The Design and Simulation of Predictive Control Algorithm Achieved by the DCS Algorithm Module Combination

The predictive inelastic state control (PISC) algorithm is proposed by incorporating an elastic-based predictive state control algorithm with inelastic structural analysis. Force analogy method based on initial stiffness is used in the analysis to simplify the numerical computation. This proposed inelastic control algorithm compensates for the time delay that occurs in practical application of structural control by predicting the inelastic structural response over a period equal to the time delay, and substituting the predicted structural response in the expression of an ideal control force without considering time delays. A one-storey moment-resisting frame with different inelastic characteristics is analyzed using this algorithm. Numerically simulated results show that the proposed control algorithm is feasible for various types of inelastic structures. While the control efficiency deteriorates with increase in time delay magnitude, PISC shows good performance within a certain range of time delays. More importantly, it is found that a control system with smaller time delay magnitude does not guarantee better performance than that with larger time delay magnitude. Finally, numerical simulation on a six-storey moment-resisting frame is performed to demonstrate that PISC gives good control results in a practical inelastic structure. Copyright © 2004 John Wiley & Sons, Ltd.

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.

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.

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.

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%.

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.

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.

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.

This paper proposes a novel control algorithm to enhance the fault ride-through (FRT) capability of a photovoltaic (PV) system. In this method, the overcurrent of the grid-tied inverter is suppressed to a preset value using model current predictive control (MCPC) algorithm, and its DC-link overvoltage is removed using a non-maximum power point tracker (non-MPPT) algorithm. Therefore, the inverter overcurrent and its DC-link overvoltage problems can be placed under decoupling control using a DC/AC converter controller and DC/DC converter controller, respectively. Using eight switching modes for the three-phase inverter, the MCPC of DC/AC converter controller establishes a value function between the current reference value and output current value. Then, by introducing the switching mode (corresponding to the minimum value function) into the DC/AC converter controller, if the current reference is set at the rated value, the inverter’s output current can be inhibited to the rated value under fault conditions. Moreover, a balanced three-phase rated current can always be obtained under either a symmetric or asymmetric fault. However, together with the MCPC, a DC-link overvoltage will appear. To remove this DC-link overvoltage quickly, based on the grid system voltage sag level, the non-MPPT algorithm is used to calculate the adjusted power for PV arrays. Then, based on the calculated power, the amended duty cycle can be obtained and immediately introduced into the DC/DC converter controller to tune the PV arrays’ output power, which significantly decreases the power imbalance between the inverter’s AC and DC sides, thus inhibiting the DC-link overvoltage. In addition, to inhibit DC-link voltage fluctuations further, DC-link voltage feedforward compensation is introduced into the DC/DC converter controller. In particular, under an asymmetric fault condition, with the help of the voltage feedforward compensation, the second harmonic frequency components of the DC-link voltage can also be removed. Finally, based on the theoretical derivation and simulation results, it can be proved that the major problems troubling a PV system’s FRT technologies can be resolved by the proposed method, especially the problem related to the harmonic components of the DC-link voltage.

This study investigates control strategies for coordinating the variable solar-optical properties of a dynamic building envelope system with a daylight controlled electric lighting system to reduce electricity consumption and increase comfort in the perimeter zone of commercial buildings. Control strategy design can be based on either simple, instantaneous measured data, or on complex, predictive algorithms that estimate the energy consumption for a selected operating state of the dynamic envelope and lighting system. The potential benefits of optimizing the operation of a dynamic envelope and lighting system are (1) significant reductions in electrical energy end-uses - lighting, and cooling due to solar and lighting heat gains - over that achieved by conventional static envelope and lighting systems, (2) significant reductions in peak demand, and (3) increased occupant visual and thermal comfort. The DOE-2 building energy simulation program was used to model two dynamic envelope and lighting systems, an automated venetian blind and an electrochromic glazing system, and their control strategies under a range of building conditions. The energy performance of simple control strategies are compared to the optimum performance of a theoretical envelope and lighting system to determine the maximum potential benefit of using more complex, predictive control algorithms. Results indicate that (1) predictive control algorithms may significantly increase the energy-efficiency of systems with non-optimal solar-optical properties such as the automated venetian blind, and (2) simpler, non-predictive control strategies may suffice for more advanced envelope systems 1 incorporating spectrally selective, narrow-band electrochromic coatings.

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.

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.

In this paper, the unmanned vehicle trajectory tracking problems were studied. In order to improve the stability of unmanned vehicles, the trajectory tracking control algorithm based on model predictive control (MPC) algorithm was researched by adding the dynamic constraints of centroid, acceleration and tire side angle on the basis of vehicle dynamics model. The trajectory planning layer was established on top of the trajectory tracking control layer, and the Carsim/Simulink simulation platform was built. At different speeds and under different road adhesion conditions, the algorithm was verified and compared. The simulation results showed that the model predictive control algorithm with dynamic constraints was provided with excellent tracking performance under severe conditions and well adaptive to environment interference.

Dynamic matrix control algorithm is a predictive control algorithm based on step response of the object, suitable for complex control systems and the process whose mathematical model is difficult to be established precisely. In the article, the dynamic matrix control algorithm has been studied, we analyse the impact on system control quality using dynamic matrix control algorithms with different setting parameters, and make simulation in the thermal power plants control process. The simulation results show that dynamic matrix predictive control algorithm can achieve good control effect in the process.

To explore the addressed issue of closed-loop vibration control using semi-active magneto-rheological (MR) fluid damper, an innovative control algorithm based on feedback of acceleration response at several points along stay cable for active/semi-active control of stay cables is firstly proposed in the present paper. Theoretical analysis, laboratory investigation, and in-situ tests are conducted in this study to verify the proposed control algorithm. Moreover, this paper presents the negative stiffness characteristic of the combined stay cable/semi-active MR damper system. These investigations indicate that the semi-active MR damper can achieve much better mitigation efficacy than the other passive MR dampers and the proposed control algorithm is proved to be practical. The effect of the negative stiffness on response reduction is demonstrated by the improved energy dissipation ability of the damper due to the enhanced displacement of the stay cable at the location attached with the damper. Introduction In the past few decades, many control methods have been proposed and some have been implemented to mitigate the unaccepted stay cable vibration (Johnson, 2003). At present, semi-active dampers have been proven to be effective in many applications and can potentially achieve performance levels that are almost the same as comparable active devices. For stay cables vibration control incorporated with semi-active dampers, most of the existing control strategies are based on full-state feedback of generalized coordinates. However, the generalized displacements and velocities of the stay cable cannot be measured directly. Since accelerometers can readily provide reliable and inexpensive measurements of the stay cable accelerations at strategic points on a stay cable, development of control method based on acceleration feedback at limited locations along stay cable is an ideal solution to solve the addressed practical problem. Moreover, even though the characteristic of negative stiffness of active control of stay cable vibration has been observed by some researchers(Johnson, 2003), the effects of negative stiffness on the stay cable vibration control using active/semi-active dampers 2 has not been fully researched in the present literatures. In this paper, a control algorithm based on feedback of acceleration response at several points for active and semi-active control of mass-distributed dynamic systems, e.g. stay cables is firstly proposed. Using the proposed control algorithm, the vibration control for the stay cable model attached with one MR damper and a series of field tests are carried out to investigate the control efficacy achieved by different control strategies. These investigations address that the semi-active MR damper can achieve much better mitigation efficacy than other passive MR dampers and the proposed control algorithm is proved to be practicable and available. Moreover, the effect of the negative stiffness provided by semi-active MR dampers on response reduction of cables is theoretically and numerically demonstrated by the improved energy dissipation ability of the damper due to the enhanced displacement of the cable at the location attached with the damper. Control Algorithm Based on Acceleration Feedback at Several Locations Governing equation of the combined stay cable/MR damper system shown in Figure 1 is given as (Irvine, 1981) ) ( ) ( ) , ( ) , ( ) , ( ) , ( d s x x t u t x f t x v c t x v T t x v m − + = + ′ ′ − δ & & & (1) with the boundary conditions v(0,t)=v(l,t)=0 for all t .where v(x,t) is the transverse deflection of the cable, l is the length of the cable, T is the cable tension, m and c are the cable mass and viscous damping per unit length, respectively, xd is the distance of the installed MR damper position from the left end, us(t) is the transverse control force provided by the MR damper at location x= xd, f(x,t) is the distributed load along the cable. δ(.) is the Dirac delta function. Figure1. Combined stay cable/MR damper system The transverse deflection could be approximated using a finite series ) ( ) ( ) , ( ∑ = = r j j j x t q t x v 1 φ (2) where the φj(x) is a set of shape modal functions and continuous with piecewise continuous slope to satisfy the geometric boundary conditionsφj(0)= φj(l)=0. Utilizing the approximate series solution and a standard Galerkin approach, the motion equation of the cable-MR damper system in matrix form is written as ) ( ) ( f Kq q C q M q t u x s d φ + = + + & & & (3)