Search

The paper discusses a self tuning control algorithm for a multivariable plant applied to a grammage cross profile control of a paper machine. The plant model represents the steady-state couplings as well as the dynamics behaviour of the system. The control algorithm handles special situations including the case where the numper of measurments is not equal to the numper of actuators and the case where special actuators are locked. The whole identification algorithm with a specical filter and an estimation of the required measurment accuary for a succesful identification of the steady-state couplings will be discussed. The paper shows how to proof the stability of the control system including the cases of locked actuators and identification errors. Finally, the whole conrol system is verified by in house simulations and at a real plant. The obtained control results are discussed.

Proportional Integral Derivative (PID) controller tuning is an area of interest for researchers in many areas of science and engineering. This paper presents a new algorithm for PID controller tuning based on a combination of bacteria foraging and particle swarm optimization. BFO algorithm has recently emerged as a very powerful technique for real parameter optimization. To overcome delay in an optimization, combine the features of BFOA and PSO for tuning the PID controller. This new algorithm is proposed to combine both the algorithms to get better optimization values. The real time prototype model of paper machine is designed and controlled by using PIC microcontroller embedded with the programming in C language.

This paper defines the efficiency similarity of different motor driving devices. Based on the efficiency function characteristics which can be approximately considered a concave, non-negative function through the origin, we present a kind of optimal control method for a felt driven by three different motors in paper machine. It gives the minimum total power consumption. The optimal control method is to keep the same torque rate for each motor driving device. This method is independent of the linearity or nonlinearity of the system and has no need for the system’s state equation or transfer function. Since the shapes of efficiency functions in nature are similar, the conclusion can also be applied to the energy optimization of other general devices.

This work is centered on IMC (Internal Model Control) control system design and its robustness analysis for controlling the basis weight of the paper produced by the paper milll. The headbox has a significant role in a paper mill. It maintains the pressure inside the box and discharges stock at an optimum required speed. In this paper, the robustness of the IMC based controllers will be investigated for the two parameters, setpoint tracking and disturbance rejection. The MATLAB software is used for carrying out all simulations done in this paper.

The paper mill is one of the major industries of India. The various processes that are present in paper mill are not simple to model and control because of presence of nonlinearities and the dead time. The present work is focused on a prime section of a paper mill that is the headbox. The regulation of its consistency is a major concern. In this paper, first of all, the FOPDT model of the head box consistency dynamics is approximated as a polynomial model using the Pade approximation and hence control system is designed for it. The Simulated Annealing (SA) technique is used for the performance optimization of this control system. MATLAB software has been used to obtain all simulation results.

Maximum Power Point Tracking (MPPT) is essential for the Wind Energy Conversion System (WECS) mainly for the small scale wind turbine. In order to captured the maximum possible power from wind turbine, it is very important to operate the WECS at MPPT and tracking the maximum power point in the power output signal. This paper presents a critical analysis of the algorithm generation to reach maximum power point based on power efficiency and simplicity.

This paper studies automated tuning of cross-directional model predictive control for industrial paper-making processes under user-specified model parameter uncertainties. Automated parameter tuning algorithms are developed to reduce the variability of the actuator and measurement profiles in the spatial domain and to achieve satisfactory performance in terms of worst case settling times and worst case control signal overshoots in the temporal domain for given parametric uncertainties. Due to decoupling properties of the spatial and temporal frequency components, the controller design and parameter tuning can be realized separately. For the spatial design and parameter tuning in the presence of parametric uncertainties, the undesirable high-frequency components in the actuator profile are suppressed via an appropriate design of the weighting matrix $S_{b}$ using the real-valued Fourier matrix approach. For the temporal design, a temporal filter is adopted to smooth the reference trajectory, where the parameter in the temporal filter is carefully tuned to achieve a tradeoff between the worst case settling time and the worst case control signal overshoot. Finally, the effectiveness of the proposed tuning algorithms is verified using a system model extracted from the pulp and paper industry.

This paper describes a flow control of the dye in the paper mill with the quasi-Z-source indirect matrix converter (QZSIMC) fed induction motor drive. More than a decade the voltage-source inverter (VSI) and current-source inverter (CSI) have been used to control the speed of the induction motor, which in turns controls the flow of dye. Recently, the matrix converter (MC) has been an excellent competitor for the VSI or CSI for its compactness. The voltage transfer ratio of the VSI, CSI, and MC has been limited to 0.866. Thus, the efficiency of these converters is less. To improve the voltage transfer ratio the quasi-Z-source network (QZSN) is to be used between voltage source and indirect MC (IMC). The modification in the shoot-through duty ratio of the QZSN varies the voltage transfer ratio greater than 0.866. Different voltage transfer ratios are needed for different voltage sag conditions. For a variation of the duty ratio of the QZSN, the fuzzy logic controller has been presented. To control the IMC vector control with space vector modulation has been presented. This paper proposes the implementation of QZSIMC adjustable speed drive for the flow control of dye in paper mill during different voltage sag conditions. A 4-kW prototype has been built and the effectiveness of the proposed system is verified with simulation results and experimental setup. Simulation is done in MATLAB, Simulink platform. Experimental setup is done with the aid of a TMS320F2812 (Texas Instrument) processor. The experimental results validate the maintenance of the speed of an induction motor at the set condition, thus controlling the perfect flow of dye in paper manufacturing technology.

Based on the sequential batch reactor (SBR) simulation model of the wastewater treatment process for a secondary fiber paper mill that we have developed, two control strategies, a neural network (BP-PID controller) and a fuzzy logic system (Fuzzy-PID controller), were separately combined with PID to control the dissolved oxygen (DO) concentration in the SBR process of paper mill. Two cases, without and with the presence of noise, have been studied and evaluated using the criteria considered in BSM1. For the case without noise, both two-control strategies demonstrated better control indices than that of the traditional PID control; for the case with noise, the Fuzzy-PID controller possessed the good control indices at almost the same expense of aeration energy. The results under two cases showed that the Fuzzy-PID controller demonstrated better performance than that of BP-PID controller, in the DO control of SBR process in the paper mill, Fuzzy-PID controller improved the control performance without increasing the aeration energy consumption.

Total Pressure and Stock Level in the headbox of a paper machine control the quality of paper produced and cross directional flow at the slice. Thus, these are the important parameters to be controlled. With the advancement in artificial intelligence, mathematical approaches that demand high degree of precision has been avoided by using the best estimates in the research work. Soft computing technique i.e. fuzzy logic is used in this paper and the simulation results are compared with classical PID controller.

This paper presents the design of a fractional order combined model reference adaptive control scheme. This approach is based on the estimates of the plant unknown parameters and the adjusted controller parameters, which are obtained from adaptive laws using the identification error, the control error and the so called closed loop estimation errors. Fractional order indirect algebraic model reference adaptive control and fractional order indirect dynamic model reference adaptive control schemes are also proposed in this paper, which are used for comparison purposes with the combined approach. Boundedness of all the signals in the control schemes is analytically proved, as well as the convergence to zero of the mean values of the control, identification and closed loop estimation errors. Simulation results are presented to show the effectiveness of the proposed control strategies.

To extend inverted decoupling control method to the non-square multivariable time delay system, this paper presents a block inverted decoupling control approach with internal model structure. In this method, the non-square system is divided into two subsystems, and the decoupling structure is composed of two blocks. Each block in the designed structure is applied to each subsystem. With the proposed method, complex matrix computations are avoided, and the designed controller has low order. The stability of closed-loop system with the proposed method is analyzed in the paper. Numerical simulations are performed to illustrate the effectiveness of this methodology.

The paper proposes a reconfigurable control design of steering and torque vectoring using a variable-geometry suspension system. Torque vectoring control is based on the independent driving of the wheels. Simultaneously, the steering angle is generated by the variable-geometry suspension system by varying the camber angle of the front wheels. The efficiency of wheel tilting and torque vectoring, and their coordinated actuation are in the focus of the paper. In the analysis a reachable set computation method on the polynomial model of the vehicle based on the trajectory reversing method and the Sum-of-Squares (SOS) programming is proposed. Based on the analysis results a reconfigurable control is designed using the Linear Parameter Varying (LPV) method.

The minimum variance controller has been extensively used as a benchmark in the performance assessment of both univariate and multivariate control loops when time delay is the fundamental performance limitation. In this paper, the spatial and temporal performance limitations in the cross-directional (CD) control of paper machines are analyzed. The idea of minimum variance benchmarking is extended to the CD process based on these performance limitations. Based on an industrial CD controller, a user-specified benchmark, which is more practical and less aggressive, is also proposed. In addition, several related performance indices are proposed for the CD process based on both the minimum variance benchmark and the user-specified benchmark. Illustrative examples from a paper machine simulator and industrial data sets are provided to show the effectiveness of the proposed performance indices.

This paper solves the controller tuning problem of machine-directional predictive control for multiple-input–multiple-output (MIMO) paper-making processes represented as superposition of first-order-plus-dead-time (FOPDT) components with uncertain model parameters. A user-friendly multi-variable tuning problem is formulated based on user-specified time domain specifications and then simplified based on the structure of the closed-loop system. Based on the simplified tuning problem and a proposed performance evaluation technique, a fast multi-variable tuning technique is developed by ignoring the constraints of the MPC. In addition, a technique to predict the computation time of the tuning algorithm is proposed. The efficiency of the proposed method is verified through Honeywell real time simulator platform with a MIMO paper-making process obtained from real data from an industrial site.

This note contains comments on the paper Zhou (2015a), with specific reference to: the fact that the result in Theorem 3 of the paper is not new but actually goes back to the early 60s; a few comments on the actual scope and applicability of the method proposed in the paper; some corrections to the literature review provided in the paper.

Wind turbine rotor imbalances may be created by a multitude of causes, e.g. pitch misalignment, blade erosion. In such situations, machines experience undesired vibrations, which cannot be compensated or mitigated by the controllers generally adopted by industry today. This happens because the control action may be collective or both collective and cyclic, but, in order to cope with imbalances, it is necessary to employ a fully individual control which allows a blade to move independently from the others. In this paper we present a new individual pitch control algorithm able to simultaneously target the imbalance and reduce oscillating loads at frequencies multiple of the rotor revolution. The effectiveness of the proposed control is demonstrated in a high-fidelity simulation environment.

The paper deals with the fault-tolerant reconfigurable control design of a four-wheel independently-actuated (4WIA) electric vehicle. The purpose of the proposed method is to ensure the velocity and path tracking of the 4WIA vehicle in the event of a fault or performance degradation in one or even several in-wheel electric motors. The novelty of the presented method is the high-level control reallocation method based on the Linear Parameter Varying (LPV) control framework, by which the torque vectoring and steering of the vehicle are modified in order to deal with the impact of faulty in-wheel motors. The operation of the designed fault-tolerant method is validated in CarSim simulation environment.

In pulp and paper plants, power transformers play a critical role in process continuity. These transformers are subject to internal short circuits, external short circuits, and abnormal operating conditions. The following protection challenges to power transformers will be explored, and methods to improve the protection are provided. First, remanence in a current transformer (CT) may cause misoperation of phase differential protection due to compromised CT performance. Heavy through-faults, sympathetic inrush, and recovery inrush all cause high current. This, combined with high remanent flux, can create a security issue. IEEE CT performance calculations will be used to support the use of dual slope differential characteristics to promote secure differential protection operation when challenged with unequal CT performance. Second, on transformer energizing, the second harmonic current has been traditionally used as a means to prevent phase differential misoperation. Certain transformers may not exhibit high enough the second harmonic, causing a dependability issue if the restraint is set too low. The use of the second and fourth harmonics for inrush detection will be shown to enhance the reliability during energizing inrush situations. Third, overexcitation can occur from abnormal operation of the utility system or the plant's excitation control. Causes of overexcitation will be outlined and the use of volts per hertz protection is explored. With overexcitation occurring from system voltage rise, the phase differential protection has been traditionally blocked using the fifth harmonic restraint. This may cause an undesired nonoperation of the phase differential protection if an internal fault occurs while the transformer is overexcited, causing a delay in tripping and severe damage to the transformer. A technique using adaptive phase differential pickup value will be illustrated to overcome this challenge. Finally, on resistance-grounded power transformers, phase differential protection sensitivity for ground faults near the neutral is decreased. The use of ground differential protection will be explored and the increased sensitivity gained will be demonstrated.

EMG is a medical technique that measures muscle response or electrical activity in response to a nerve’s stimulation of the muscle [10]. In this paper we have classified multiple hand gestures using small number of electrodes. This report aims to review current use of EMG based control system and explain the existing challenges. The stages involved includes data acquisition, feature extraction, classification and controller. Some of the application includes Arm rehabilitation, game controller, wheel chair controllers, etc.

Inertial stabilized platforms are extensively used to keep the light of sight free from the effects of vehicle motion or vibration. In order to reduce the assembly difficulty and cost as well as achieve an accurate pointing in the presence of disturbances, a finite-time output feedback control strategy is proposed. On the basis of the dynamic model with both matched and unmatched uncertainties, two high-order sliding mode observers are designed to estimate the newly defined virtual state and the lumped uncertainty, respectively. Combing with the nonsingular terminal sliding mode technique, an output feedback control based on the estimates and the output is developed. The proposed method can achieve not only stability and robustness against uncertainties but also the finite-time convergence to the desired angular rate without control chattering. Finally, the comparative simulations are performed to show the effectiveness of the proposed method.

The problem of curse-of-dimensionality in finite state and action Markov decision processes is considered using iterative clustering of closed-loop data and repeated discretization of the state space process model. The performance of the control design approach is demonstrated using a multi-input van der Vusse continuous stirred tank reactor control benchmark. It is demonstrated that the finite state description provides a simple implementation of Bayesian state estimation using cell filters, and dynamic programming gives means to conduct optimization of closed-loop performance of a nonlinear stochastic multidimensional chemical plant.

The integration of distributed Energy Resources (DER) is challenging electrical network quality which is the main vital and ubiquitous all over the world. Coordination and energy management of microgrids in grid connected and in islanded mode is one of solutions of distribution power quality with best reliability, and stability to ensure power system resilience. In this review, the coordination and energy management of microgrids broad based on PQ controller and droop control some useful information is given in details and analyzed. Additionally, possible structures, options and control methods of DER units are presented which is followed by the descriptions of system controls and energy management strategies. Eventually, future trends of microgrids are discuss, point out how this concept can be a key to select a best method of coordination and managing microgrids for future researchers in order to tackle some challenges based on power quality.

Although flexible neural networks FNNs have been used more successfully than classical neural networks CNNs in many industrial applications, nothing is rigorously known about their properties. In fact they are not even well known to the systems and control community. In the first part of this paper, existing structures of and results on FNNs are surveyed. In the second part FNNs are examined in a theoretical framework. As a result, theoretical evidence is given for the superiority of FNNs over CNNs and further properties of the former are developed. More precisely, several fundamental properties of feedforward and recurrent FNNs are established. This includes the universal approximation capability, minimality, controllability, observability, and identifiability. In the broad sense, the results of this paper help that general use of FNNs in systems and control theory and applications be based on firm theoretical foundations. Theoretical analysis and synthesis of FNN-based systems thus become possible. The paper is concluded by a collection of topics for future work.

The choice of this investigation is to tune the proportional-integral-derivative (PID) parameters separately for controlling the moisture content in paper industry by using Particle Swarm Optimization (PSO). This paper boon a new algorithm for PID controller tuning based particle swarm optimization. PSO algorithm has recently developed as a very powerful method for real parameter optimization. This new process is proposed to combine both the algorithms to get better optimization values. The proposed algorithm tuned the PID parameters and its performance has been compared with PID algorithm. Compared to PID algorithm technique, dynamic performance requirements such as rise time settling time and peak overshoot optimal values produced by PSO. The plant model represented by the transfer function is obtained by the system identification toolbox.

Different methods for measuring the friction forces are investigated in this paper. We consider the paper-on-paper contact as an example of application. We first underline several drawbacks for the two main standard methods, namely the inclined and horizontal plane methods. In particular, the horizontal plane test method often involves stick-slip oscillations that make the measurement of the friction force impossible. We then propose a method for characterizing these oscillations and removing their influence on the friction force measurement. The comparison of the proposed method to standards suggests that our proposed method delivers measurements that are much more accurate and repeatable. We finally discuss the validity of averaging the friction force measured during the sliding movement.

Load shedding is critical for facilities that have cogeneration capability to prevent collapse of the cogeneration assets. Fast load shedding requires sub-cycle speed to manage thousands of distributed loads within a facility. This paper describes the experience of applying such a fast load shedding scheme across a wide array of relays provided by various vendors and potentially a large quantity. The system described in this paper utilizes a proven fast load shed system and makes it easy to configure larger and more complex load shed schemes. The paper proposes the architecture of a central fast load shed controller interfaced with local feeder protection relays over IEC 61850 GOOSE and additional fast load shed aggregators if total GOOSE is more than 64. One of the challenges with a large-scale load shed scheme deployment is the configuration of devices, and especially the IEC 61850 GOOSE engineering process which may add further complexity. This paper proposes a simple to configure IEC 61850 GOOSE scheme with high-speed performance requirements. Lessons learned from the fast load shed deployment are also discussed.

To increase the efficiency of photovoltaic (PV) systems, maximum power point (MPP) tracking of the solar arrays is needed. Under partially shaded conditions (PSCs), the solar arrays power–current (P–I) characteristic has multiple MPP. This paper presents various methods and approaches of tracking the MPP from a PV generator operating under PSC. Some comparisons, advantages, drawbacks and critical analysis of each method are discussed. It was found that, indirect methods use empirical data or mathematical expressions of numerical approximations to estimate the MPP from the PV generator’s voltage, current and irradiance. Direct methods offer the advantage of obtaining the actual maximum power from the PV generator’s voltage and current. Artificial intelligence methods do not need exact mathematical models. They can perform under parameter variation, load and supply voltage disturbances. Finally, novel methods require less number of iterations to converge, independent to the initial conditions. All t...

In controlling uncertain processes, it is decisive to utilize information provided by measurements in order to estimate parameters and states. Nonlinear Model Predictive Control (NMPC) is a popular method to implement feedback control and deal with uncertainties. Conventional NMPC or nominal control, however, sometimes does not provide enough information for system estimation, leading to unsatisfactory performance. Dual control attempts to strike a balance between the two goals of enhancing system estimation and optimizing the nominal objective function. In this paper, we analyze the performance of these strategies through the interplay between the performance control task and the information gain task in connection with Optimal Experimental Design. Examples illustrate the conflict and agreement between the two tasks and explain why in some cases nominal control performs well. It is also observed that measurement noise provides excitation helping to improve the quality of estimates.

This paper concerns the global cooperative output regulation problem for a class of nonlinear multi-agent systems with unknown control directions and unknown exosystem. The communication among agents makes it difficult to implement the Nussbaum gain technique to handle the non-identical unknown control directions. Moreover, the unknown parameter in the exosystem, which cannot be easily handled by the estimators in existing results, makes our problem more intricate. To overcome these challenges, we employ a novel internal model candidate to develop a two-layer control scheme for an adaptive distributed controller design. Theoretic stability analysis is presented and a numerical example is given to show the effectiveness of the proposed controller.

Model-based controllers based on incorrect estimates of the true plant behaviour can be expected to perform poorly. This work studies the effect of model plant mismatch on the closed loop behaviour and system performance for a certain class of MIMO systems. Performance is measured using a minimum variance index and a closely related user-specified criterion. We study the effect of model plant mismatch on the output variance and performance indices. Under mild assumptions, the performance of each output in a MIMO system can be analysed independently. Moreover, we propose an approach to distinguish the effect of model–plant mismatch from the effect of changes in disturbance characteristics on closed-loop performance. We define a sensitivity measure that relates system performance to model–plant mismatch, and use it to explore this sensitivity for three realistic types of parametric modelling errors. Next, we suggest a quantitative method that compares a system's actual output to its desired response in a transient setting. The performance of the transient response is demonstrably more sensitive to the model–plant mismatch than the steady state performance. The results are illustrated on industrial paper machine data.

The paper focuses on the robust control design of a look-ahead cruise control system. The aim of the look-ahead control is to consider the topographic conditions and speed regulations of the oncoming road sections. The look-ahead cruise control leads to an optimization method, which generates a fuel and time optimal reference speed for the vehicle. It requires external topographic and road information, and measurements concerning the vehicle, such as speed and acceleration. Since the signals of the vehicle are considered in the generation of the reference speed, the generator has an impact on the stability of the cruise control system. In the paper a robust H∞ cruise control design method is proposed, which considers the efect of the reference speed generation, and guarantees the stability and the speed tracking performance of the entire system. The efciency of the method is analyzed through a simulation example with real highway data.

A robust tuning problem for a two-degree-of-freedom model predictive controller is explored for single-input, single-output uncertain paper-making processes. The objective is to achieve satisfactory closed-loop responses, as measured by overshoots, settling times and output oscillations with user-specified parametric uncertainties. As the output variation cannot be easily specified by the end users, two methods are proposed to connect a total variation specification to user-friendly indices, based on which two algorithms are designed to solve the tuning problem. An application to a process extracted from the pulp and paper industry is employed to verify the effectiveness of the proposed algorithms.

Model predictive control is a modem method which was used in many different applications. One of the applications are electrical machines. Designing a controller for a synchronous drive has shown to be a challenging task. Many papers substitute one or more controllers in the cascade structure. Nevertheless, this approach calculates only the suboptimal trajectory, not the optimal trajectory. This paper presents a linearization approach of nonlinear model predictive controller and its preparation for implementation. Simulation is performed to verify presented ideas and its results are shown and discussed at the end of the paper.

The objective of this paper is to address the problem of state estimation in an Anaerobic Digestion Reactor (ADR) with unknown inputs - typically some influent concentrations, and to compare two different state and input estimation schemes. The first one is the classical Extended Kalman Filter (EKF) based on an augmented system that considers a slowly varying input (approach that has been already applied to this system and reported in the literature), whereas the second one is a recently proposed Unknown Input Observer (UIO), formulated in the spirit of a Kalman Filter, for continuous estimation with discrete measurements. The two filters are evaluated in simulation, demonstrating the superiority of the UIO.

The integration of process design and control (IPDC) has become increasingly important and received considerable academic and practitioner attention over the recent decades. Moreover, some works in the field have utilized advanced controllers into IPDC framework. Considering the fact that the hierarchical control structure which consists of local PID controllers and an advanced process control (APC) as the central controller has been used widely in industrial practice, in this paper, the two-level hierarchical control structure is taken into account within the IPDC framework for the first time. The IPDC problem using hierarchical control structure is formulated as a bi-level optimization problem, which separates the design decisions from the APC decisions and then keeps the problem size manageable. And the APC decisions are incorporated in a superstructure-based dynamic optimization formulation which considers both cost and controllability. A continuous stirred tank reactor is used to test the proposed IPDC methodology.

In this paper, a novel coordinated control method is proposed to handle the negative voltage impacts on weak sub-transmission systems, which are caused by wind power fluctuations. Firstly, wind variations are divided into large and small fluctuations. Then, according to the different control characteristics of on-load tap changers (OLTCs) and static compensators (STATCOMs), the OLTCs are used to minimize the impacts caused by large fluctuations, while the STATCOMs are used to eliminate the impacts introduced by small fluctuations. The model predictive control (MPC) and a consensus based distributed control techniques are adopted to design the control schedules of OLTCs and STATCOMs, respectively. It is shown that by the coordination of OLTCs and STATCOMs, voltage violations of points of interconnection (POIs) are eliminated, and voltage fluctuations at unregulated load buses are mitigated. The effectiveness of our proposed control scheme is presented through a case study on the modified IEEE 14-bus test system with a real wind power profile. According to the simulation results, it turns out that the proposed approach can reduce the operation number of OLTCs significantly, and decrease the required capacity of STATCOMs dramatically by comparison with the existing control scheme.

In this paper two control problems for a surface robotic vessel are addressed. One is design of a dynamic positioning (DP) system. The other deals with advanced dynamic positioning (ADP) which is extended by an unknown disturbance rejection. Considered plant is represented as a MIMO system which comprised of three independent SISO channels. For the DP problem all SISO channels are controlled by virtual control inputs generated using a robust approach, so called, "consecutive compensator". Then after an inverse MIMO transformation real control signals are calculated for robotic actuators. For the APD problem we design the cancellation scheme for channels with input delays that may be caused by a command transmission via the radio channel or computational complexity of the image processing. To finalize the investigation proposed DP algorithm is applied to the robotic complex of vessel motion modeling.

The paper proposes a method for the LPV-based (Linear Parameter-Varying) control design of the variable-geometry suspension system. The tilting actuation of the front wheel improves the lateral dynamics of the vehicle and assists for the driver in avoiding critical situations. The novelty of the method is the consideration of the nonlinearities in the tyre characteristics. The tyre forces are approximated by polynomials, which are used in the linearizing of the vehicle model. A parameter-varying vehicle model is derived, on which a robust LPV control is designed. The proposed method improves the performances of vehicle dynamics, and handles the critical maneuvers caused by e.g. a skidding car and high velocities. The proposed method is illustrated through CarSim simulation examples.

In this work, a new framework for control of power flow of an energy storage is proposed. As part of the framework, an advanced controller for manipulating the power flow of an energy storage system, a photovoltaic (PV) source, and the utility grid is developed. The new controller relies on the Model-based Predictive Control (MPC) concept. The proposed controller realizes an optimal control scheme that maximizes the local self-consumption of renewable energy source. At the same time, the energy consumption supplied by the electric grid is minimized subject to its price posted in the grid. The presented approach is evaluated through computer-aided simulations using data available from a real installation in Switzerland. The data consists of photovoltaic panels, industrial building as a load as well as a battery energy storage system (BESS).

An effective method of trajectory reconstruction is presented in order to identify aerodynamic parameters. A novel smoother URTSS, which can get more optimal ballistic parameters, is introduced because of the biggish error caused by the uncertain initial conditions. Finally the validity of the method is verified by the experimental data. It also proves that URTSS can improves the accuracy of the identification of aerodynamic parameters.

This paper considers the design of a controller and a constraint-enforcement scheme for application to dualmode, autonomous and manual steering systems. A tracking controller is designed to track a desired pinion angle during autonomous operation, and to provide assistive torque during manual operation. The tracking controller is designed using H ∞ synthesis with tracking made possible via the solution to a full-information output regulation problem. A reference governor scheme is implemented in order to enforce constraints. Numerical simulations are presented corresponding to an aggressive step-steer maneuver in autonomous mode and show strict constraint enforcement.

The price of electrocardiograph (ECG) machine on the market is very high. Currently, the technology used is still very complicated and ineffective, and the ECG machine cannot be connected to other devices. A new development of a low-cost ECG machine with a customized design was needed to integrate the machine with other devices. Therefore, the purpose of this study is to develop a low-cost ECG machine which can be connected to other devices and equipped with sensitivity and paper speed setting. So that portable ECG machines can be produced and used at small clinics in the society. In this study, the main controller of the 12 channels ECG machines was supported by ATMEGA16 microcontroller, that is available on the market at low prices. The main part of the ECG amplifier is built using a high common mode rejection ratio (CMRR) instrumentation amplifier (AD620) and a bandpass filter which the cutoff frequency for highpass filter and lowpass filter are 0.05 Hz and 100 Hz, respectively. In order to complement the previous study, some features were introduced such as selectivity and motor speed option. In this study, 10 participants are involved for data acquisition,and an ECG phantom was used to calibrate the machine. The performance of the ECG machine was evaluated using standard measurement namely relative percentage error (% error) and uncertainty (UA). The result shows that %error from all of the feature is less than 2% and the UA is 0.0 which shows that the ECG machine is feasible for diagnostic purposes.

We propose to use noncausal transfer functions to model the spatial behavior of cross-directional (CD) processes so as to circumvent the high-dimensionality of a causal transfer function. This noncausal representation is shown to have a causal-equivalent form. We prove that the covariance of maximum likelihood estimate of the causal-equivalent model asymptotically converges to that of the noncausal model. This result is then used to design optimal inputs in closed-loop for the original noncausal model of the CD process. An illustrative example is provided to highlight the advantage of using optimally designed excitation signal for CD closed-loop identification over white noise excitation or the current industrial practice of spatial bump excitation.

Multilevel inverters are highly capable of achieving higher quality output voltage waveforms and higher power ratings with the help of their multilevel structure. They have been of great interest in the field of power industry and are best suited for reactive power compensation. Multilevel voltage source inverters are capable of operating at high voltage with less electromagnetic interference and results in higher efficiency. The harmonic elimination in a multilevel voltage source inverter is of atmost importance and different types of modulation strategies can be applied to the inverters to eliminate these harmonics. Among these modulation techniques, Selective harmonic elimination PWM is a significant switching strategy that can be applied to the output voltage waveform of multilevel inverters for lower order harmonic elimination. This paper gives a review on the various optimization algorithms that is been used for the SHEPWM technique. Performance comparisons of these optimization algorithms in SHEPWM are discussed.