Predictive inelastic state control of modern structures during earthquakes

Critical earthquake load inputs for multi-degree-of-freedom inelastic structures

A method of generating energy density spectra obtained from earthquake response in actively controlled structural models is proposed for practical application based on the definition of different energy forms. Both elastic and inelastic energy density spectra are generated to study the energy transfer among various energy components. In the parametric study of elastic spectra with active control, the control force restraint parameter is adopted as an additional parameter in addition to the two commonly used parameters (i.e. natural period and damping ratio), and the yield displacement is adopted as the additional parameter in the analysis of inelastic spectra. In this first part of the research, numerical studies are carried out for both elastic and inelastic controlled structures using optimal linear control and ad hoc control algorithms. The energy density spectra of the three main components of the input energy, i.e. damping energy, control energy, and plastic energy, of the inelastic structure are investigated using two methods: (1) by varying structural stiffness; and (2) by varying structural mass. These energy density spectra for structures with a range of periods are evaluated and compared to verify their engineering significance. Copyright © 2005 John Wiley & Sons, Ltd.

This paper proposes a predictive controller design for a sensorless interior permanent magnet synchronous motor drive system. The controllers include a current-loop predictive controller and a speed-loop predictive controller with an external load estimator. In addition, the estimated error of the rotor position estimator is reduced due to the using of the on-line tuning stator resistance and q-axis inductance. By using the proposed control algorithms, the closed-loop sensorless interior permanent magnet synchronous motor drive system can achieve fast transient responses, satisfactory load disturbance responses, and good tracking responses. A digital signal processor, TMS 320F2812, is used to perform the rotor position estimating algorithm and the predictive control algorithm. As a result, the hardware is very simple. Experimental results can validate the theoretical analysis and show that the predictive controllers have better performance than the PI controllers.

Servo applications often require fast response inner current loop in order to ensure high dynamic performance of the whole system. Predictive control algorithm can achieve fast dynamic response while avoiding overshoot. However, there is an inherent defect that it relies on the accurate mathematical model of the controlled object. An incremental predictive current control algorithm adopting an improved PWM updating strategy was presented. The relationship between system stability and parameter mismatch was analyzed using root locus method. Simulation and experimental results demonstrate that the proposed incremental predictive current control algorithm shows well dynamic performance and the improved updating strategy achieves significantly increased robustness to parameter mismatch, verifying the feasibility of the methods presented.

This article deals with the design of a simple predictive control algorithm applied to a bidirectional DC-DC power converter for the angular speed control of a DC motor. We used the dynamics of a DC motor but mathematically reduced them to arrive at a simple model that is ideal for our purpose, not only to meet the control objective but also to generate reliable data for further analysis. This predictive control approach is based on the discrete time mathematical model of a DC motor. A huge capacitor to emulate an electric vehicle battery was then successfully connected to our experimental platform. Due to the robustness of the proposed control algorithm, the same predictive control scheme provided sufficient information to monitor the battery’s state. On this basis, and due to the system’s efficiency, it was possible to configure a fault detection scheme in our electric car battery emulator using only classical statistical tools. A PIC18F252 microcontroller was used in our experimental platform to implement our predictive control algorithm. It was then appropriately coupled to the power electronics required by the DC-DC converter to drive the DC motor. Our experimental results proved the excellent performance of the control method and also of the health monitoring system. On the other hand, the main difficulty in achieving our main goal was the realization of discrete control, which had to be as simple as possible while maintaining the control objective and while also being capable of generating reliable data for the health monitoring stage. Thus, the primary contribution of this work was the development of the predictive control of the speed of a universal motor, followed by the modification of the experimental design to simulate an electric car battery and the introduction of a novel statistical method for fault detection.

During the spinning forming process of chitosan and collagen absorbable suture, the diameter is uneven. To solve this problem, this article provides a diameter control system of absorbable suture which based on generalized predictive control (GPC) algorithm. The system consists of the diameter detection module based on linear CCD, the control module based on STM32, the actuator of reverse series type double hydraulic cylinder. The control algorithm adopts self-adaptive generalized predictive control (GPC) algorithm which successes in realizing closed loop control of large time delay system in the process of absorbable suture forming. The results of the experiment show that the control system has good rapidity and good stability, the control accuracy reached 1%.

The automatic train operation (ATO) system of urban rail trains includes a two-layer control structure: upper-layer control and lower-layer control. The upper-layer control is to optimize the target speed curve of ATO, and the lower-layer control is the tracking by the urban rail train of the optimal target speed curve generated by the upper-layer control according to the tracking control strategy of ATO. For upper-layer control, the multi-objective model of urban rail train operation is firstly built with energy consumption, comfort, stopping accuracy, and punctuality as optimization indexes, and the entropy weight method is adopted to solve the weight coefficient of each index. Then, genetic algorithm (GA) is used to optimize the model to obtain an optimal target speed curve. In addition, an improved genetic algorithm (IGA) based on directional mutation and gene modification is proposed to improve the convergence speed and optimization effect of the algorithm. The stopping and speed constraints are added into the fitness function in the form of penalty function. For the lower-layer control, the predictive speed controller is designed according to the predictive control principle to track the target speed curve accurately. Since the inflection point area of the target speed curve is difficult to track, the softness factor in the predictive model needs to be adjusted online to improve the control accuracy of the speed. For this paper, we mainly improve the optimization and control algorithms in the upper and lower level controls of ATO. The results show that the speed controller based on predictive control algorithm has better control effect than that based on the PID control algorithm, which can meet the requirements of various performance indexes. Thus, the feasibility of predictive control algorithm in an ATO system is also verified.

In the complex heating systems, higher order, large inertia, large delay and uncertainty typically exist. This work introduces generalized predictive PID control algorithm to adjust the system. The purpose of the control algorithm is to achieve a steady state in a shorter time. On the basis of analyzing PID control algorithm and Generalized Predictive Control (GPC), configuring a given performance function index control-weighted sequences to GPC control. Derived form of generalized predictive control algorithm with PID. The Matlab simulation shows that using generalized predictive PID control algorithm can produce better results in shorter time than simple using PID or GPC in the heating system.

We present the results from our predictive wavefront control algorithm tested using the near-infrared pyramid wavefront sensor on the Keck II adaptive optics (AO) bench. The algorithm aims to minimise the servo-lag error of the AO system. We compare the achieved contrast for a vortex coronagraph for both the predictive control algorithm and the standard integral control law.

In the last decade, robotic systems have penetrated human life more than human can imagine. In particular, the multi-mobile robotic systems have faced a fast growing due to the fact that by deploying a large collection of mobile robots the overall system has a high redundancy and offers the capability of handling more complex tasks. A group of mobile robots increases robustness against failures and provide flexibility to system changes. In order to achieve a fully autonomous operation, the control algorithm to coordinate the robots becomes more important and decisive. The controller has to meet different requirements that sometimes are conflicting. The main goal of this thesis is to evaluate performance of coordination control algorithms for a group of mobile robots. In the first part of this thesis, a modular framework for simulation and experiments of coordination control of mobile robots is presented. All connections between the modules follow the subscriber/publisher paradigm on the exchanged data, i.e. a component publishes data and other components can subscribe on that data. The data is identified by a named magazine, and actual data packets are called issues of the magazine. By means of simulation and experiments, it is demonstrated how the modularity in the framework allows straightforward modification of system configurations, design parameters, and control algorithms. In the second part, hierarchical coordination control of mobile robots is presented. The hierarchy consists of three layers: a high-level control for motion planning of the robots, a low-level control for motion execution, and a flexible layer to accommodate shifting of responsibilities. One advantage of using a hierarchical approach is the isolation of control design in each layer. Changes in the control strategy of a layer do not necessarily require adaptation of other layers. The proposed control algorithms are used to coordinate a group of unicycle mobile robots that realize the transportation system of an automated warehouse. Using the framework, series of simulations with different design parameters are conducted, as well as real-time experiments. Subsequently, performance analysis of the results is carried out. It is shown that the proposed algorithms are flexible to system changes and scalable to variation in the warehouse demands. The algorithms are robust against failures in the transportation systems. It is found that an algorithm, denoted high-level control, with higher throughputs requires more information sharing between the robots. This algorithm is less robust against failures compared to the algorithm, denoted low-level control, that yields lower throughput. In addition, a cost analysis of the proposed control algorithms is given. It is studied that the cost of realizing the transport using a group of mobile robots, coordinated by the proposed control algorithm, has similar performance-to-cost ratio compared to conveyor systems. This conclusion is very promising having in minds that the proposed control algorithm is not necessarily the optimal one. In the third part, the problem of simultaneous tracking of individual references and formation keeping for a group of mobile robots is investigated. The control algorithm is developed using dynamic feedback linearization. The stability proof is analyzed using the theorem on interconnected systems. Using a root-mean-square-like indicator, the trade off between individual tracking and formation keeping, as well as the influences of communication topologies are analyzed. From the analysis of real-time experiment results, it is found that the best formation keeping is obtained when all robots communicate. As a trade off, this requires a larger communication bandwidth and yields large individual tracking errors. Furthermore, the analysis suggests that to achieve optimal individual tracking and formation keeping simultaneously, there is less need to share information between the robots. In the fourth part, as a complement to the hierarchical control approach, a coordination control algorithm based on Model Predictive Control (MPC) is presented. Focusing on the practical aspects, a sequentially decentralized MPC, i.e. a single MPC computes the control signals of all robots where priority rules are used to determine which robots handled earlier in optimization procedure, is implemented. Using the similar automated warehouse environment, the sequentially decentralized MPC is validated in simulations and real-time experiments. For comparison purposes, a centralized MPC, i.e. a single MPC computes the control signals of all robots, is also implemented. Using completion time as indicators, the influences of the MPC parameters are investigated. It is found that the centralized MPC is better than the sequentially decentralizwed MPC, but with the cost of high computation load and limited number of robots in real-time application. Relevance and performance comparison between the MPC and the hierarchical control approach are presented. It is found that regardless of the control algorithm choices, in terms of completion time, a better performance is obtained when the control algorithm uses information from all robots in computing the control signals.

In this paper, we propose three advanced digital predictive current-mode control (DPCC) algorithms based on a simplified estimation method of duty cycle lost time for asymmetric half-bridge (AHB) LED constant-current driver. They are the digital predictive peak current mode control(DPPCC) algorithm based on leading-edge modulation, the digital predictive quasi-valley current mode control(DPqVCC) algorithm based on trailing-edge modulation, and the digital predictive quasi-average current mode control(DPqACC) algorithm based on double-edge modulation. Simulation and experimental results demonstrate that the three DPCC algorithms can effectively offset the effect of delay on digital control performance, which confirms the superiority of DPqACC. When the DPqACC is applied, the series-load-jump transition times are below 4.5 ms, the maximum load adjustment rate is 0.5%/ V, and the output current can be tuned continuously between 0.3 A and 4.5 A with the longest transition time of 1 ms. Moreover, the three DPCC algorithms are capable of compensating for low-frequency ripples due to the rapid regulation speed of the inner loop. With the electrolytic capacitance removed without additional ripple compensation, the maximum peak-to-peak value of the low-frequency secondary ripple is 0.1163 A (2.91%) when the input is 400 53sin(2ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>lin</i></sub> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> ) V and the output is 4 A. This approach provides an excellent solution for the digital design of low-ripple AC-DC LED constant-current drivers without electrolytic capacitance.

ABSTRACTThis paper is mainly concerned with the design problem of two-step model predictive control (MPC) for nonlinear systems represented by Hammerstein model, where the network-induced time delays exist between sensor to controller (S2C) and controller to actuator (C2A) links. We assume that the system state is not measurable, so the state observer is employed to estimate the state. The intermediate variable for the linear part of the system is calculated by minimising the quadratic performance function. The time-delay compensation algorithm of two-step output feedback predictive control (TSOFPC) for Hammerstein systems is presented and validated by a numerical example.

This paper proposes a design method and parameter tuning method for how only using the DCS configuration control software module to combine and achieve generalized predictive control algorithm in a complicated controlled plant case. In order to show the effectiveness of the control algorithm using the DCS layer control software module to achieve model predictive, two types of complex objects are considered as example to do simulation : non-minimum phase process and the complex process with recycling and pure delay. The simulation results show that the proposed control algorithm can provide similar control performance of GPC.

A new framework is proposed to cope with the uncertain time delay of networked control system. Event-clock-driven controller nodes, together with clock-driven sensor nodes and actuator nodes are required in this framework. Queuing Strategy is introduced both in controller nodes and actuator nodes while the time delay between controller node and actuator node is compensated by multi-step control increment given by the algorithm of General Predictive Control. An output error prediction model is built using BP neural network to deal with the time delay between sensor node and controller node. The principle of this model is to revise the predictive output of general predictive control model using predictive error signal; if the value of time delay exceeds the upper limit, controller nodes will immediately produce the control strategies adopting the revised predictive output, and thus the compensation for time delay between sensor nodes and controller nodes would be accomplished. Simulation experiments are practiced over Ethernet network which embraces both kinds of time delay. It is proved that the scheme of complete compensation remains a good control performance.

Extreme UltraViolet (EUV) lithography is a new technology for production of integrated circuits. In EUV lithographic machines, optical elements are heated by absorption of exposure energy. Heating induces thermoelastic deformations of optical elements and consequently, it creates wavefront aberrations. These Thermally Induced Wavefront Aberrations (TIWA) can significantly degrade the resolution of EUV lithographic machines. One of the ways to correct TIWA and consequently, to improve the resolution of EUV lithographic machines, is to use the Adaptive Optics (AO) technique and predictive control algorithms. However, the predictive control of TIWA is a challenging problem, mainly because the dynamical behavior of TIWA is described by thermoelastic Partial Differential Equations (PDEs). By discretizing the thermoelastic equations using the finite difference or finite element methods, large-scale state-space models can be obtained. A striking feature of these state-space models is that they have sparse (multi) banded matrices. Consequently, these state-space models can be interpreted as large-scale networks of local subsystems. All this implies that the problem of correcting TIWA can be placed in a much more general context of identifying, estimating and controlling large-scale interconnected (distributed) systems. However, currently used estimation and control algorithms are not computationally feasible for large-scale systems. For this reason, this thesis focuses on the development of computationally efficient identification, estimation and control algorithms for large-scale interconnected systems. In this thesis, we prove that the inverses of (finite-time) Gramians of large-scale interconnected systems, obtained by discretizing PDEs, belong to a class of off-diagonally decaying matrices. Consequently, these inverses can be approximated by sparse (multi) banded matrices with O(N) complexity, where N is the number of local subsystems. To compute the approximate inverses, the Chebyshev approximation method and the Newton iteration are used. On the basis of these theoretical results, we develop novel estimation and identification algorithms for large-scale interconnected systems. The computational and memory complexity of these methods scale with O(N). This is the main advantage over the traditional estimation and identification methods, which complexity is O(N^3). Furthermore, in this thesis we develop and experimentally verify AO control algorithms for predictive correction of TIWA. The experimental results give a good first indication for a possible implementation of the developed predictive control algorithms in EUV lithographic machines. Finally, we develop and experimentally test an iterative learning control algorithm for high performance correction of wavefront aberrations.