Solving Control Problems Research Articles

The Bubnov–Galerkin method in control problems for bilinear systems

We suggest to apply the Bubnov---Galerkin method to solving control problems for bilinear systems. We reduce the solution of a control problem to a finite-dimensional system of linear problem of mo...

Imprementation of Sochastic Searching for Complex Processes Identification

This paper presents implementation of stochastic searching method for complex technological process identification. As the first step of control systems design is identification of a process mathematcal model. The author uses certain stochastic algorithms created & explained in some of his earlier published papers. The basis of the said algorithms is a non-linear stochastic searching. From general math process description this paper considers a process steady state property as an object of identification. Some of presented examples are useful to observe possible problems most of control system designers face through attempts to solve control problems or to modify process technology. As fas as the implemented algorithms are concerned certain advantages & efficiency have been obtained vs regular gradient methods. Since the said algorithms are with some modification useful for training of artificial neural networks, the future development could be toward an inteligent control systems design & implementation. Numerical procedures have been processed by using MATLAB.

Control of a PCM ventilated facade using reinforcement learning techniques

Artificial intelligence techniques have been successfully applied to control dynamic systems looking for an optimal control. Among those techniques, reinforcement learning has been shown as particularly effective at reducing the dimensionality of some real problems and solving control problems by learning from experience. The use of thermal energy storage active systems in the building sector is identified as suitable option to reduce their energy demand for heating and cooling. However, these systems might be expensive and require appropriate control strategies in order to improve the performance of the building. In this paper a ventilated facade with PCM is controlled using a reinforcement learning algorithm. The ventilated facade uses mechanical ventilation during nighttime to solidify the PCM and releases this cold stored to the inner environment during the peak demand period. It is crucial to decide correctly the schedule of charge and discharge process of the PCM according to the weather and indoor conditions. An experimentally validated numerical model is used to test the performance of the control algorithm under different weather conditions. Important improvements on the energy savings due to the use of control strategies were found and supported by the data under the different tested climatic conditions.

Active Disturbance Rejection Control Design for Fast AFM

Active disturbance rejection control (ADRC) as an alternative to classical PID control to solving control problems, has gained significant traction these years. With its simple tuning method, robustness against process parameter variations and ability of disturbance rejection, we tried it in our homemade fast atomic force microscope (AFM). Experiments are carried out in contact mode on standard optical grating sample with 50Hz line rate. The results show that ADRC can reject external disturbances well, and can reduce system vibrations obviously.

Optimization of hybrid stochastic differential systems in communications networks

This paper addresses issues from applied stochastic analysis for modeling and solving control problems in communications networks. We consider the problem of optimal scheduling in a wireless system with time varying traffic. The system is handled by a single base station transmitting over time varying channels. This may be the case in practice for a hybrid TDMA–CDMA (Time Division Multiple Access–Code Division Multiple Access) system. Heavy traffic approximation for the physical system yields a problem of optimal control in a constrained hybrid stochastic differential system. Here constrained means bounded or reflected in the K-dimensional positive orthant. In this work we establish a closed form solution for the nascent optimal control problem. The aim is to find a control which minimizes the expected total delay for the users. The control is constrained to satisfy some inequalities. Hence it seems natural that the cost function involve a penalty term for breaking these inequalities. We study this control problem by a dynamic programming approach and we are led to the resolution of a Hamilton–Jacobi–Bellman (HJB) equation in the finite dimensional space R+K. While optimizing it turns out that the optimum falls inside the class of feedback controls. Hence our method consists in finding a smooth solution of Bellman’s equation and consequently getting a unique solution for the closed loop. Here a separation of variables enables us to construct an explicit C2 solution of the HJB equation so that existence of a unique optimal control is proven. Further stochastic analysis of the hybrid stochastic differential system under the optimal control is provided: the Fokker–Planck equation for the distribution density of the state.

New results on strong practical stability and stabilization of discrete linear repetitive processes

Discrete linear repetitive processes operate over a subset of the upper-right quadrant of the 2D plane. They arise in the modeling of physical processes and also the existing systems theory for them can be used to effect in solving control problems for other classes of systems, including iterative learning control design. This paper uses a form of the generalized Kalman–Yakubovich–Popov (GKYP) Lemma to develop new linear matrix inequality (LMI) based stability conditions and control law design algorithms for the strong practical stability property. Relative to alternatives, the LMIs for stability have a simpler structure and it is not required to impose particular structures on the matrix variables. These properties are extended to control law design, including those where state vector access is not required. Illustrative numerical simulation examples conclude the paper.

Decentralized SISO Active Disturbance Rejection Control of the Newell-Lee forced circulation evaporator

Active Disturbance Rejection Control (ADRC) has received considerable attention in recent years as an effective tool for advanced control practitioners to solve control problems for nonlinear uncertain systems. This paper presents results of a simulation study for the control of an evaporator system benchmark, when a multiple linear ADRC control structure is used, and the controllers are designed based on first and second order linear models approximating the process dynamics. In addition to the control performance under nominal conditions, the robustness with respect to plant-model mismatch is studied. The major advantage of ADRC compared to MPC and PI control approaches is the simple and transparent tuning, and that only a coarse process model is sufficient for the design. For a broader industrial application of ADRC in the process industries, user-friendly off-line design tools as well as real-time ADRC control software for PLCs and DCS still have to be developed.

Fuzzy Neural-Network-Based Controller

Using a controller is necessary for any automation system. The controller must be cheap, reliable, user friendly and not cause any problems for inputs and outputs. Classical control systems like proportional integral derivative (PID) put adequate results of linear systems and continuous-time. In fact, real control systems are time-variant, with non-linearity and poorly calculated dynamic variables. For this reason, conventional control systems need an expert person to adjust controller parameters in general. Sometimes an operator is required to solve control problems. Human control is not completely reliable. Also, it does not include any electronic communication. In modern factories, every point must be monitored and electronically controlled from remote points when necessary. In this study, including every electronic communication channel, a simplified handling, low-cost, reliable, Fuzzy Neural Network Controller (FNNC) is designed.

SoftMobile: control evolution for future heterogeneous mobile networks

Heterogeneous mobile networks, HMNs, with flexible spectrum use, densified cell deployment, and multi-layer multiple types of radio access technologies, are expected to be key to meeting the 1000 times increase of mobile data traffic in 2020 and beyond. The increasing complexity in HMNs renders the control and coordination of networks a challenging task. The control frameworks of current cellular networks, which were previously designed for sparse network deployment, hit the wall for HMNs. HMNs need good separation of control and data planes, and call for novel control methods to handle the highly complex dynamics therein. In this article, we first briefly review the control planes of 2G to 4G cellular networks, and then identify their constraints to support HMNs. We analyze the complexity in HMNs and examine enabling control technologies for HMNs. We believe new thinking is needed for efficient control of HMNs. SDN is a promising technology to solve complex control problems in the Internet. Principle-based control methods applied in SDN are promising to solve control problems in HMNs. Several SDN approaches have been proposed for mobile networks. However, most of them are targeted at mobile core networks. We propose an SDNbased control framework named SoftMobile to coordinate complex radio access in HMNs. The main features of SoftMobile are low-layer abstraction, separation of control and data planes, and network-wide high-layer programmable control. Important research problems in SDN for mobile networks are highlighted. We believe SDN for mobile networks will be the controlling evolution of future HMNs.

Assessment of the applicability of independent brain-computer interfaces in robotic systems

The paper is devoted to assessing the applicability of the use of independent brain-computer interfaces in engineering systems (in particular, in robotics). This technology provides the closest integration between human beings and automated systems for various purposes. It may be of particular interest in solving control problems in robotic systems—in particular, in systems of devices extending human manipulatory capabilities. However, currently there is no universal algorithm for the assessment of the applicability of brain-computer interfaces in various technological systems. This hampers the implementation of this technology. A description of the proposed method of assessing applicability is given that is based on the conceptual and methodological apparatus of digital signal processing, i.e., the discrete Fourier transform, cross-correlation coefficient, and distance between the vectors. The stages of operation of the proposed algorithm can be reduced to a step-by-step comparison of groups of averaged spectra of amplitudes calculated for signals that are measured of the brain’s electrical activity. Experimental results of the practical application of the method that has been developed are presented in graphical form. The method makes it possible to efficiently assess the applicability of the use of an independent brain-computer interface in various technological systems and circumstances.

Control Approach in Cloaking Problems for 2-D Model of Sound Scattering

We consider control problems for 2-D Helmholtz equation in a bounded domain with partially coated boundary. These problems are associated with acoustic cloaking. Dirichlet boundary condition is given on one part of the boundary and the impedance boundary condition is given on another part of the boundary. The role of control in control problem under study is played by surface impedance. Solvability of control problem is proved and optimality system is derived.

Error estimates for spectral approximation of elliptic control problems with integral state and control constraints

The goal of this paper is to investigate the Legendre–Galerkin spectral approximation of elliptic optimal control problems with integral state and control constraints. Thanks to the appropriate base functions of the discrete spaces, the discrete system is with sparse coefficient matrices. We first present the optimality conditions of the control system. Then a priori and a posteriori error estimates both in H1 and L2 norms are derived. Some numerical tests indicate that the spectral accuracy can be achieved, and the proposed method is competitive for solving control problems.

A recurrent neural fuzzy controller based on self‐organizing improved particle swarm optimization for a magnetic levitation system

SummaryThis paper proposes a recurrent neural fuzzy controller (RNFC) approach based on a self‐organizing improved particle swarm optimization (SOIPSO) algorithm used for solving control problems. The proposed SOIPSO algorithm can adaptively determine the number of fuzzy rules and automatically adjust the parameters in an RNFC. The proposed learning algorithm consisted of phases of structure and parameter learning. Structure learning adopts several subswarms to constitute the adjustable variables in fuzzy systems, and an elite‐based structure strategy determines the suitable number of fuzzy rules. This paper proposes an improved particle swarm optimization technique, which consists of the modified evolutionary direction operator (MEDO) and traditional PSO techniques. The proposed MEDO method used the EDO and migration operation to improve the search ability of a global solution. Finally, the proposed RNFC approach based on the SOIPSO learning algorithm (RNFC–SOIPSO) was adopted to control a magnetic levitation system. Experimental results demonstrated that the proposed RNFC–SOIPSO model outperforms other models. Copyright © 2014 John Wiley & Sons, Ltd.

Mathematical Modeling of the Control of Dynamics of Thick Elastic Plates. I. Control Under Continuous Desired State

The authors formulate and solve control problems for the dynamics of the three-dimensional field of transverse dynamic displacements of elastic plates of finite thickness coordinated with the mean square fixed continuous desired condition. The control factors are superficially distributed external-dynamic loads, initial and boundary disturbing factors taken individually, in pairs, and all three together. The features of the solution of these problems are described for the case where some of the initial and boundary disturbances are not important. The conditions of the accuracy and uniqueness of the solutions are analyzed.

Control of dynamic microsystems

This paper formulates and solves control problems for nonlinear microsystems which comprise micro-electromechanical devices, micromachined transducers and microelectronics. We perform a consistent dynamic analysis and coherent designs with a minimum level of simplifications using high-fidelity mathematical models. The proposed methodology enables practical implementation for multi-input/multi-output systems due to overall conceptual consistency, design coherence, computational efficiency, algorithmic effectiveness and hardware simplicity. Various issues in nonlinear analysis and control are examined and experimentally verified substantiating design concepts for high-performance microsystems. The reported findings are demonstrated for a proof-of-concept closed-loop electrostatic microactuator.

A survey on soft computing techniques used in intelligent building control

In this paper, we present different papers discussing soft computing methods applied in development, optimization and control of intelligent building systems. We present the approaches used, their advantages in solving control problems, analyze and modelling of human behavior in order to implement energy saving control solutions besides accounting for user comfort.

Compensatory neural fuzzy networks with rule-based cooperative differential evolution for nonlinear system control

This study presents a rule-based cooperative differential evolution (RCDE) learning algorithm to optimize the parameters of a compensatory neural fuzzy network (CNFN) for solving control problems. CNFN uses the compensatory degree for fuzzy reasoning to dynamically adjust fuzzy operators that can make the fuzzy system more adaptive and effective. RCDE decomposes the fuzzy system into multiple rule-based subpopulations where each subpopulation represents a fuzzy rule set, and each individual within each subpopulation evolves by differential evolution (DE) separately. The proposed RCDE uses cooperative behavior among multiple subpopulations for combining their information and building the complete fuzzy system to accelerate the search and increase global search capacity. Finally, the experimental results show that the proposed RCDE method better approximates the global optimal solution and has a faster convergence rate than the other methods.

Robust Controller in the Structure of Lateral Control of Maneuvering Aircraft

Efficiency in the process of aircraft control is expressed as the adaptability of the control system to the changes in the physical properties of the object. As a criterion of efficiency there exists an indicator by which the rules of selecting the best ways of solving control problems are determined. The contribution is describing the method of analyzing, synthetizing the designed parameters of a robust controller for lateral control of aircraft utilizing assisting damping automated devices (ADAD). The design of the controller parameters was done using Matlab program with the demo version of the lateral control of a maneuvering aircraft and the synthesis of the suggested controller is based on applying the

Корректная разрешимость задач управления для систем дифференциальных уравнений неявного вида

Корректная разрешимость задач управления для систем дифференциальных уравнений неявного вида

Using proteomic analysis to find the proteins involved in resistance against Sclerotinia sclerotiorum in adult Brassica napus

Sclerotinia disease, caused by Sclerotinia sclerotiorum, is one of the most serious plant diseases in China. Research on the mechanism of disease resistance to S. sclerotiorum will help solve control problems. In this study, near-isogenic lines were first used in combination with the proteomic technique. A comparison of protein expression profiles in a susceptible line with those in a resistant line during the interaction of adult Brassica napus with S. sclerotiorum resulted in the identification of 20 important proteins related to disease resistance. Those proteins were then determined to be involved in various functions, including pathogen resistance, antioxidation, and transcription regulation. Our finding showed that some proteins involved in defence—a glycine rich protein (GRP); a trypsin inhibitor protein (TIP); two heat shock proteins (HSPs); and a thiol methyltransferase (TMT)—were upregulated or expressed specially in the resistant B. napus lines. These proteins contribute to ROS (reactive oxygen species) elimination and pathogen-defence in the resistant line, which would help the host defend itself against S. sclerotiorum. As a consequence, the onset of PCD (programmed cell death) was delayed, and the spread of S. sclerotiorum was slowed in the resistant line. Presented results underline the role of specific proteins in the disease process. By building on these results, future research may help determine the genes that are important in conveying resistance to S. sclerotiorum in B. napus.