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101.
A General Robust MPC Design for the State‐Space Model: Application to Paper Machine Process
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Applying model predictive control (MPC) in some cases such as complicated process dynamics and/or rapid sampling leads us to poorly numerically conditioned solutions and heavy computational load. Furthermore, there is always mismatch in a model that describes a real process. Therefore, in this paper in order to prevail over the mentioned difficulties, we design a robust MPC using the Laguerre orthonormal basis in order to speed up the convergence at the same time with lower computation adding an extra parameter “a” in MPC. In addition, the Kalman state estimator is included in the prediction model and accordingly the MPC design is related to the Kalman estimator parameters as well as the error of estimations which helps the controller react faster against unmeasured disturbances. Tuning the parameters of the Kalman estimator as well as MPC is another achievement of this paper which guarantees the robustness of the system against the model mismatch and measurement noise. The sensitivity function at low frequency is minimized to tune the MPC parameters since the lower the magnitude of the sensitivity function at low frequency the better command tracking and disturbance rejection results. The integral absolute error (IAE) and peak of the sensitivity are used as constraints in optimization procedure to ensure the stability and robustness of the controlled process. The performance of the controller is examined via the controlling level of a Tank and paper machine processes. 相似文献
102.
103.
In this paper, a non-cooperative distributed MPC algorithm based on reduced order model is proposed to stabilize large-scale systems. The large-scale system consists of a group of interconnected subsystems. Each subsystem can be partitioned into two parts: measurable part, whose states can be directly measured by sensors, and the unmeasurable part. In the online computation phase, only the measurable dynamics of the corresponding subsystem and neighbour-to-neighbour communication are necessary for the local controller design. Satisfaction of the state constraints and the practical stability are guaranteed while the complexity of the optimization problem is reduced. Numerical examples are given to show the effectiveness of this algorithm. 相似文献
104.
Model Predictive Control for Discrete‐Time Systems with Random Delay and Randomly Occurring Nonlinearity
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In this paper, the model predictive control problem is investigated for a class of discrete‐time systems with random delay and randomly occurring nonlinearity. The randomly occurring nonlinearity, which describes the phenomena of a class of nonlinear disturbances occurring in a random way, is modeled according to a Bernoulli distributed white sequence with a known conditional probability. Moreover, the random delay is governed by a discrete‐time finite‐state Markov chain. The approach of delay fractioning is applied to the controller synthesis. It is shown that the proposed model predictive controller guarantees the stochastic stability of the closed‐loop system. Finally, a numerical simulation is given to illustrate the effectiveness of the proposed method. 相似文献
105.
106.
Predictive control for voltage collapse avoidance using a modified discrete multi-valued PSO algorithm 总被引:2,自引:0,他引:2
Voltage stability is one of the most challenging concerns that power utilities are confronted with, and this paper proposes a voltage control scheme based on Model Predictive Control (MPC) to overcome this kind of instability. Voltage instability has a close relation with the adequacy of reactive power and the response of Under Load Tap Changers (ULTCs) to the voltage drop after the occurrence of a contingency. Therefore, the proposed method utilizes reactive power injection and tap changing to avoid voltage collapse. Considering discrete nature of the changes in the tap ratio and also in the reactive power injected by capacitor banks, the search area for the optimizer of MPC will be an integer area; consequently, a modified discrete multi-valued Particle Swarm Optimization (PSO) is considered to perform this optimization. Simulation results of applying the proposed control scheme to a 4-bus system confirm its capability to prevent voltage collapse. 相似文献
107.
Moira Bent 《New Review of Information Networking》2013,18(2):81-99
This article uses data from research conducted in the course of writing a book on the needs, wants and characteristics of researchers to examine how researchers view themselves and the research process. As a result of this, the Seven Ages of Research model was developed which categorises a researcher's learning life into a series of discrete stages. The article explores learning needs relevant to each age in the learning life and links them to the development of information literacy. The authors explore the polarisation that exists between what researchers think research is about and what they believe the library can offer them, and suggest that information literacy needs to be seen as involving a change in understanding of, and attitude to, the world of information, as well as the nature of those information needs. Finally, they explore the librarian's role in an e-environment which has changed the way researchers work and identify some of the challenges facing library and information services professionals in the future in supporting researchers throughout their learning lives. 相似文献
108.
109.
Nonlinear model predictive control with relevance vector regression and particle swarm optimization 总被引:1,自引:0,他引:1
In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression (RVR) and particle swarm optimization with controllable random exploration velocity (PSO-CREV) is applied to a catalytic continuous stirred tank reactor (CSTR) process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function (RBF) kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control (MPC) using a deterministic sparse kernel learning technique called Least squares support vector machines (LS-SVM) regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control. 相似文献
110.
A steady-state interval operability methodology is introduced here for multivariable non-square systems with fewer inputs than output variables to be used in the design of model-based constrained controllers (MPC, DMC). For such systems, set-point control is not possible for all the outputs and interval control is needed. The proposed iterative approach enables the selection of the needed interval constraints systematically, so that the tightest possible control is achieved without rendering the control problem infeasible. The application of this methodology to high-dimensional industrial problems characterizing processes of Air Products and Chemicals and DuPont shows that very significant reduction of the constrained region can be achieved from the steady-state point of view. Ratios of the initial to the calculated volume of the constrained regions examined range between 104 and 108. 相似文献