behavior, open loop instability or non-minimum phase characteristics and works satisfactory under the effect of parametric uncertainty. The method does not require reduced order model and provides simple way to design the controllers. The simulation and experimentation results show that the proposed method ensures desired tracking dynamics.
TL;DR: A comparison of the estimation capabilities of the three models has been done by simulation of the developed models and it was observed that radial basis network model produced better estimation capabilities than the back propagation and regression network models.
Abstract: The online estimation of process outputs mostly related to quality, as opposed to their belated measurement by means of hardware measuring devices and laboratory analysis, represents the most valuable feature of soft sensors. As of now there have been very few attempts for soft sensing of cement clinker quality which is mostly done by offline laboratory analysis resulting at times in low quality clinker. In the present work three different neural network based soft sensors have been developed for online estimation of cement clinker properties. Different input and output data for a rotary cement kiln were collected from a cement plant producing 10,000 tons of clinker per day. The raw data were pre-processed to remove the outliers and the resulting missing data were imputed. The processed data were then used to develop a back propagation neural network model, a radial basis network model and a regression network model to estimate the clinker quality online. A comparison of the estimation capabilities of the three models has been done by simulation of the developed models. It was observed that radial basis network model produced better estimation capabilities than the back propagation and regression network models.
TL;DR: An adaptive control strategy for a class of 5 DOF upper-limb exoskeleton robot with a special safety consideration is studied, designed to be robust to the model uncertainties and to improve the safety and fault-tolerance in the presence of unknown large parameter variances or actuator faults.
Abstract: This paper studies an adaptive control strategy for a class of 5 DOF upper-limb exoskeleton robot with a special safety consideration. The safety requirement plays a critical role in the clinical treatment when assisting patients with shoulder, elbow and wrist joint movements. With the objective of assuring the tracking performance of the pre-specified operations, the proposed adaptive controller is firstly designed to be robust to the model uncertainties. To further improve the safety and fault-tolerance in the presence of unknown large parameter variances or even actuator faults, the adaptive controller is on-line updated according to the information provided by an adaptive observer without additional sensors. An output tracking performance is well achieved with a tunable error bound. The experimental example also verifies the effectiveness of the proposed control scheme.
TL;DR: An adaptive sliding mode controller is designed to maintain the sliding mode, where the gain of the nonlinear unit vector term is updated automatically to compensate for the effects of actuator faults, quantization errors, exogenous disturbances and parameter uncertainties without the need for a fault detection and isolation (FDI) mechanism.
Abstract: This paper is concerned with the problem of robust fault-tolerant compensation control problem for uncertain linear systems subject to both state and input signal quantization. By incorporating novel matrix full-rank factorization technique with sliding surface design successfully, the total failure of certain actuators can be coped with, under a special actuator redundancy assumption. In order to compensate for quantization errors, an adjustment range of quantization sensitivity for a dynamic uniform quantizer is given through the flexible choices of design parameters. Comparing with the existing results, the derived inequality condition leads to the fault tolerance ability stronger and much wider scope of applicability. With a static adjustment policy of quantization sensitivity, an adaptive sliding mode controller is then designed to maintain the sliding mode, where the gain of the nonlinear unit vector term is updated automatically to compensate for the effects of actuator faults, quantization errors, exogenous disturbances and parameter uncertainties without the need for a fault detection and isolation (FDI) mechanism. Finally, the effectiveness of the proposed design method is illustrated via a model of a rocket fairing structural-acoustic.
TL;DR: The obtained simulation results show an adequate dynamic of the conversion system using the proposed method compared to the classical approaches, and a comparison was made with classical DTC and field oriented control method.
Abstract: This paper presents a modulated hysteresis direct torque control (MHDTC) applied to an induction generator (IG) used in wind energy conversion systems (WECs) connected to the electrical grid through a back-to-back converter. The principle of this strategy consists in superposing to the torque reference a triangular signal, as in the PWM strategy, with the desired switching frequency. This new modulated reference is compared to the estimated torque by using a hysteresis controller as in the classical direct torque control (DTC). The aim of this new approach is to lead to a constant frequency and low THD in grid current with a unit power factor and a minimum voltage variation despite the wind variation. To highlight the effectiveness of the proposed method, a comparison was made with classical DTC and field oriented control method (FOC). The obtained simulation results, with a variable wind profile, show an adequate dynamic of the conversion system using the proposed method compared to the classical approaches.
TL;DR: A novel control law model is proposed to take the network-induced delay, random packet dropout and packet-dropout compensation into consideration simultaneously and the cone complementarity linearization procedure is exploited to solve the nonconvex feasible problem.
Abstract: In this paper, the H∞ static output feedback control problem is investigated for a class of nonlinear networked control systems with imperfect measurements. A novel control law model is proposed to take the network-induced delay, random packet dropout and packet-dropout compensation into consideration simultaneously. The network status is assumed to vary in a Markovian fashion satisfying a certain transition probability matrix. By constructing a network-status-dependent Lyapunov functional, a sufficient condition for the existence of the H∞ output feedback controller is formulated in the form of nonconvex matrix inequality, and the cone complementarity linearization (CCL) procedure is exploited to solve the nonconvex feasible problem. Finally, two illustrative examples are provided to show the effectiveness and applicability of the proposed method.
TL;DR: The main objective of the paper is to developed a methodology, named as vague Lambda-Tau, for reliability analysis of repairable systems, which improves the shortcomings of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation.
Abstract: The main objective of the paper is to developed a methodology, named as vague Lambda-Tau, for reliability analysis of repairable systems. Petri net tool is applied to represent the asynchronous and concurrent processing of the system instead of fault tree analysis. To enhance the relevance of the reliability study, vague set theory is used for representing the failure rate and repair times instead of classical(crisp) or fuzzy set theory because vague sets are characterized by a truth membership function and false membership functions (non-membership functions) so that sum of both values is less than 1. The proposed methodology involves qualitative modeling using PN and quantitative analysis using Lambda-Tau method of solution with the basic events represented by intuitionistic fuzzy numbers of triangular membership functions. Sensitivity analysis has also been performed and the effects on system MTBF are addressed. The methodology improves the shortcomings of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation. The washing unit of a paper mill situated in a northern part of India, producing approximately 200 ton of paper per day, has been considered to demonstrate the proposed approach. The results may be helpful for the plant personnel for analyzing the systems' behavior and to improve their performance by adopting suitable maintenance strategies.
TL;DR: This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems.
Abstract: This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach.
TL;DR: The proposed PD+d control architecture is modified for trilateral teleoperation with internet communication between telemanipulators that caused such communication complications as packet loss, data duplication and swapping.
Abstract: A novel trilateral control architecture for the Dual-master/Single-slave teleoperation is proposed in this paper. This framework has been used in surgical training and rehabilitation applications. In this structure, the slave motion has been controlled by weighted summation of signals transmitted by the operator referring to task control authority through the dominance factors. The nonlinear dynamics for telemanipulators are considered which were considered as disregarded issues in previous studies of this field. Bounded variable time-delay has been considered which affects the transmitted signals in the communication channels. Two types of controllers have been offered and an appropriate stability analysis for each controller has been demonstrated. The first controller includes Proportional with dissipative gains (P+d). The second one contains Proportional and Derivative with dissipative gains (PD+d). In both cases, the stability of the trilateral control framework is preserved by choosing appropriate controller's gains. It is shown that these controllers attempt to coordinate the positions of telemanipulators in the free motion condition. The stability of the Dual-master/Single-slave teleoperation has been proved by an appropriate Lyapunov like function and the stability conditions have been studied. In addition the proposed PD+d control architecture is modified for trilateral teleoperation with internet communication between telemanipulators that caused such communication complications as packet loss, data duplication and swapping. A number of experiments have been conducted with various levels of dominance factor to validate the effectiveness of the new control architecture.
TL;DR: An IP-self-tuning controller tuned by a fuzzy adjustor, is proposed to improve induction machine speed control and different tests used to compare the performances of the proposed controller to the two others in terms of computation time, tracking performances and disturbances rejection.
Abstract: An IP-self-tuning controller tuned by a fuzzy adjustor, is proposed to improve induction machine speed control. The interest of such controller is the possibility to adjust only one gain, instead of two gains for the case of the PI-self-tuning controllers commonly used in the literature. This paper presents simulation and experimental results. These latter were obtained by practical implementation on a DSPace 1104 board of three different speed controllers (the classical IP, the fuzzy-like-PI and the IP-self-tuning), for a 1.5KW induction machine. The paper presents different tests used to compare the performances of the proposed controller to the two others in terms of computation time, tracking performances and disturbances rejection.
TL;DR: Simulation results demonstrate the efficacy of the proposed method by showing satisfactory nominal and robust performances and the robustness analysis is carried out using Kharitonov's theorem.
Abstract: Unlike self-regulating processes, cascade control strategies for control of integrating processes with time delay are limited. A novel series cascade control structure to enhance the closed loop performance is proposed for integrating and time delay processes. The proposed controller structure has only two controllers and a setpoint filter. The inner loop controller is designed based on IMC approach and the primary setpoint filter is based on optimal performance index. The primary load disturbance rejection controller, a PID controller in series with a lead/lag compensator, is designed on the basis of the desired closed-loop complementary sensitivity function. The robustness analysis is carried out using Kharitonov's theorem. Simulation results demonstrate the efficacy of the proposed method by showing satisfactory nominal and robust performances.
TL;DR: Simulation results show superiority and capability of the proposed controller to improve the steady state performance and transient response specifications by using less numbers of fuzzy rules and on-line adaptive parameters in comparison to other methods.
Abstract: This paper proposes novel adaptive fuzzy wavelet neural sliding mode controller (AFWN-SMC) for a class of uncertain nonlinear systems. The main contribution of this paper is to design smooth sliding mode control (SMC) for a class of high-order nonlinear systems while the structure of the system is unknown and no prior knowledge about uncertainty is available. The proposed scheme composed of an Adaptive Fuzzy Wavelet Neural Controller (AFWNC) to construct equivalent control term and an Adaptive Proportional-Integral (A-PI) controller for implementing switching term to provide smooth control input. Asymptotical stability of the closed loop system is guaranteed, using the Lyapunov direct method. To show the efficiency of the proposed scheme, some numerical examples are provided. To validate the results obtained by proposed approach, some other methods are adopted from the literature and applied for comparison. Simulation results show superiority and capability of the proposed controller to improve the steady state performance and transient response specifications by using less numbers of fuzzy rules and on-line adaptive parameters in comparison to other methods. Furthermore, control effort has considerably decreased and chattering phenomenon has been completely removed.
TL;DR: This paper presents a technique for computing the membership functions of the intuitionistic fuzzy set (IFS) by utilizing imprecise, uncertain and vague data by formulating a nonlinear optimization problem through ordinary arithmetic operations instead of fuzzy operations.
Abstract: The main objective of this paper is to present a technique for computing the membership functions of the intuitionistic fuzzy set (IFS) by utilizing imprecise, uncertain and vague data. In literature so far, membership functions of IFS are computed via using fuzzy arithmetic operations within collected data and hence contain a wide range of uncertainties. Thus it is necessary for optimizing these spread by formulating a nonlinear optimization problem through ordinary arithmetic operations instead of fuzzy operations. Particle swarm optimization (PSO) has been used for constructing their membership functions. Sensitivity as well as performance analysis has also been conducted for finding the critical component of the system. Finally the computed results are compared with existing results. The suggested framework has been illustrated with the help of a case.