Dynamics Of Control System Research Articles

A Data-Driven Dual-Rate Control Method for a Heat Exchanging Process

The heat exchanging process is a strong nonlinear and cascade industrial unit, which is effected by the large random disturbances in the steam pressure, the outdoor temperature, and the water discharged by users. To solve this problem, in this paper, a PI controller with unmodeled dynamic compensation is combined with a dual-rate control, and a dynamic model of the supply water temperature in the outer loop with linear model and unknown higher order nonlinear is established by introducing the dynamics of the closed-loop control system for the steam flow-rate with a lifting method. This is then used in designing a one-step optimal PI control algorithm with unmodeled dynamics compensation. Second, a data-driven dual-rate control method is proposed for the control of water temperature and steam flow-rate. The stability and convergence of the proposed algorithm is analyzed. Finally, both, a contrast simulated experiment with the interval intelligent cascade control algorithm and an industrial application are included to demonstrate that the proposed method can control water temperature and steam flow-rate with large random disturbances within the target range of values for process requirements.

Virtual prototyping of embedded control software in mechatronic systems: A case study

The large majority of technological devices can be seen as the sum of components of heterogeneous nature (electrical, mechanical, thermal, software, etc.) so that their analysis and design calls for the use of tools from different engineering disciplines. Integration among the different tools is particularly relevant at control system design level, since it is at this stage that it is required to analyze the behavior of both each single component and the system as a whole, with different levels of detail. In this paper mechatronic systems are considered, that exhibit a strong interplay between mechanical and electrical components, and the issue of modeling and designing embedded control algorithms and architectures for such systems is addressed. In particular, an integrated virtual prototyping approach for analyzing the system behavior down to embedded software level is proposed, that can be used in a wide number of situations that can represent the actual real-world operational conditions of consumer products. This approach can be used for system design (in particular at control systems level), embedded software design, and virtual testing so as to optimize and reduce the costs of late stage software development, physical prototypes, and their testing. The case study is based on some recently derived software tools that perform the co-simulation of the firmware execution and the multi-physical controlled system dynamics. The actual control software implemented in the final product can be entirely developed and tested inside the virtual prototype. To prove the validity and potentialities of the proposed approach, a real case study is presented, regarding a very common, though complex to simulate, mechatronic system such as an electric sliding gate. It turns out that the proposed environment goes beyond hardware-in-the-loop tools, since it does not require the use of specific hardware (the hardware itself is simulated in detail) but allows to analyze in detail the status of the microprocessors and peripherals at arbitrary time-scales and allows the designer to study at the earliest design stages the dynamics between the multi-physical controlled system and the firmware, without committing to a given hardware structure.

Pattern-Based NN Control of a Class of Uncertain Nonlinear Systems.

This paper presents a pattern-based neural network (NN) control approach for a class of uncertain nonlinear systems. The approach consists of two phases of identification and another two phases of recognition and control. First, in the phase (i) of identification, adaptive NN controllers are designed to achieve closed-loop stability and tracking performance of nonlinear systems for different control situations, and the corresponding closed-loop control system dynamics are identified via deterministic learning. The identified control system dynamics are stored in constant radial basis function (RBF) NNs, and a set of constant NN controllers are constructed by using the obtained constant RBF networks. Second, in the phase (ii) of identification, when the plant is operated under different or abnormal conditions, the system dynamics under normal control are identified via deterministic learning. A bank of dynamical estimators is constructed for all the abnormal conditions and the learned knowledge is embedded in the estimators. Third, in the phase of recognition, when one identified control situation recurs, by using the constructed estimators, the recurred control situation will be rapidly recognized. Finally, in the phase of pattern-based control, based on the rapid recognition, the constant NN controller corresponding to the current control situation is selected, and both closed-loop stability and improved control performance can be achieved. The results presented show that the pattern-based control realizes a humanlike control process, and will provide a new framework for fast decision and control in dynamic environments. A simulation example is included to demonstrate the effectiveness of the approach.

Research of the combined extremum-seeking control system dynamics on mathematical and computer models

The article introduces the process of studying the dynamics of the combined extremum-seeking control system, which is used for grinding-mixing unit automatic balancing. The structure of the proposed system is shown and the algorithm of its operation is described. On the basis of the harmonic balance and Goldfarb’s methods, the values of quality factors of the extremum-seeking control system on the first cycle were obtained in the absence of static map drift and the stability of the system was established. The co-simulation model of the combined extremum-seeking control system implemented in the MATLAB / Simulink software is presented and described. MSC.Adams model of the grinding-mixing unit, which is a plant in the co-simulation model of the system, is presented. The results of co-simulation of the extremum-seeking control system dynamics are presented and analysed.

Identification and control of an oxygen provider system for a biological capsule

AbstractProviding oxygen is a necessary process in manned missions to space. The aim of this paper is to identify the dynamics of an open loop control system in preparation for feature missions. A linear time invariant model is identified with data gathered from pre-flight tests of Kavoshgar, the Iranian bio-capsule. Numerical evaluation of the resulting model and its validation are given. The resulting time discrete model is used to drive the optimal setting that minimizes power consumption of the system.

The extension and exploitation of the inventory and order based production control system archetype from 1982 to 2015

In 1994, through classic control theory, John, Naim and Towill developed the ‘Automatic Pipeline, Inventory and Order-based Production Control System’ (APIOBPCS) which extended the original IOBPCS archetype developed by Towill in 1982 ─ well-recognised as a base framework for a production planning and control system. Due to the prevalence of the two original models in the last three decades in the academic and industrial communities, this paper aims to systematically review how the IOBPCS archetypes have been adopted, exploited and adapted to study the dynamics of individual production planning and control systems and whole supply chains. Using various databases such as Scopus, Web of Science, Google Scholar (113 papers), we found that the IOBPCS archetypes have been studied regarding the a) modification of four inherent policies related to forecasting, inventory, lead-time and pipeline to create a ‘family’ of models, b) adoption of the IOBPCS ‘family’ to reduce supply chain dynamics, and in particular bullwhip, c) extension of the IOBPCS family to represent different supply chain scenarios such as order-book based production control and closed-loop processes. Simulation is the most popular method adopted by researchers and the number of works based on discrete time based methods is greater than those utilising continuous time approaches. Most studies are conceptual with limited practical applications described. Future research needs to focus on cost, flexibility and sustainability in the context of supply chain dynamics and, although there are a few existing studies, more analytical approaches are required to gain robust insights into the influence of nonlinear elements on supply chain behaviour. Also, empirical exploitation of the existing models is recommended.

Performance bounds for the mean-field limit of constrained dynamics

In this work we are interested in the mean-field formulation of kinetic models under control actions where the control is formulated through a model predictive control strategy (MPC) with varying horizon. The relation between the (usually hard to compute) optimal control and the MPC approach is investigated theoretically in the mean-field limit. We establish a computable and provable bound on the difference in the cost functional for MPC controlled and optimal controlled system dynamics in the mean-field limit. The result of the present work extends previous findings for systems of ordinary differential equations. Numerical results in the mean-field setting are given.

Introduction of a New Numerical Simulation Tool to Analyze Micro Gas Turbine Cycle Dynamics

Micro gas turbine (MGT) technology is evolving toward a large variety of novel applications, such as weak gas electrification, inverted Brayton cycles, and fuel cell hybrid cycles; however, many of these systems show very different dynamic behaviors compared to conventional MGTs. In addition, some applications impose more stringent requirements on transient maneuvers, e.g., to limit temperature and pressure gradients in a fuel cell hybrid cycle. Besides providing operational safety, optimizing system dynamics to meet the variable power demand of modern energy markets is also of increasing significance. Numerical cycle simulation programs are crucial tools to analyze these dynamics without endangering the machines, and to meet the challenges of automatic control design. For these tasks, complete cycle simulations of transient maneuvers lasting several minutes need to be calculated. Moreover, sensitivity analysis and optimization of dynamic properties like automatic control systems require many simulation runs. To perform these calculations in an acceptable timeframe, simplified component models based on lumped volume or one-dimensional discretization schemes are necessary. The accuracy of these models can be further improved by parameter identification, as most novel applications are modifications of well-known MGT systems and rely on proven, characterized components. This paper introduces a modular in-house simulation tool written in fortran to simulate the dynamic behavior of conventional and novel gas turbine cycles. Thermodynamics, gas composition, heat transfer to the casing and surroundings, shaft rotation and control system dynamics as well as mass and heat storage are simulated together to account for their interactions. While the presented models preserve a high level of detail, they also enable calculation speeds up to five times faster than real-time. The simulation tool is explained in detail, including a description of all component models, coupling of the elements and the ODE solver. Finally, validation results of the simulator based on measurement data from the DLR Turbec T100 recuperated MGT test rig are presented, including cold start-up and shutdown maneuvers.

Dynamic equivalent model of VSC based on singular perturbation

Scalable and computationally efficient model of voltage source converter (VSC) that accurately captures the dynamics is key to the modelling, analysis and control of VSC–high-voltage direct current system. In this study, a dynamic equivalent model of VSC including the dynamics of control system is obtained by using singular perturbation method for the first time. The reduced second-order model with simple form and good generality captures the steady-state and transient behaviours of original full model with a high degree of accuracy. This equivalent model retains the physical meaning of the variables, accommodates different control modes and significantly reduces the computational burden compared with the original full model. Singular perturbation method itself contains some systematic procedures to be able to improve the accuracy of the dynamic behaviour of the lower-order model. The ability of the dynamic equivalent model to accurately describe the original dynamics of the system has been verified with numerical simulations in normal operation and on fault case associated with MTDC grids.

A System for the Comprehensive Quantification of Real-Time Heartbeat Activity

Heartbeat can reflect the dynamics of the heart control system, and it is also a commonly used index in health monitoring, exercise load calculation and psycho-physiological arousal quantification. This paper fuses three heartbeat measures, i.e. the running mean, the range of local Hurst exponents and the relative fluctuation, to construct a system that can automatically quantify the heartbeat activity both from its static aspect and from its dynamic aspect in a real-time manner. Experiments show that the system can reveal the heartbeat arousal difference between physically relaxed status and exercise-loaded status. When the affective heartbeat data in literature are quantified by this system, the results also show the capability of the system to illustrate psycho-physiological arousal.

Quantized Iterative Learning Control Design For Linear Systems Based On A 2‐D Roesser Model

AbstractThis paper considers the problem of iterative learning control design for linear systems with data quantization. It is assumed that the control input update signals are quantized before they are transmitted to the iterative learning controller. A logarithmic quantizer is used to decode the signal with a number of quantization levels. Then, a 2‐D Roesser model is established to describe the entire dynamics of the iterative learning control (ILC) system. By using the sector bound method, a sufficient asymptotic stability condition for such a 2‐D system is established and then the ILC design is given simultaneously. The result is also extended to more general cases where the system matrices contain uncertain parameters. The effectiveness of the proposed method is illustrated by a numerical example.

A Novel Dynamic Co-Simulation Analysis for Overall Closed Loop Operation Control of a Large Wind Turbine

A novel dynamic co-simulation methodology of overall wind turbine systems is presented. This methodology combines aerodynamics, mechanism dynamics, control system dynamics, and subsystems dynamics. Aerodynamics and turbine properties were modeled in FAST (Fatigue, Aerodynamic, Structures, and Turbulence), and ADAMS (Automatic Dynamic Analysis of Mechanical Systems) performed the mechanism dynamics; control system dynamics and subsystem dynamics such as generator, pitch control system, and yaw control system were modeled and built in MATLAB/SIMULINK. Thus, this comprehensive integration of methodology expands both the flexibility and controllability of wind turbines. The dynamic variations of blades, rotor dynamic response, and tower vibration can be performed under different inputs of wind profile, and the control strategies can be verified in the different closed loop simulation. Besides, the dynamic simulation results are compared with the measuring results of SCADA (Supervisory Control and Data Acquisition) of a 2 MW wind turbine for ensuring the novel dynamic co-simulation methodology.

An eco-epidemic pest-natural enemy SI model in two patchy habitat with impulsive effect

In this paper, the dynamics of a pest-natural enemy model with impulsive diffusion of pest in two patchy habitat is studied by considering the disease in pest population with two classes: susceptible, infectious and the disease is transmitted from infected pest to susceptible pest and does not propagate to the natural enemy. To reduce the pest at a desired level, the infected pest and natural enemy are impulsively released in both the patches. In the analysis, it is obtained that the susceptible pest free periodic solution is locally asymptotically stable by exploiting the Floquet theory of impulsive differential equation and small amplitude perturbation skills. It is also globally asymptotically stable using comparison techniques of impulsive differential equations. The study shows that the infection incidence rates and impulsive techniques play an important role on the dynamics of pest control system. Moreover, a short impulse period and large releasing amount of infected pest and natural enemy are cause the extinction of pest population. Finally, numerical simulations are carried out to validate our theoretical findings.

Estimates of reachable sets of control systems with nonlinearity and parametric perturbations

We consider estimation techniques for trajectory tubes of a nonlinear control system with uncertainty in the initial data and under the assumption of quadratic nonlinearity of the velocity vectors with respect to the states of the system. It is assumed that the uncertain initial states and admissible controls are subject to ellipsoidal constraints. We study problems of sensitivity of reachable sets and of their ellipsoidal estimates to a finite-dimensional parameter appearing in the constraints and in the dynamics of the uncertain control system. The results are based on algorithms and techniques of the theory of ellipsoidal estimation and the theory of differential inclusions.

Design of switching hyperplanes for multi‐inputs multi‐outputs discrete‐time linear systems

The design of sliding switching surfaces consist in finding a manifold where the controlled system dynamics are asymptotically stable. This problem has been studied for a long time, during which a wide range of designing approaches has been proposed, which can be classified into two research methodologies; the first one corresponding to eigenvalues designing approaches and the second one is based on optimal approaches. In addition, some research approaches only can be applied to specific type of systems, e.g. single-input single-output systems or continuous time systems. Finally, the performance or the level of the achieved accuracy among the different approaches varies significantly. The proposed approach facilitates the task of designing sliding surfaces applied to multi-input multi-output (MIMO) linear systems. The presented methodology introduces a switching surface for MIMO linear systems through a simple equation that allows both eigenvalues placement design or optimal methods without any coordinate system representation transformation. In addition, a design parameter is also included for the adjustment of the non-ideal dynamics. Moreover, this approach guarantees high accuracy of the system dynamics, either for arbitrary eigenvalues assignment or for optimal switching surface definitions. In addition, these benefits are attained using a single switching surface equation. An illustrative example is included to show the advantages of the proposed design methodology.

Fuzzy logic control for vehicle stability control system with virtual prototype and experimental research

Electronic stability program (ESP) is an active safety system, which can keep a vehicle stable by traction or braking in extreme conditions and improve handling stability and driving safety significantly. In this paper, the vehicle subsystem key parameters were tested or identified through experimental means firstly, such as hydraulic power steering system, brake systems, shock absorbers and suspension hard points, wheels and tyres, engine and other components or subsystems parameters. And then the vehicle virtual prototype was built in multi-body dynamics software Adams/Car. According to the basic principles of vehicle stability control, the vehicle stability fuzzy-PID controller was designed in MATLAB/Simulink, and a combined simulation platform of control system and vehicle dynamics virtual prototype model was established. The model accuracy was verified by comparison of actual vehicle experiment and simulation under typical handling and stability control conditions. The results show that the ESP fuzzy-PID controller can effectively control the sideslip angle and yaw rate, and the vehicle trajectory following performance is satisfied. Finally, a winter field experiment was carried out, including double lane on snow roads and J-Turn on ice road. At the same time the original vehicle control algorithms are analysed.

A Study of the stability of solutions to inverse problems of dynamics of control systems under perturbations of initial data

For systems linear in control, we consider problems of recovering the dynamics and control from a posteriori statistics of trajectory sampling and known estimates for the sampling error. An optimal control problem of minimizing an integral regularized functional of dynamics and statistics residuals is introduced. Optimal synthesis is used to construct controls and trajectories that approximate a solution of the inverse problem. A numerical approximation method based on the method of characteristics for the Hamilton–Jacobi–Bellman equation and on the notion of minimax/viscosity solution is developed. Sufficient conditions are obtained under which the proposed approximations converge to a normal solution of the inverse problem under a matched vanishing of the approximation parameters (bounds for the sampling error, the regularizing parameter, the grid step in the state variable, and the integration step). Results of the numerical solution of identification and recovery problems for controls and trajectories are presented for a mechanical model of gravitation under given statistics of phase coordinate sampling.

On the solution of inverse problems of dynamics of linearly controlled systems by the negative discrepancy method

The paper is devoted to the substantiation of the negative discrepancy method for solving inverse problems of the dynamics of deterministic control systems that are nonlinear in state variables and linear in control. The problem statements include the known sampling history of trajectories measured inaccurately, with known error estimates. The investigation is based on the Pontryagin maximum principle. The results of simulation of an inverse problem for a macroeconomic model are presented.

Design of Robust Fractional PID Controller Using Triangular Strip Operational Matrices

Abstract The present article proposes a simple tuning technique aimed to produce a robust non-integer order PID controller exhibiting iso-damping property during reparameterization of a plant. The required robustness property is achieved by letting the fractional PID control system to imitate the dynamics of a reference system having Bode’s ideal transfer function in its forward path. The objective of designing robust controller by tracking the dynamics of reference control system is defined mathematically as an H∞- optimal control problem. Fractional differential systems are transformed into algebraic equations by the use of triangular strip operational matrices. The H∞-optimal control problem is then changed to an ∞-norm minimization of a parameter (Kc,KI,Kd, λ, μ) varying square matrix. Global optimization techniques, Luus-Jaakola direct search and particle swarm optimization are employed to find the optimum values of fractional PID controller parameters. The proposed method of control system design is implemented in heating furnace temperature control, automatic voltage regulator system and some integer and fractional order process models. Fractional PI, fractional PD and various versions of fractional PID controllers are designed as optimal controllers using the triangular strip operational matrix based control design method.

Bilateral Control of a Velocity Control System Using Electric and Hydraulic Actuators

SUMMARYWorking in extreme environments (e.g., at disaster sites) is dangerous for humans. Hence, it is important to use teleoperated robots in such extreme environments. Because heavy lifting work is involved, hydraulic actuators with high output are more commonly used than electric actuators. Therefore, it is necessary to operate remotely controlled robots driven by hydraulic actuators. Bilateral control is remote control with a sense of force. Because most bilateral control systems have been studied mainly using electric actuators, bilateral control using hydraulic actuators is still a challenge. In this study, we propose placing a bilateral controller between an electric actuator on the master side and a hydraulic actuator on the slave side using position and velocity. The controller exhibits the dynamics of bilateral control systems using position and velocity. In addition, the control performance of conventional methods and that of the proposed method are compared and experimentally verified.