基于改进PSO-BP的模糊PID烧结炉温度控制

采用改进粒子群算法整定优化PID参数,并在反馈回路中加入BP神经网络预测下一时刻温度,将超前温度信息作为改进粒子群算法适应度函数参数,提前调整PID控制器参数,从而给出超前的控制,以此来减弱烧结炉温度变化的滞后性.通过模糊推理在温度控制过程中在线调整PID控制器参数,加强温度控制的跟随性.试验结果表明,与传统PID控制...     

大平台多塔楼新型隔震体系的智能磁流变控制

本文将隔震技术应用到大平台多塔楼结构中,研究了这种新型隔震体系的抗震性能,并将磁流变(MR)阻尼器设置于隔震层,探讨了这种新型隔震体系智能磁流变控制的减震效果.文中以北京通惠家园某典型小区为研究对象,建立了大平台多塔楼新型隔震减震体系的运动方程,考虑了隔震支座的非线性.本研究中MR阻尼器的半主动控制算法选用限幅最优控制算法,其主控制器采用H2/LQG方法来设计.仿真分析结果表明,这种新型隔震体系可以有效地减小上部住宅结构与下部平台的地震反应,为提高大平台多塔楼结构的抗震安全性提供了一条崭新的途径.采用MR阻尼器与这种新型隔震体系相结合可以进一步减小隔震结构下部平台的地震反应与隔震层的非线性反应,提高这种新型隔震体系的抗震安全性.     

A Neuro-Fuzzy Logic for ATIS Stand-Alone Control Systems: Structure, Calibration, and Analyses

Abstract:   An Advanced Traveler Information System (ATIS) stand-alone operation scheme is formulated as a bi-level optimization problem. The scheme logic attempts to optimize the network overall travel time by adjusting the path proportions while guessing the signal phase split decisions. An approximate simulation-based optimization algorithm is devised as an example of the logic operating this scheme. The logic is then replicated by a fuzzy-logic control system. Neural nets are utilized to develop the knowledge base of the fuzzy system and to calibrate the fuzzy set parameters. The neural nets utilize data replicates generated by the approximate simulation-based optimization algorithm. The calibration and effectiveness results of the fuzzy control system are presented.     

Model‐Based Multi‐input,Multi‐output Supervisory Semi‐active Nonlinear Fuzzy Controller

Abstract: The authors recently proposed a new multi‐input, single‐output (MISO) semi‐active fuzzy controller for vibration control of seismically excited small‐scale buildings. In this article, the previously proposed MISO control system is advanced to a multi‐input, multi‐output (MIMO) control system through integration of a set of model‐based fuzzy controllers that are formulated in terms of linear matrix inequalities (LMIs) such that the global asymptotical stability is guaranteed and the performance on transient responses is also satisfied. The set of model‐based fuzzy controllers is divided into two groups: lower level controllers and a higher level coordinator. The lower level fuzzy controllers are designed using acceleration and drift responses; while velocity information is used for the higher level controller. To demonstrate the effectiveness of the proposed approach, an eight‐story building structure employing magnetorheological (MR) dampers is studied. It is demonstrated from comparison of the uncontrolled and semi‐active controlled responses that the proposed design framework is effective in vibration reduction of a building structure equipped with MR dampers.     

A Hybrid Particle Swarm—Gradient Algorithm for Global Structural Optimization

Abstract: The particle swarm optimization (PSO) method is an instance of a successful application of the philosophy of bounded rationality and decentralized decision making for solving global optimization problems. A number of advantages with respect to other evolutionary algorithms are attributed to PSO making it a prospective candidate for optimum structural design. The PSO‐based algorithm is robust and well suited to handle nonlinear, nonconvex design spaces with discontinuities, exhibiting fast convergence characteristics. Furthermore, hybrid algorithms can exploit the advantages of the PSO and gradient methods. This article presents in detail the basic concepts and implementation of an enhanced PSO algorithm combined with a gradient‐based quasi‐Newton sequential quadratic programming (SQP) method for handling structural optimization problems. The proposed PSO is shown to explore the design space thoroughly and to detect the neighborhood of the global optimum. Then the mathematical optimizer, starting from the best estimate of the PSO and using gradient information, accelerates convergence toward the global optimum. A nonlinear weight update rule for PSO and a simple, yet effective, constraint handling technique for structural optimization are also proposed. The performance, the functionality, and the effect of different setting parameters are studied. The effectiveness of the approach is illustrated in some benchmark structural optimization problems. The numerical results confirm the ability of the proposed methodology to find better optimal solutions for structural optimization problems than other optimization algorithms.     

Hybrid Control of Structures Using Fuzzy Logic

Abstract: This investigation examined the application of control algorithms based on fuzzy logic to a class of hybrid structural control systems. The investigation included both analytical and experimental verification of the fuzzy control algorithm. The objective of the hybrid system under investigation is to obtain an ideal sliding system with perfect base isolation. As the hybrid system approaches the state of ideal isolation, the effects of imperfections, signal noise, uncertainties, modeling errors, and compensation errors start to play a dominant role in the control performance. Fuzzy logic (or fuzzy set theory) provides a simple framework to capture the effects of nonlinearities and uncertainties in a real problem without an explicit model of the plant or controller.
The applicability of this approach was first investigated analytically and then verified using a benchmark experimental model consisting of a 1:4 scale sliding-base isolated system controlled at its base by a servohydraulic actuator with a digital computer to provide the control signal. The fuzzy controller used feedback from either the acceleration of the moving foundation or the force at the interface to produce control forces in a series of shaking-table tests. The results from this study show the feasibility of the implementation of fuzzy logic to highly nonlinear problems.     

Optimal design of real‐size building structures using quantum‐behaved developed swarm optimizer

In this paper, the quantum‐behaved developed swarm optimizer is proposed for optimal design of real‐size building structures in which the quantum computing is introduced into the standard developed swarm optimizer. In this method, the position‐updating process for the search agents is conducted by simultaneous utilization of the so far best position of all particles, center of mass of all particles, so far best position of each particle, and the mean best position of all particles in which the first two of these aspects satisfy the exploration phase of the algorithm, whereas the other two are utilized for improving the exploitation phase of the proposed method. In order to evaluate the capability of the proposed method in dealing with difficult optimization problems, three real‐size building structures are considered, namely, a 10‐story building with 1,026 structural members, a 20‐story building with 3,860 members, and a 60‐story building with 8,272 members. The overall performance of the proposed quantum‐behaved developed swarm optimizer is compared with that of the standard developed swarm optimizer and other approaches. The obtained results proved that the proposed method is capable of providing better results for the considered examples than are the other algorithms.     

Comparison of numerical and experimental responses of pavement systems using various resilient modulus models

The accuracy of the structural design of flexible pavements based on mechanistic approaches is directly related to the appropriateness of the structural response algorithm and the material resilient modulus models selected. Mechanistic response algorithms can be based on layered theory or finite element algorithms. The geomaterials can be modeled as linear or nonlinear. To evaluate the appropriateness of the numerical models and the available resilient modulus models for estimating the response of pavements, several small-scale pavements were constructed and tested under different loads, loading areas and moisture conditions. A nonlinear numerical structural model was then utilized with different resilient modulus models to match the experimental responses. With some modifications, a three-parameter nonlinear model provided the same patterns as the experimentally measured values as long as the weight of the material was considered. In all cases, a transfer function was necessary to accommodate the differences in stiffness properties due to the differences between the field and the laboratory compaction methods.     

A hybrid LQR-PID control design for seismic control of buildings equipped with ATMD

This paper presents an efficient hybrid control approach through combining the idea of proportional-integral-derivative (PID) controller and linear quadratic regulator (LQR) control algorithm. The proposed LQR-PID controller, while having the advantage of the classical PID controller, is easy to implement in seismic-excited structures. Using an optimization procedure based on a cuckoo search (CS) algorithm, the LQR-PID controller is designed for a seismic- excited structure equipped with an active tuned mass damper (ATMD). Considering four earthquakes, the performance of the proposed LQR-PID controller is evaluated. Then, the results are compared with those given by a LQR controller. The simulation results indicate that the LQR-PID performs better than the LQR controller in reduction of seismic responses of the structure in the terms of displacement and acceleration of stories of the structure.     

Adaptive control of base-isolated structures against near-field earthquakes using variable friction dampers

This paper investigates the effectiveness of two adaptive control strategies for modulating the control force of variable friction dampers (VFDs) that are employed as semi-active devices in combination with laminated rubber bearings for the seismic protection of buildings. The first controller developed in this study is an adaptive fuzzy neural controller (AFNC). It consists of a direct fuzzy controller with self-tuning scaling factors based on neural networks. A simple neural network is implemented to adjust the input and output scaling factors such that the fuzzy controller effectively determines the command voltage of the damper according to current level of ground motion. A multi-objective genetic algorithm is used to learn the shape of the activation functions of the network. The second controller is based on the simple adaptive control (SAC) method, which is a type of direct adaptive control approach. The objective of the SAC method is to make the plant, the controlled system, track the behavior of the structure with the optimum performance. Here, SAC methodology is employed to obtain the required control force which results in the optimum performance of the structure. For comparison purposes, an optimal linear quadratic Gaussian (LQG) controller is also developed and considered in the simulations together with maximum passive operation of the friction damper. The results reveal that the developed adaptive controllers can successfully improve the seismic response of base-isolated buildings against various types of earthquake.     

Fuzzy自适应协调决策控制器的特性研究

本文针对一般模糊控制器在算法及其结构上存在的问题,提出模糊自适应协调决策控制算法(FACDC)。它模仿智能控制,采用分级递阶结构形式,通过一协调级来协调模糊控制与自适应PI控制,使得两者有机地结合起来,充分发挥两者的优点。由于本文采用的自适应算法是一种参考模型模糊自适应算法,因此避免了一般自适应算法的复杂性。     

Fuzzy-Logic-based control of payloads subjected to double-pendulum motion in overhead cranes

Overhead cranes are widely utilized. The use of overhead cranes suffers from the natural sway of payloads. The sway deteriorates not only safety but also throughput. Under certain operating conditions, the sway exhibits double-pendulum motions. The motions complicate crane control. A fuzzy inference model is entitled ‘single-input-rule modules’ (SIRMs). The SIRMs based model is preferable because it can decrease the number of fuzzy rules. This paper develops a SIRMs based fuzzy controller for transport control of double-pendulum-type systems. The controller includes six SIRMs that are dynamically weighted. Genetic algorithm (GA) is adopted to tune some parameters of the controller. Compared with other three controllers, i.e., passivity-based controller, sliding-mode controller and input-shaping controller, simulation results are presented to show the performance and effectiveness of the SIRMs based fuzzy controller for transport control of double-pendulum-type systems.     

A GBMO-based PIλDμ controller for vibration mitigation of seismic-excited structures

The advantages of fractional-order proportional–integral–derivative (FOPID) controllers in terms of flexibility, robustness against model uncertainties and good disturbance rejection, are good motivations to use them for vibration mitigation of seismic-excited structures. The application of the FOPID controller for adjusting the control force of an active tuned mass damper (ATMD) for seismic control a building is studied in this paper. Gases Brownian motion optimization (GBMO) algorithm is employed for optimal tuning of parameters of the FOPID and PID controllers. Evaluation of the frequency responses of the structure for uncontrolled and controlled cases shows that PID and FOPID controllers are very effective for the seismic control of structures. For further investigation, the performance of the FOPID controller in the reduction of seismic responses of the building in four well-known earthquake excitations is compared with those provided by the PID, linear–quadratic regulator (LQR) and fuzzy logic control (FLC). Simulation results show that the PID and LQR controllers provide the same performance in reducing the maximum top floor displacement of the structure. Moreover, the FLC and FOPID controllers demonstrate a superior performance in seismic control of the structure. The FLC is able to reduce the maximum displacements of all stories for all studied earthquakes. However, the proposed FOPID controller is able to provide a better performance in comparison with the FLC. In average, the FOPID controller mitigates the seismic responses of the structure as much as 29%, 27% and 15% better than the LQR, PID controller and FLC.     

Assessing the saving potential of blind controller via multi-objective optimization

This paper presents the results of computational experiments where multi-objective algorithms were used to tune a controller for blind movements in a residential building and a room of the LESO (Solar Energy and Building Physics Laboratory) experimental building. The blind controller, which is based on fuzzy logic, was optimized not only in terms of energy consumption but also in terms of thermal comfort. The goal is to show saving potential for intelligent blind controller in a real world example rather than in tailored idealized test rooms. Therefore, a state of the art simulation program with a multi-objective evolutionary algorithm was combined. It was found that with elementary control systems, like schedules for the lighting in a building, almost 40% of the energy could be saved. With the help of more advanced controllers this can be further increased. Also discussed in this paper are the results and the feasibility of implementing such a controller.     

A multi-attribute model for construction site layout using intuitionistic fuzzy logic

Most researchers have concentrated on studying optimization models to produce optimal construction site layout plans using different algorithms, while the overall method for evaluating and selecting the best site layout generated from optimization models has received less attention. In an optimization model, construction cost is generally considered in the objective function. However, several objectives, such as security and tie-in with external transportation, are difficult to quantify in the objective function and were not considered in previous studies. This paper focuses on evaluating and selecting the construction site layout considering qualitative objectives. An intuitionistic fuzzy multi-attribute decision-making model is developed that combines intuitionistic fuzzy set theory and the technique for order preference by similarity to the ideal solution (TOPSIS). This model overcomes the shortcomings of a traditional fuzzy set when describing ambiguous and unclear circumstances by using membership functions. The application of this model for site layout selection is shown to be reasonable and effective based on data from a real construction project.     

Optimal wide-area hybrid fuzzy controller to enhance the stability in power system

To stabilise power oscillation, power system stabilizer is often used as an effective device to enhance the damping of electromechanical oscillations in power systems. This device is working with small-signal stability, which is often applied as part of excitation control system. Different methods have been proposed to demonstrate the effectiveness of remote signals application in increasing damping of power system. In this paper, we used both local and remote signals control based on fuzzy controller and wide-area control, respectively. Accordingly, Takagi Sugeno controller based an intelligent algorithm and clustering algorithm is optimised. A global signal from the centralised controller is employed in wide-area control scheme to damp out the inter-area mode as well as local mode of oscillations. To demonstrate the capability of proposed strategy, three case studies have been used in this paper. The results obtained demonstrate the validity of the proposed model.     

Nonlinear multivariable control and performance analysis of an air-handling unit

To maintain satisfactory comfort conditions in buildings with low energy consumption and operation cost, control of air-conditioner units is required. In this paper, nonlinear control of an air-handling unit (AHU) is investigated and compared for two control approaches: gain scheduling and feedback linearization. A nonlinear multi input-multi output model (MIMO) of an air-handling unit (AHU) is considered. Both indoor temperature and relative humidity are controlled via manipulation of valve positions of air and cold water flow rates. Using an observer to estimate state variables, a hybrid control system including regulation system for disturbance rejection and nonlinear control system for tracking objectives is designed. Achievement of tracking objectives is investigated for various desired commands of indoor temperature and relative humidity; including a sequence of steps and ramps-steps. According to results, more quick time responses with a bit more overshoot in tracking set-points/paths are achieved by using feedback linearization method (especially for temperature). However, valves position as input control signals are associated with less oscillation (and consequently less energy consumption) when the controller designed based on gain scheduling approach is used. Finally, it is shown through phase portrait of the system that the controller designed based on feedback linearization shows a robust performance in the presence of random uncertainty in model parameters.     

Advanced energy‐based analyses of trusses employing hybrid metaheuristics

Total potential optimization using metaheuristic algorithm (TPO/MA) is an alternative method in structural analyses, and it is a black‐box application for nonlinear analyses. In the study, an advanced TPO/MA using hybridization of several metaheuristic algorithms is investigated to solve large‐scale structural analyses problems. The new generation algorithms considered in the study are flower pollination algorithm (FPA), teaching learning‐based optimization, and Jaya algorithm (JA). Also, the proposed methods are compared with methodologies using classic and previously used algorithms such as differential evaluation, particle swarm optimization, and harmony search. Numerical investigations were carried out for structures with four to 150 degrees of freedoms (design variables). It has been seen that in several runs, JA gets trapped into local solutions. For that reason, four different hybrid algorithms using fundamentals of JA and phases of other algorithms, namely, JA using Lévy flights, JA using Lévy flights and linear distribution, JA with consequent student phase, and JA with probabilistic student phase (JA1SP), are developed. It is observed that among the variants tried, JA1SP is seen to be more effective on approaching to the global optimum without getting trapped in a local solution.     

Advanced fuzzy logic controllers design and evaluation for buildings’ occupants thermal–visual comfort and indoor air quality satisfaction

The aim of this paper is to present and evaluate control strategies for adjustment and preservation of air quality, thermal and visual comfort for buildings’ occupants while, simultaneously, energy consumption reduction is achieved. Fuzzy PID, fuzzy PD and adaptive fuzzy PD control methods are applied. The inputs to any controller are: the PMV index affecting thermal comfort, the CO₂ concentration affecting indoor air quality and the illuminance level affecting visual comfort. The adaptive fuzzy PD controller adapts the inputs and outputs scaling factors and is based on a second order reference model. More specifically, the scaling factors are modified according to a sigmoid type function, in such a way that the measured variable to be as closer as possible to the reference model. The adaptive fuzzy PD controller is compared to a non-adaptive fuzzy PD and to an ON–OFF one. The comparison criteria are the energy required and the controlled variables response. Both, energy consumption and variables responses are improved if the adaptive fuzzy PD type controller is used. The buildings’ response to the control signals has been simulated using MATLAB/SIMULINK.     

Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment

Recent advances in control engineering suggest that hybrid control strategies, integrating some ideas and paradigms existing in different soft computing techniques, such as fuzzy logic, genetic algorithms, rough set theory, and neural networks, may provide improved control performance in wastewater treatment processes. This paper presents an innovative hybrid control algorithm leading to integrate the distinct aspects of indiscernibility capability of rough set theory and search capability of genetic algorithms with conventional neural-fuzzy controller design. The methodology proposed in this study employs a three-stage analysis that is designed in series for generating a representative state function, searching for a set of multi-objective control strategies, and performing a rough set-based autotuning for the neural-fuzzy logic controller to make it applicable for controlling an industrial wastewater treatment process. Research findings in the case study clearly indicate that the use of rough set theory to aid in the neural-fuzzy logic controller design can produce relatively better plant performance in terms of operating cost, control stability, and response time simultaneously, which is effective at least in the selected industrial wastewater treatment plant. Such a methodology is anticipated to be capable of dealing with many other types of process control problems in waste treatment processes by making only minor modifications.