Abstract: Ziegler-Nichols tuned PI and PID controllers are usually found to provide poor performances for high-order and nonlinear systems. In this study, an improved auto-tuning scheme is presented for Ziegler-Nichols tuned PI controllers (ZNPICs). With a view to improving the transient response, the proportional and integral gains of the proposed controller are continuously modified based on the current process trend. The proposed controller is tested for a number of high-order linear and nonlinear dead-time processes under both set-point change and load disturbance. It exhibits significantly improved performance compared to ZNPIC, and Refined Ziegler-Nichols tuned PI controller (RZNPIC). Robustness of the proposed scheme is established by varying the controller parameters as well as the dead-time of the process under control.

Abstract: A mixed H 2 / H ∞ output-feedback control design methodology is presented in this paper for second-order neutral linear systems with time-varying state and input delays. Delay-dependent sufficient conditions for the design of a desired control are given in terms of linear matrix inequalities (LMIs). A controller, which guarantees asymptotic stability and a mixed H 2 / H ∞ performance for the closed-loop system of the second-order neutral linear system, is then developed directly instead of coupling the model to a first-order neutral system. A Lyapunov–Krasovskii method underlies the LMI-based mixed H 2 / H ∞ output-feedback control design using some free weighting matrices. The simulation results illustrate the effectiveness of the proposed methodology.

Abstract: Linear control of a boiler-turbine unit is discussed in this paper. Based on the nonlinear model of the unit, this paper analyzes the nonlinearity of the unit, and selects the appropriate operating points so that the linear controller can achieve wide-range performance. Simulation and experimental results at the No. 4 Unit at the Dalate Power Plant show that the linear controller can achieve the desired performance under a specific range of load variations.

Abstract: This paper presents an approach to fuzzy rule base design using tabu search algorithm (TSA) for nonlinear system modeling. TSA is used to evolve the structure and the parameter of fuzzy rule base. The use of the TSA, in conjunction with a systematic neighbourhood structure for the determination of fuzzy rule base parameters, leads to a significant improvement in the performance of the model. To demonstrate the effectiveness of the presented method, several numerical examples given in the literature are examined. The results obtained by means of the identified fuzzy rule bases are compared with those belonging to other modeling approaches in the literature. The simulation results indicate that the method based on the use of a TSA performs an important and very effective modeling procedure in fuzzy rule base design in the modeling of the nonlinear or complex systems.

Abstract: This paper presents a practical network platform to design and implement a networked-based cascade control system linking a Smar Foundation Fieldbus (FF) controller (DFI-302) and a Siemens programmable logic controller (PLC-S7-315-2DP) through Industrial Ethernet to a laboratory pilot plant. In the presented network configuration, the Smar OPC tag browser and Siemens WinCC OPC Channel provide the communicating interface between the two controllers. The paper investigates the performance of a PID controller implemented in two different possible configurations of FF function block (FB) and networked control system (NCS) via a remote Siemens PLC. In the FB control system implementation, the desired set-point is provided by the Siemens Human-Machine Interface (HMI) software (i.e, WinCC) via an Ethernet Modbus link. While, in the NCS implementation, the cascade loop is realized in remote Siemens PLC station and the final element set-point is sent to the Smar FF station via Ethernet bus. A new fuzzy PID control strategy is then proposed to improve the control performances of the networked-based control systems due to an induced transmission delay degradation effect. The proposed strategy utilizes an innovative idea based on sectionalizing the error signal of the step response into three different functional zones. The supporting philosophy behind these three functional zones is to decompose the desired control objectives in terms of rising time, settling time and steady-state error measures maintained by an appropriate PID-type controller in each zone. Then, fuzzy membership factors are defined to configure the control signal on the basis of the fuzzy weighted PID outputs of all three zones. The obtained results illustrate the effectiveness of the proposed fuzzy PID control scheme in improving the performances of the implemented NCS for different transportation delays.

Abstract: Adaptive fuzzy control is proposed for a class of affine nonlinear systems in strict-feedback form with unknown nonlinearities. The unknown nonlinearities include two types of nonlinear functions: one satisfies the "triangularity condition" and can be directly approximated by fuzzy logic system, while the other is assumed to be partially known and consists of parametric uncertainties. Takagi-Sugeno type fuzzy approximators are used to approximate unknown system nonlinearities and the design procedure is a combination of adaptive backstepping and generalized small gain design techniques. It is proved that the proposed adaptive control scheme can guarantee the uniformly ultimately bounded (UBB) stability of the closed-loop systems. Simulation studies are shown to illustrate the effectiveness of the proposed approach.

Abstract: A robust sliding mode control that follows a self-tuning law for nonlinear systems possessing uncertain parameters is proposed. The adjustable control gain and a bipolar sigmoid function are on-line tuned to force the tracking error to approach zero. Control system stability is ensured using the Lyapunov method. Both simulation and experimental application of a planetary gear type inverted pendulum control system verify the effectiveness of the developed approach.

Abstract: Design of a nonlinear control system for a Variable Air Volume Air Conditioning (VAVAC) plant through feedback linearization is presented in this article. VAVAC systems attempt to reduce building energy consumption while maintaining the primary role of air conditioning. The temperature of the space is maintained at a constant level by establishing a balance between the cooling load generated in the space and the air supply delivered to meet the load. The dynamic model of a VAVAC plant is derived and formulated as a MIMO bilinear system. Feedback linearization is applied for decoupling and linearization of the nonlinear model. Simulation results for a laboratory scale plant are presented to demonstrate the potential of keeping comfort and maintaining energy optimal performance by this methodology. Results obtained with a conventional PI controller and a feedback linearizing controller are compared and the superiority of the proposed approach is clearly established.

Abstract: In this paper, a novel fault detection and identification (FDI) scheme for a class of nonlinear systems is presented. First of all, an augment system is constructed by making the unknown system faults as an extended system state. Then based on the ESO theory, a novel fault diagnosis filter is constructed to diagnose the nonlinear system faults. An extension to a class of nonlinear uncertain systems is then made. An outstanding feature of this scheme is that it can simultaneously detect and identify the shape and magnitude of the system faults in real time without training the network compared with the neural network-based FDI schemes. Finally, simulation examples are given to illustrate the feasibility and effectiveness of the proposed approach.

Abstract: A modified form of Smith predictor is designed for controlling unstable second order plus time delay (USOPTD) processes, with/without a zero. USOPTD process transfer functions arise in modeling of many chemical and biological systems, such as isothermal continuous stirred tank reactors (CSTR) carrying out autocatalytic reactions, crystallizers and non-isothermal CSTRs. The modified Smith predictor scheme requires the design of three controllers. Synthesis method is used for the design of the three controllers, and analytical formulas are given for these controllers. The method has two tuning parameters, and guidelines are provided for selecting the tuning parameters. Robust disturbance rejection performances are achieved. Different examples are considered, and simulation studies are given, to show the effectiveness of the proposed method. A significant improvement is obtained, when compared with recently reported methods.

Abstract: This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi-Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.

Abstract: This paper deals with the design of adaptive fuzzy sliding-mode controllers for the T-S fuzzy model based on the Lyapunov function. It is shown that the Lyapunov function can be used to establish fuzzy sliding surfaces by solving a set of linear matrix inequalities (LMIs). The design of the fuzzy sliding surfaces and the adaptive fuzzy sliding-mode controllers is proposed. The adaptive mechanism is also used to deal with unknown parameter perturbations and external disturbances. Two examples illustrate the feasibility of the proposed methods.

Abstract: In this paper, we present a new algorithm for designing a fixed, low-order controller with time response specifications for a linear time invariant (LTI), single input single output (SISO) plant. For a two-parameter feedback configuration, the problem of finding a fixed or low-order controller to meet the desired time response specification is reduced to the least square estimation (LSE) in the sense of partial model matching (PMM), which minimizes a quadratic cost function. The closed-loop stability condition imposed on the controller parameters is formulated by the polynomial matrix inequality (PMI) constraint associated with the cost function. When the cascade feedback structure is considered, the zeros of the controller may be a substantial obstacle when designing a controller that has a good time response. This problem can also be formulated using polynomial constraints. Consequently, it is shown that the total problem here can be formulated as an optimization problem with a quadratic objective function and several polynomial constraints in the controller parameter space. We show that the SeDuMi with YALMIP interface [Lofberg J. YALMIP: A toolbox for modeling and optimization in MATLAB, in: Proceedings of the IEEE symposium on computer aided control systems design 2004. p. 284–9. http://control.ee.ethz.ch/~joloef/yalmip.php ] can be used for solving this problem. Finally, several illustrative examples are given.

Abstract: This paper presents the implementing multiple fan beam projection technique using optical fibre sensors for a tomography system. From the dynamic experiment of solid/gas flow using plastic beads in a gravity flow rig, the designed optical fibre sensors are reliable in measuring the mass flow rate below 40% of flow. Another important matter that has been discussed is the image processing rate or IPR. Generally, the applied image reconstruction algorithms, the construction of the sensor and also the designed software are considered to be reliable and suitable to perform real-time image reconstruction and mass flow rate measurements.

Abstract: Most chemical processes are inherently nonlinear. However, because of their simplicity, linear control algorithms have been used for the control of nonlinear processes. In this study, the use of the dynamic matrix control algorithm and a simplified model predictive control algorithm for control of a bench-scale pH neutralization process is investigated. The nonlinearity is handled by dividing the operating region into sub-regions and by switching the controller model as the process moves from one sub-region to another. A simple modification for model predictive control algorithms is presented to handle the switching. The simulation and experimental results show that the modification can provide a significant improvement in the control of nonlinear processes.

Abstract: Correlation between a dithering signal and the prediction error has been used for detecting model mismatch in univariate model based control systems. This paper extends that approach to MIMO control systems. A closed-loop cross-correlation method is presented to detect which specific input-output pairings of a model-based controller are mismatched. This method may be used in screening the complete set of models and in selecting candidate models for re-identification. The method first finds the rows and columns of the transfer function matrix that contain mismatch, and then the candidates are found by the intersection of the said rows and columns. Placing the system under partial control, whereby one or more of the manipulated variables are held constant, can be used to further reduce the set of candidate models.

Abstract: The goal of this work is to provide a method for monitoring performance degradation in counter-flow double-pipe heat exchangers. The overall heat transfer coefficient is estimated by an adaptive observer and monitored in order to infer when the heat exchanger needs preventive or corrective maintenance. A simplified mathematical model is used to synthesize the adaptive observer and a more complex model is used for simulation. The reliability of the proposed method was demonstrated via numerical simulations and laboratory experiments with a bench-scale pilot plant.

Abstract: This research proposes the fuzzy theory for the control of weld lines in plastic injection molding. The weld line occurs as a result of geometrical changes in molded parts in the injection molding process. The weld line is one of the defects present in plastic injection-molded parts; the line affects the quality of parts as well as the strength of the products. In the present study, fuzzy theory was applied in the design of injection molding. First, expert experiences were transformed into I F ∼ T H E N ∼ rules to establish the knowledge base for developing fuzzy inference rules. The rules were then used to adjust the molding parameters, which in turn were applied to control the weld line position in the injection molding process. The results indicate that fuzzy theory exhibited favorable applicability in the control of the weld line as well as decreased the simulation time, thereby accelerating the design process of injection molding.

Abstract: In this paper, parameter identification of two-dimensional continuous-time systems via two-dimensional modulating functions is proposed. In the proposed method, trigonometric functions and sine-cosine wavelets are used as modulating functions. By this, a partial differential equation on the finite-time intervals is converted into an algebraic equation linear in parameters. The parameters of the system can then be estimated using the least square algorithms. The underlying computations utilize a two-dimensional fast Fourier transform algorithm, without the need for estimating the unknown initial or boundary conditions, at the beginning of each finite-time interval. Numerical simulations are presented to show the effectiveness of the proposed algorithm.

Abstract: A two-stage robust control scheme improving the performance of an Activated Sludge Process is proposed. In the first stage, asymptotic command following the substrate concentration with simultaneous attenuation of the fluctuations of the dissolved oxygen concentration is assured. The first stage is a pure dynamic controller. The second stage is a PID controller. Good performance of the proposed control scheme on the corresponding nonlinear ASP model is illustrated through extensive simulation experiments. The contribution of the paper can be summarized to the derivation of the following two results: An accurate to a wide range of inputs and disturbances, linearized generic model of the ASP and, most important, a linear robust controller that controls accurately the effluent substrate concentration without using measurements of it.

Abstract: This paper presents a Fuzzy Neural Network (FNN) control system for a traveling-wave ultrasonic motor (TWUSM) driven by a dual mode modulation non-resonant driving circuit. First, the motor configuration and the proposed driving circuit of a TWUSM are introduced. To drive a TWUSM effectively, a novel driving circuit, that simultaneously employs both the driving frequency and phase modulation control scheme, is proposed to provide two-phase balance voltage for a TWUSM. Since the dynamic characteristics and motor parameters of the TWUSM are highly nonlinear and time-varying, a FNN control system is therefore investigated to achieve high-precision speed control. The proposed FNN control system incorporates neuro-fuzzy control and the driving frequency and phase modulation to solve the problem of nonlinearities and variations. The proposed control system is digitally implemented by a low-cost digital signal processor based microcontroller, hence reducing the system hardware size and cost. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a conventional proportional-integral control system.

Abstract: The modified Smith predictor (MSP) for integrating process with dead time (IPDT) has at least one pole at the origin of s-plane. The integral effect leads to large overshoot and slow settling time when the control signal exceeds the saturation limits of the system actuator. The windup problem in MSP is presented and studied. Based on the two degrees of freedom (2DOF) control schemes presented by Normey-Rico and Camacho, an anti-windup controller (AWC) is proposed. By simulation, significant performance improvement demonstrates the effectiveness of the proposed scheme.

Abstract: In this work the modeling and basic control design process of a rotary flexible spindle hovered by Active Magnetic Bearings (AMB) whose good capabilities for machine-tool industry extensively treated in the literature is presented. The modeling takes into account the three main behavioral characteristics of such magnetically-levitated rotor: the rigid dynamics, the flexible dynamics and the rotating unbalanced motion. Besides, the gyroscopic coupling is also studied proving that in this case, its effects are not significant and can be neglected. Using this model, a stabilizing controller based on symmetry properties is successfully designed for the system and a complete experimental analysis of its performance is carried out. Also, the predictions of the model are compared with the actual measured experimental results on a laboratory set-up based on the MBC500 Rotor Dynamics. Afterwards, a brief study about some nonlinear behavior observed in the system and its effect over the system stability at the critical speed is included.

Abstract: High-quality protective garments are essential in many occupational assignments, e.g., in the petrochemical industry and for firefighting. The problem addressed in this paper is how to objectively asses the performance of protective garments designed to resist fire. We present a system based on a flame mannequin equipped with an array of temperature sensors that provide information about the temperature on the mannequin's surface during exposure to fire. Particular attention is devoted to reconstructing the heat flux reaching the mannequin's surface on the basis of the available temperature records (the inverse heat-conduction problem). The estimated heat flux is used in a skin-simulation model to predict the level of injury that would occur in human exposed to the same heat. The paper includes a novel computational procedure based on a detailed model of the temperature sensor and a numerical solution of the accompanying heat equation. The effectiveness of the solution is demonstrated with experimental data.

Abstract: The design of two single-input single-output (SISO) controllers for induction motors based on adaptive passivity is presented in this paper The two controllers work together with a field orientation block Because of the adaptive nature of the proposed controllers, the knowledge of the set motor-load parameters is not needed and robustness under variations of such parameters is guaranteed Simple proportional controllers for the torque, rotor flux and stator current control loops are used, due to the control simplification given by the use of feedback passive equivalence A new principle called the "Torque-Flux Control Principle" is also stated in this article, which considerably simplifies the controller design, diminishing the control efforts and avoiding also rotor flux estimation

Abstract: This article has been retracted at the request of the author and/or the Editor-in-Chief. Please see Elsevier Policy on Article Withdrawal ( http://www.elsevier.com/locate/withdrawalpolicy ). Reason: This article has been retracted at the request of the Editor in Chief as the author has plagiarized parts of previously published papers in introductory and discussion sections: Jang J-SR, ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans Syst Man Cybern 1993;23(3):665-85 Parrott Daniel, Li Xiaodong, Locating and tracking multiple dynamic optima by a particle swarm model using speciation, IEEE Trans Evol Comput 2006;10(4):440-58. Juang, C.-F., Liou, Y.-C. On the hybrid of genetic algorithm and particle swarm optimization for evolving recurrent neural network 2004 IEEE International Conference on Neural Networks - Conference Proceedings 3, pp. 2285-2289 Cai, X., Zhang, N., Venayagamoorthy, G.K., Wunsch II, D.C., Time series prediction with recurrent neural networks using a hybrid PSO-EA algorithm, 2004 IEEE International Conference on Neural Networks - Conference Proceedings 2, pp. 1647-1652 Lu, C.-F., Juang, C.-F., Control of flexible AC transmission system by evolutionary fuzzy controller, 2004 Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics 3, pp. 2292-2296 Jang, Jyh-Shing R., Self-learning fuzzy controllers based on temporal back propagation, 1992, IEEE Transactions on Neural Networks 3 (5), pp. 714-723 Ray, W.H., New approaches to the dynamics of nonlinear systems with implications for process and control design, 1981, Chemical Process Control 2 119 One of the conditions of submission of a paper for publication is that authors declare explicitly that all portions of their work are original and have not appeared in a publication elsewhere. Re-use of any text, figures, or data should be appropriately cited. Since this article quoted sections of prior work without appropriate acknowledgment, it violates scientific publishing system protocol. The scientific community takes a very strong view on this matter and we apologize to readers of the journal that this was not detected during the submission process.

Abstract: This paper presents a controller for use in speed control of an internal combustion engine for series-hybrid electric vehicle applications. Particular reference is made to the stability of the rectified DC link voltage under load disturbance. In the system under consideration, the primary power source is a four-cylinder normally aspirated gasoline internal combustion engine, which is mechanically coupled to a three-phase permanent magnet AC generator. The generated AC voltage is subsequently rectified to supply a lead-acid battery, and permanent magnet traction motors via three-phase full bridge power electronic inverters. Two complementary performance objectives exist. Firstly to maintain the internal combustion engine at its optimal operating point, and secondly to supply a stable 42 V supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the internal combustion engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. An electronically operated throttle allows closed loop engine velocity control. System time delays and nonlinearities render closed loop control design extremely problematic. A model-based controller is designed and shown to be effective in controlling the DC link voltage, resulting in the well-conditioned operation of the hybrid vehicle.

Abstract: This paper presents the design and implementation of an embedded soft sensor, i.e., a generic and autonomous hardware module, which can be applied to many complex plants, wherein a certain variable cannot be directly measured. It is implemented based on a fuzzy identification algorithm called "Limited Rules", employed to model continuous nonlinear processes. The fuzzy model has a Takagi-Sugeno-Kang structure and the premise parameters are defined based on the Fuzzy C-Means (FCM) clustering algorithm. The firmware contains the soft sensor and it runs online, estimating the target variable from other available variables. Tests have been performed using a simulated pH neutralization plant. The results of the embedded soft sensor have been considered satisfactory. A complete embedded inferential control system is also presented, including a soft sensor and a PID controller.

Abstract: In this paper, a mathematical model and control theoretical framework for designing AQM controllers in networks supporting TCP Vegas is introduced. We have emphasized on a modified TCP Vegas algorithm that can respond to congestion signals through explicit congestion notification (ECN). The overall nonlinear delayed differential equations of the dynamics model of closed loop system have been derived based on TCP Vegas model. The model is then linearized to derive a transfer function representation between the packet marking probability and the bottleneck router queue length as the input and output of the modified TCP Vegas/AQM system. The model properties have been then examined especially for the case of single bottleneck homogeneous network which is closely investigated. Finally an AQM controller based on Coefficient Diagram Method (CDM) has been designed for the system and its performance has been compared with some other AQM controllers. CDM is a new indirect pole placement method that considers the speed, stability and robustness of the closed loop system in terms of time domain specifications. In order for synthesizing the simulation scenarios, our campus router traffic has been studied experimentally for a sample period of one hour and the corresponding parameters has been extracted. The simulation results are representative of good performance of developed TCP Vegas/AQM structure for different simulated scenarios.