This paper discusses the empirical modeling using system identification technique with a focus on an interacting series process. The study is carried out experimentally using a gaseous pilot plant as the process, in which the dynamic of such a plant exhibits the typical dynamic of an interacting series process. Three practical approaches are investigated and their performances are evaluated. The models developed are also examined in real-time implementation of a linear model predictive control. The selected model is able to reproduce the main dynamic characteristics of the plant in open-loop and produces zero steady-state errors in closed-loop control system. Several issues concerning the identification process and the construction of a MIMO state space model for a series interacting process are deliberated.
The emergence of wireless technologies such as WirelessHART and ISA100 Wireless for deployment at industrial process plants has urged the need for research and development in wireless control. This is in view of the fact that the recent application is mainly in monitoring domain due to lack of confidence in control aspect. WirelessHART has an edge over its counterpart as it is based on the successful Wired HART protocol with over 30 million devices as of 2009. Recent works on control have primarily focused on maintaining the traditional PID control structure which is proven not adequate for the wireless environment. In contrast, Internal Model Control (IMC), a promising technique for delay compensation, disturbance rejection and setpoint tracking has not been investigated in the context of WirelessHART. Therefore, this paper discusses the control design using IMC approach with a focus on wireless processes. The simulation and experimental results using real-time WirelessHART hardware-in-the-loop simulator (WH-HILS) indicate that the proposed approach is more robust to delay variation of the network than the PID.
Direct Torque Control (DTC) scheme introduces a robust and simple control of electrical drive. However, its shortcomings such as broad torque ripple and variable switching frequency have offered several improvements like Space Vector Modulation (SVM) strategy, multilevel inverter (MLI) topology, etc. The conventional DTC which is fed by the two-level inverter has limited voltage vector, results in some difficulties to optimize the operation, especially at low operating speed. In contrast to MLI, the abundant of voltage vector has provided various amplitudes and angles that can overcome the problem of conventional DTC. Thus, this paper introduces the selected optimal voltage vector obtained from five-level Cascaded H-Bridge (CHB) inverter that employs in DTC hysteresis-based to achieve better optimization that similar to the DTC-SVM. Initially, the research work begins with an investigation on the performance comparison between a DTC hysteresis-based between two-level inverter (conventional method) and a five-level CHB inverter (proposed method). Here, a DC generator acted as a load is employed to control the operating speed instead of the speed controller (speed controller is negligible). Hence, the DTC method is optimized by minimizing the torque ripple as well as retaining the torque control capability at constant torque region on several operating speed. The selected optimal vector from the look-up table DTC of five-level CHB inverter must be dynamically appropriate to any change of torque (increased or decreased torque). For simplicity, this paper will only discuss the experimental results for both topologies of drive system. From the experimental results, it is verified that the torque ripples by the proposed method have achieved 10% and 50% reduction at high and low operating speed respectively. It is found that the DTC hysteresis-based result simpler control method than DTC-SVM while maintaining similar output performance.
Fuzzy Logic Speed Controller (FLSC) has been widely used for motor drive due to its robustness and its non-reliance to real plant parameters. However, it is computationally expensive to be implemented in real-time and prone to the fuzzy rules' selection error which results in the failure of the drive's system. This paper proposes an improved simplified rules method for Fuzzy Logic Speed Controller (FLSC) based on the significant crisp output calculations to address these issues. A systematic procedure for the fuzzy rules reduction process is first described. Then, a comprehensive evaluation of the activated crisp output data is presented to determine the fuzzy dominant rules. Based on the proposed method, the number of rules was significantly reduced by 72%. The simplified FLSC rule is tested on the Induction Motor (IM) drives system in which the real-time implementation was carried out in the dSPACE DS1103 controller environment. The simulation and experimental results based on the proposed FLSC have proved the workability of the simplified rules without degrading the motor performance.
Automatic Voltage Regulator (AVR) is fabricated to sustain the voltage level of a synchronous generator spontaneously. Several control strategies have been introduced into the AVR system with the aim of gaining a better dynamic response. One of the most universally utilized controllers is the Proportional-Integral-Derivative (PID) controller. Despite the PID controller having a relatively high dynamic response, there are still further possibilities to improve in order to obtain more appropriate responses. This paper designed a sigmoid-based PID (SPID) controller for the AVR system in order to allow for an accelerated settling to rated voltage, as well as increasing the control accuracy. In addition, the parameters of the proposed SPID controller are obtained using an enhanced self-tuning heuristic optimization method called Nonlinear Sine Cosine Algorithm (NSCA), for achieving a better dynamic response, particularly with regards to the steady-state errors and overshoot of the system. A time-response specifications index is used to validate the proposed SPID controller. The obtained simulation results revealed that the proposed method was not only highly effective but also greatly improved the AVR system transient response in comparison to those with the modern heuristic optimization based PID controllers.
A hybrid technique is proposed in this manuscript for the optimal design of an induction motor (IM) drive for the dynamic load profiles during torque and flux control. The proposed hybrid method combines a Ladder-Spherical-Evolution-Search-Algorithm (LSE) and a recalling-enhanced recurrent-neural network (RERNN), which is called an LSE-RERNN technique. The major objective of the proposed method is to minimize IM losses while maintaining control over speed and torque. The proposed method effectively tunes the gain parameter of the PI controller for flux and torque regulation. The LSE methodgenerates a set of gain parameters optimally predicted by RERNN. The method reduces losses without prior knowledge of load profiles, achieving energy savings for steady-state optimum flux. The performance of the proposed technique is done in the MATLAB and is compared with different existing techniques. The value of the proposed method for the mean is 0.328, the standard deviation (SD) is 0.00334, and the median is 0.4173. The loss of the proposed method is much less than 0.3 W while compared to different existing approaches. Moreover, the computation time of the proposed approach is lesser than the existing techniques.
Open-loop unstable systems with time-delays are often encountered in process industry, which are often more difficult to control than stable processes. In this paper, the stabilization by PID controller of second-order unstable processes, which can be represented as second-order deadtime with an unstable pole (SODUP) and second-order deadtime with two unstable poles (SODTUP), is performed via the necessary and sufficient criteria of Routh-Hurwitz stability analysis. The stability analysis provides improved understanding on the existence of a stabilizing range of each PID parameter. Three simple PID tuning algorithms are proposed to provide desired closed-loop performance-robustness within the stable regions of controller parameters obtained via the stability analysis. The proposed PID controllers show improved performance over those derived via some existing methods.
This paper investigates the issue of fault-tolerant and anti-disturbance attenuation for a two-dimensional modified repetitive control system (2D MRCS) which is described by switched fuzzy systems with multiple disturbances. In particular, the multiple disturbances contain an exogenous disturbance and standard Wiener noise. Specifically, a generalized extended state observer (GESO) is incorporated with the 2D MRCS to estimate both fault and exogenous multiple disturbances so that the disturbances and faults can be attenuated in the control input. Further, the improved 2D MRCS relaxes the stability condition and provides an enhanced tracking performance. Based on the Lyapunov function approach, pole placement technique and average dwell time approach, the stability criteria for the considered system is developed in terms of linear matrix inequality (LMI). Then an algorithm for designing a GESO-based 2D MRC design is developed based on the obtained LMIs. Further, the results developed are validated in the simulation section through three numerical examples.
This paper examines two approaches in tuning fractional order proportional-integral-differential (FOPID) control named as neuro-based FOPID (NNFOPID) and particle swarm-based FOPID (PSOFOPID) for pitch control of a Twin Rotor Aerodynamic System (TRAS). For the neuro-based FOPID control, the innovations are the modification of output equation in the artificial neural network and the implementation of the Rectified Linear Unit (ReLU) activation function. The advantages of the proposed approach are a lighter network and the ability to tune more practical controller parameters without a deep knowledge of the system to achieve a satisfying pitch tracking response. As for the particle swarm-based FOPID control, the application of PSO with spreading factor algorithm is extended for tuning the FOPID controller gains and the innovation here is a new procedure in setting the initial search range. The important advantages of this proposed swarm-based algorithm are the avoidance of being trapped in local optima and reduction of the search area respectively. The performances of the proposed controllers are proven by extensive simulations and experimental verifications based on five standard criteria: square-wave characteristics, reference to disturbance ratio, evaluation time, energy consumption of the control signal and tracking performance. The performances of the proposed controllers are compared against an optimised PID control in three system conditions, namely Case I) without coupling effect and wind disturbance, Case II) with coupling effect only and Case III) with wind disturbance only. Together, this study finds that NNFOPID control offers an accurate system positioning by a 34% reduction in steady-state error with the lowest energy consumption and minimum evaluation time in Case II. In terms of the tracking performance and robustness for Case II, the superiority of PSOFOPID control is confirmed by a 27% reduction in the tracking error and the lowest oscillation value. The experimental results also validate the robustness and energy consumption of both controllers in Case III. It is envisaged that the proposed control designs can be very useful in tuning FOPID controller gains for high performance, low energy, and robust aerodynamics systems.
The motivation behind this paper is to seek alternative techniques to achieve a near optimal controller for non-linear systems without solving the analytical problem. In classical optimal control systems, the system states and optimization co-state parameters generate a two-point boundary value problem (TPBVP) using Pontryagin's minimum principle (PMP). The paper contributes a new fuzzy time-optimal controller to the existing fuzzy controllers which has two regular inputs and one bang-bang output. The proposed controller closely approximates the output of the classical time-optimal controller. Further, input membership function are tuned on-line to improve the time-optimal output. The new controller exhibits optimal behaviour for second order non-linear systems. The rules are selected to satisfy the stability and optimality conditions of the new fuzzy time-optimal controller. The paper describes a systematic procedure to design the controller and how to achieve the desired result. To benchmark the new controller performance, a sliding mode controller is used for guidance and comparison purpose. Simulation of three non-linear examples shows promising results. The work described here is expected to incite researcher's interest in fuzzy time-optimal controller design.
This paper proposes an improved hierarchical control strategy consists of a primary and a secondary layer for a three-phase 4-wire microgrid under unbalanced and nonlinear load conditions. The primary layer is comprised of a multi-loop control strategy to provide balanced output voltages, a harmonic compensator to reduce the total harmonic distortion (THD), and a droop-based scheme to achieve an accurate power sharing. At the secondary control layer, a reactive power compensator and a frequency restoration loop are designed to improve the accuracy of reactive power sharing and to restore the frequency deviation, respectively. Simulation studies and practical performance are carried out using the DIgSILENT Power Factory software and laboratory testing, to verify the effectiveness of the control strategy in both islanded and grid-connected mode. Zero reactive power sharing error and zero frequency steady-state error have given this control strategy an edge over the conventional control scheme. Furthermore, the proposed scheme presented outstanding voltage control performance, such as fast transient response and low voltage THD. The superiority of the proposed control strategy over the conventional filter-based control scheme is confirmed by the 2 line cycles decrease in the transient response. Additionally, the voltage THDs in islanded mode are reduced from above 5.1% to lower than 2.7% with the proposed control strategy under nonlinear load conditions. The current THD is also reduced from above 21% to lower than 2.4% in the connection point of the microgrid with the offered control scheme in the grid-connected mode.
This paper proposes a generalized robust synchronization method for different dimensional fractional order dynamical systems with mismatched fractional derivatives in the presence of function uncertainty and external disturbance by a designing sliding mode controller. Based on the proposed theory of generalized robust synchronization criterion, a novel audio cryptosystem is proposed for sending or sharing voice messages secretly via insecure channel. Numerical examples are given to verify the potency of the proposed theories.
Numerous applications of artificial olfaction resulting from research in many branches of sciences have caused considerable interest in the enhancement of these systems. In this paper, we offer an architecture which is suitable for critical applications, such as medical diagnosis, where reliability and precision are deemed important. The proposed architecture is able to tolerate failures in the sensors of the array. In this study, the discriminating ability of the proposed architecture in detecting complex odors, as well as the performance of the proposed architecture in encountering sensor failure, were investigated and compared with the generic architecture. The results demonstrated that by applying the proposed architecture in the artificial olfactory system, the performance of system in the healthy mode was identical to the classic structure. However, in the faulty situation, the proposed architecture implied high identification ability of odor samples, while the generic architecture showed very poor performance in the same situation. Based on the results, it was possible to achieve high odor identification through the developed artificial olfactory system using the proposed architecture.
The performance of a chemical process plant can gradually degrade due to deterioration of the process equipment and unpermitted deviation of the characteristic variables of the system. Hence, advanced supervision is required for early detection, isolation and correction of abnormal conditions. This work presents the use of an adaptive neuro-fuzzy inference system (ANFIS) for online fault diagnosis of a gas-phase polypropylene production process with emphasis on fast and accurate diagnosis, multiple fault identification and adaptability. The most influential inputs are selected from the raw measured data sets and fed to multiple ANFIS classifiers to identify faults occurring in the process, eliminating the requirement of a detailed process model. Simulation results illustrated that the proposed method effectively diagnosed different fault types and severities, and that it has a better performance compared to a conventional multivariate statistical approach based on principal component analysis (PCA). The proposed method is shown to be simple to apply, robust to measurement noise and able to rapidly discriminate between multiple faults occurring simultaneously. This method is applicable for plant-wide monitoring and can serve as an early warning system to identify process upsets that could threaten the process operation ahead of time.
This paper presents a fast terminal sliding mode based control design strategy for a class of uncertain underactuated nonlinear systems. Strategically, this development encompasses those electro-mechanical underactuated systems which can be transformed into the so-called regular form. The novelty of the proposed technique lies in the hierarchical development of a fast terminal sliding attractor design for the considered class. Having established sliding mode along the designed manifold, the close loop dynamics become finite time stable which, consequently, result in high precision. In addition, the adverse effects of the chattering phenomenon are reduced via strong reachability condition and the robustness of the system against uncertainties is confirmed theoretically. A simulation as well as experimental study of an inverted pendulum is presented to demonstrate the applicability of the proposed technique.
Optical tomography provides a means for the determination of the spatial distribution of materials with different optical density in a volume by non-intrusive means. This paper presents results of concentration measurements of gas bubbles in a water column using an optical tomography system. A hydraulic flow rig is used to generate vertical air-water two-phase flows with controllable bubble flow rate. Two approaches are investigated. The first aims to obtain an average gas concentration at the measurement section, the second aims to obtain a gas distribution profile by using tomographic imaging. A hybrid back-projection algorithm is used to calculate concentration profiles from measured sensor values to provide a tomographic image of the measurement cross-section. The algorithm combines the characteristic of an optical sensor as a hard field sensor and the linear back projection algorithm.
This paper describes a system using lensed optical fiber sensors that are arranged in the form of two orthogonal projections. The sensors are placed around a process vessel for upstream and downstream measurements. The purpose of the system is for on-line monitoring of particles and droplets being conveyed by a fluid. The lenses were constructed using a custom heating fixture. The fixture enables the lenses to be constructed with similar radii resulting in identical characteristics with minimum differences in transmitted intensity and emission angle. By collimating radiation from two halogen bulbs, radiation can be obtained by the sensors with radiation intensity related to the nature of the media. Each sensor interrogates a finite section of the measurement section. Each sensor provides a view. Parallel sensors provide a projection. Signal processing is carried out on the measured data in the time and frequency domains to investigate the latent information present in the flow signals.
Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg-Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.
This paper investigates a novel offset-free control scheme based on a multiple model predictive controller (MMPC) and an adaptive integral action controller for nonlinear processes. Firstly, the multiple model description captures the essence of the nonlinear process, while keeping the MPC optimization linear. Multiple models also enable the controller to deal with the uncertainty associated with changing setpoint. Then, a min-max approach is utilized to counter the effect of parametric uncertainty between the linear models and the nonlinear process. Finally, to deal with other uncertainties, such as input and output disturbances, an adaptive integral action controller is run in parallel to the MMPC. Thus creating a novel offset-free approach for nonlinear systems that is more easily tuned than observer-based MPC. Simulation results for a pH-controller, which acts as an example of a nonlinear process, are presented to demonstrate the usefulness of the technique compared to using an observer-based MPC.
This paper proposes an improved method of integral backstepping for real time control of a laboratory helicopter with variable speed rotors known as the Two-Rotor Aero-dynamic System (TRAS). The coupled system is decomposed into the horizontal subsystem (HS) and the vertical subsystem (VS) and traditional backstepping, augmented with direct integral action is designed for each subsystem. The transient response to both constant and time varying references is then simultaneously improved by modifying an already proposed method called dual boundary conditional integration. A switching technique is also employed to enhance the tracking response of the undamped HS for its bi-directional motor which exhibits jerking effects. Experimental results show that the proposed approach yields improved transient and tracking performance when compared to previously proposed methods exploiting conditional integration earlier proposed for improving the transient response of controlled nonlinear systems with integral action. The results also show the robustness of the proposed method in the presence of the coupling effects and additional external disturbance applied to the system in the form of a wind gust.