136,490 research outputs found

    Optimal Input Design for Active Parameter Identification of Dynamic Nonlinear Systems

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    There are many important aspects to be considered while designing optimal excitation signal for system identification experiment in control applications. Active parameter identification is an important issue in system and control theory. In this dissertation, the problem of optimal input design for active parameter identification of dynamic nonlinear system is addressed. Real life physical systems are identified by excitation with a suitable input signal and observing the resulting output behavior of the system. It is important to choose the input signal intelligently in the sense that it is responsible to determine the accuracy and nature of the unknown system characteristics. This leads to a spurred interest in designing such an optimal excitation signals that can yield maximal information from the identification experiment. The information obtained from parameter identification is usually not accurate due to incomplete knowledge of the system, disturbance as exogenous inputs and noisy measurements. Hence, the input spectrum is designed in such a way that it can improve the system performance and shape the quality of obtained information. A welldesigned input signal can maximize the amount of information and reduce the experimental cost and time. The input signal is usually given some a-priori characteristics (knowledge on the pdf) so that \u201cexcitation\u201d of the system is guaranteed. In this thesis, a closed-loop method is investigated which is able to improve the parameter identification on the basis of the actual system\u2019s behavior. The effectiveness of the proposed algorithm is presented by the experimental results which corresponds to the perfect identification of the unknown parameter vector. The major technical contribution of this work is to propose an optimal feedback input design method for active parameter identification of dynamic nonlinear systems. The proposed framework can design such optimal excitation signals, considering the information from the identified parameters, that can maximize the amount of information from the identified parameters, guarantee to meet the specified control performance and minimize some cost function of the error covariance matrix of the identified parameters. The problem is formulated in a receding horizon framework where extended Kalman filter is used for system identification and the optimal input is designed in a nonlinear model predictive control framework. In order to carry out a comparison study, also Unscented Kalman Filter and Gaussian Sum Filter are used for the active parameter identification of dynamic nonlinear system. Towards this end, a suitable optimality criterion related to the unknown parameters is proposed and motivated as an information measure. The aim of the optimal input design is to yield maximal information from the unknown system by minimizing the cost related to the unknown parameters while maintaining some process performance and satisfying the possible constraints. Simulations are performed to show the effectiveness of the proposed algorithm

    Setpoint Tracking Predictive Control in Chemical Processes Based on System Identification

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    A Kraft recovery boiler in a pulp-paper mill provides a means for recovery of the heat energy in spent liquor and recovery of inorganic chemicals while controlling emissions. These processes are carried out in a combined chemical recovery unit and steam boiler that is fired with concentrated black liquor and which produces molten smelt. Since the recovery boiler is considered to be an essential part of the pulp-paper mill in terms of energy resources, the performance of the recovery boiler has to be controlled to achieve the highest efficiency under unexpected disturbances. This dissertation presents a new approach for combining system identification technique with predictive control strategy. System identification is the process of building mathematical models of dynamical systems based on the available input and output data from the system. Predictive control is a strategy where the current control action is based upon a prediction of the system response at some number of time steps into the future. A new algorithm uses an i-step-ahead predictor integrated with the least-square technique to build the new control law. Based on the receding horizon predictive control approach, the tracking predictive control law is achieved and performs successfully on the recovery boiler of the pulp-paper mill. This predictive controller is designed from ARX coefficients that are computed directly from input and output data. The character of this controller is governed by two parameters. One parameter is the prediction horizon as in traditional predictive control and the other parameter is the order of the ARX model. A recursive version of the developed algorithm can be evolved for real-time implementation. It includes adaptive tuning of these two design parameters for optimal performance. The new predictive control is proven to be a significant improvement compared to a conventional PID controller, especially when the system is subjected to noise and disturbances

    System Identification for the design of behavioral controllers in crowd evacuations

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    Behavioral modification using active instructions is a promising interventional method to optimize crowd evacuations. However, existing research efforts have been more focused on eliciting general principles of optimal behavior than providing explicit mechanisms to dynamically induce the desired behaviors, which could be claimed as a significant knowledge gap in crowd evacuation optimization. In particular, we propose using dynamic distancekeeping instructions to regulate pedestrian flows and improve safety and evacuation time. We investigate the viability of using Model Predictive Control (MPC) techniques to develop a behavioral controller that obtains the optimal distance-keeping instructions to modulate the pedestrian density at bottlenecks. System Identification is proposed as a general methodology to model crowd dynamics and build prediction models. Thus, for a testbed evacuation scenario and input?output data generated from designed microscopic simulations, we estimate a linear AutoRegressive eXogenous model (ARX), which is used as the prediction model in the MPC controller. A microscopic simulation framework is used to validate the proposal that embeds the designed MPC controller, tuned and refined in closed-loop using the ARX model as the Plant model. As a significant contribution, the proposed combination of MPC control and System Identification to model crowd dynamics appears ideally suited to develop realistic and practical control systems for controlling crowd motion. The flexibility of MPC control technology to impose constraints on control variables and include different disturbance models in the prediction model has confirmed its suitability in the design of behavioral controllers in crowd evacuations. We found that an adequate selection of output disturbance models in the predictor is critical in the type of responses given by the controller. Interestingly, it is expected that this proposal can be extended to different evacuation scenarios, control variables, control systems, and multiple-input multiple-output control structures.Ministerio de EconomĂ­a y Competitivida

    System Identification and Control of Cavity Noise Reduction

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    This dissertation first presents indirect closed-loop system identification through residual whitening, then identifies the cavity noise system and applies controllers to reduce the noise. High speed air flow over the cavity produces a complex oscillatory flow-field and induces pressure oscillations within the cavity. The existence of cavities induces large pressure fluctuations which generate undesirable and loud noise. This may have an adverse effect on the objects, such as reducing the stability and performance of overall system, or damaging the sensitive instruments within the cavity. System identification is the process of building mathematical models of dynamical systems based on the available input and output data from the systems. The indirect system identification by residual whitening is used to improve the accuracy of the identification result, and the optimal properties of the Kalman filter could be enforced for a finite set of data through residual whitening. Linear Quadratic Gaussian (LQG) and deadbeat controllers are applied to obtain the desired system performance. Linear Quadratic Gaussian (LQG) control design is the technique of combining the linear quadratic regulator (LQR) and Kalman tilter together, namely, state feedback (LQR) and state estimation (Kalman filter). Deadbeat control design is to bring the output to zero, and both indirect and direct algorithms are applied. For the indirect method, one needs to calculate the finite difference model coefficient parameters first, then form the control parameters. In the recursive direct algorithms, however, one can compute the control parameters directly. When systems change with time, the system parameters become time-varying. An adaptive predictive control is needed for this situation. Since the system parameters are time-varying, the control parameters need to be updated in order to catch up with the systems\u27 changes. The classical recursive least-squares technique is used for the recursive deadbeat controller, and it could be operated for on-line application

    Direct Adaptive Predictive Control For Wastewater Treatment Plant

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    The purpose of this paper was to design a much simpler control method for a wastewater treatment plant. The work proposes a direct adaptive predictive control (DAMPC) also known as subspace predictive control (SPC) as a solution to the conventional one. The adaptive control structure is based on the linear model of the process and combined with numerical algorithm for subspace state space system identification (N4SID). This N4SID plays the role of the software sensor for on-line estimation of prediction matrices and control matrices of the bioprocess, joint together with model predictive control (MPC) in order to obtain the optimal control sequence. The performances of both estimation and control algorithms are illustrated by simulation results. Stability analysis is done to investigate the response of the system-proposed when parameter changes exist. This project proves that subspace-adaptive method has a large number of important and useful advantages, primarily the application ability to Multi Input Multi Output (MMO) systems, and the low requirements on prior system information. Given the advantages observed, the most likely areas of application for the proposed algorithm are multivariable processes, about which little information is known such as this wastewater treatment plant. Hence, direct adaptive predictive control (DAMPC) can provide simplicity and good performance in of an activated sludge process

    Control Relevant System Identification Using Orthonormal Basis Filter Models

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    Models are extensively used in advanced process control system design and implementations. Nearly all optimal control design techniques including the widely used model predictive control techniques rely on the use of model of the system to be controlled. There are several linear model structures that are commonly used in control relevant problems in process industries. Some of these model structures are: Auto Regressive with Exogenous Input (ARX), Auto Regressive Moving Average with Exogenous Input (ARMAX), Finite Impulse Response (FIR), Output Error (OE) and Box Jenkins (BJ) models. The selection of the appropriate model structure, among other factors, depend on the consistency of the model parameters, the number of parameters required to describe a system with acceptable accuracy and the computational load in estimating the model parameters. ARX and ARMAX models suffer from inconsistency problem in most open-loop identification problems. Finite Impulse Response (FIR) models require large number of parameters to describe linear systems with acceptable accuracy. BJ, OE and ARMAX models involve nonlinear optimization in estimating their parameters. In addition, all of the above conventional linear models, except FIR, require the time delay of the system to be separately estimated and included in the estimation of the parameters. Orthonormal Basis Filter (OBF) models have several advantages over the other conventional linear models. They are consistent in parameters for most open-loop identification problems. They are parsimonious in parameters if the dominant pole(s) of the system are used in their development. The model parameters are easily estimated using the linear least square method. Moreover, the time delay estimation can be easily integrated in the model development. However, there are several problems that are not yet addressed. Some of the outstanding problems are: (i) Developing parsimonious OBF models when the dominant poles of the system are not known (ii) Obtaining a better estimate of time delay for second or higher order systems (iii) Including an explicit noise model in the framework of OBF model structures and determine the parameters and multi-step ahead predictions (iv) Closed-loop identification problems in this new OBF plus noise model frame work This study presents novel schemes that address the above problems. The first problem is addressed by formulating an iterative scheme where one or two of the dominant pole(s) of the system are estimated and used to develop parsimonious OBF models. A unified scheme is formulated where an OBF-deterministic model and an explicit AR or ARMA stochastic (noise) models are developed to address the second problem. The closed-loop identification problem is addressed by developing schemes based on the direct and indirect approaches using OBF based structures. For all the proposed OBF prediction model structures, the method for estimating the model parameters and multi-step ahead prediction are developed. All the proposed schemes are demonstrated with the help of simulation and real plant case studies. The accuracy of the developed OBF-based models is verified using appropriate validation procedures and residual analysis

    Optimal operation of combined heat and power systems: an optimization-based control strategy

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    The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany.Peer ReviewedPostprint (author's final draft

    Optimal Control for Aperiodic Dual-Rate Systems With Time-Varying Delays

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    [EN] In this work, we consider a dual-rate scenario with slow input and fast output. Our objective is the maximization of the decay rate of the system through the suitable choice of the n-input signals between two measures (periodic sampling) and their times of application. The optimization algorithm is extended for time-varying delays in order to make possible its implementation in networked control systems. We provide experimental results in an air levitation system to verify the validity of the algorithm in a real plant.This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) under the Projects DPI2012-31303 and DPI2014-55932-C2-2-R.Aranda-Escolástico, E.; Salt Llobregat, JJ.; Guinaldo, M.; Chacon, J.; Dormido, S. (2018). Optimal Control for Aperiodic Dual-Rate Systems With Time-Varying Delays. Sensors. 18(5):1-19. https://doi.org/10.3390/s18051491S119185Mansano, R., Godoy, E., & Porto, A. (2014). The Benefits of Soft Sensor and Multi-Rate Control for the Implementation of Wireless Networked Control Systems. Sensors, 14(12), 24441-24461. doi:10.3390/s141224441Shao, Q. M., & Cinar, A. (2015). System identification and distributed control for multi-rate sampled systems. Journal of Process Control, 34, 1-12. doi:10.1016/j.jprocont.2015.06.010Albertos, P., & Salt, J. (2011). Non-uniform sampled-data control of MIMO systems. Annual Reviews in Control, 35(1), 65-76. doi:10.1016/j.arcontrol.2011.03.004Cuenca, A., & Salt, J. (2012). RST controller design for a non-uniform multi-rate control system. Journal of Process Control, 22(10), 1865-1877. doi:10.1016/j.jprocont.2012.09.010Cuenca, Á., Ojha, U., Salt, J., & Chow, M.-Y. (2015). A non-uniform multi-rate control strategy for a Markov chain-driven Networked Control System. Information Sciences, 321, 31-47. doi:10.1016/j.ins.2015.05.035Kalman, R. E., & Bertram, J. E. (1959). 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    Data-driven adaptive model-based predictive control with application in wastewater systems

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    This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms
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