IFAC Journal of Systems and Control最新文献

This paper discusses about a data-driven control method which is called Estimated Response Iterative Tuning (ERIT). ERIT focuses on a two-degree of freedom control system, and updates the feedforward controller from one-shot experiment. The main contribution of this paper is to propose a pre-processing for ERIT to improve robustness against noise. In particular, this paper proposes to project the measured output onto a subspace, and regard the projected signal as a noise-free output. How to design this subspace is also proposed in this paper. A practical experiment with flexible link system is shown to demonstrate the effectiveness of the proposed method.

Analysis of control degrees of freedom (CDOF) is a main step in control structure design. The CDOF in a process flow diagram (PFD) is the maximum number of manipulated material or energy streams. In this study, a CDOF analyzer is developed within Aspen Hysys simulation software by writing the scripts for enumeration of structural PFD parts i.e. boundary streams, recycle/bypass loops, circuits, holdup vessels and the engaged fluid phases in holdup vessels. The scripts are written in WinWrap Basic v10, the embedded language in Aspen Hysys. The function for identification of recycle/bypass loops is adopted from an algorithm for enumeration of chordless cycles in graph theory. Finally, the generality, reliability and ease of application of the developed analyzer are examined for analysis of several PFDs including simple units as well as the large-scale plants.

The optimal control of a water reservoir system represents a challenging problem, due to uncertain hydrologic inputs and the need to adapt to changing environment and varying control objectives. In this work, we propose a real-time learning-based control strategy based on a hierarchical predictive control architecture. Two control loops are implemented: the inner loop is aimed to make the overall dynamics similar to an assigned linear model through data-driven control design, then the outer economic model-predictive controller compensates for model mismatches, enforces suitable constraints, and boosts the tracking performance. The effectiveness of the proposed approach is illustrated on an accurate simulator of the Hoa Binh reservoir in Vietnam. Results show that the proposed approach outperforms stochastic dynamic programming.

This paper presents a method of co-design of models and observers for buoyancy-driven turbulent flows. Recent work on data-driven techniques for estimating turbulent flows typically involve obtaining a dynamical model using Dynamical Mode Decomposition (DMD) and using the model to design estimators. Unfortunately, such a sequential design could result in state-space models that do not possess control-theoretic properties (such as detectability) that ensure guaranteed performance of the observer. In this paper, we propose semi-definite programs (SDPs) that allow us to simultaneously construct observer gains, along with DMD models which exhibit desired properties. Since DMD models for turbulent flows are typically high-dimensional, we provide a tractable algorithm for solving the high-dimensional SDP. We demonstrate the potential of our proposed approach on an industrial application using real-world data, and illustrate that the co-design significantly outperforms sequential design.

This paper revisits the problem of stability analysis of Phase-Locked Loops (PLLs), focusing specifically on stability conditions derived using the Popov Stability Criterion. A new form of the Popov based frequency domain criterion is derived in which the size of the stability region, the PLL locking range, appears independently of the loop transfer-function. This enables one to maximize the stability region graphically and directly on the Popov plot, rather than iteratively. Various numerical and analytic results available in the literature are shown to be particular cases of the proposed new stability test. It is also shown that for PLLs of type $r$, in which $r$ denotes the number of integrators in the loop, it is not possible to achieve full locking range if $r$ is larger or equal than three.

Due to the increase in volatile power generation facilities, the need for flexible modeling options of an energy network is growing. One approach consists of a cellular architecture whose hierarchy levels are less pronounced. Such an architecture is provided by the Loop Circle Arc theory (LoCA theory). Each cell consists of essentially uniform basic building blocks, such as a storage unit, an energy converter, and a source and load, as well as an interface to the next cell. Based on this theory, a model of $N$ households connected to a Circle is created. In order to report the demand of the connected households to the next cell, the Arc, via the interface, it is necessary to know the summed power values. Since the households generally represent stochastic processes, the densities associated with the households are estimated under the assumption of measured consumption values over a 24-hour period. Using the EM-Algorithm, mixed distribution densities are estimated based on normal distribution densities for each household and superimposed accordingly. In this way, in addition to the expected total power consumption, a variance can be given at the same time. This allows not only an estimation of the energy to be made available at certain times. It is also possible to simplify the network, since the $N$ households can be approximated by the time evolution of the expected overall power consumption values.

Attitude stabilization is a necessary function for a shipboard Marine Satellite Tracking Antenna (MSTA), which is responsible for making the antenna dish track the geostationary satellite in the presence of severe environment. A control scheme based on Model Predictive Control (MPC) is proposed to stabilize the antenna dish of an MSTA, and the detailed design process from algorithm design to Hardware-in-the-loop (HIL) simulation is explained. Due to the use of stepper motor as the actuator, the MPC is proposed to deal with the speed acceleration and deceleration problems of stepper motors, which can result in the optimal velocity profile. The MPC algorithm is implemented in Field Programmable Gate Array (FPGA) with three different data types. In addition to using traditional floating-point and fixed-point data types to represent values in MPC, a special data type, half-precision floating-point, is also explored for the first time. The comparison results are presented and analyzed in terms of FPGA resource usage and algorithm execution time. The performance of the proposed control scheme is validated in HIL simulation, which is creatively implemented in a low-cost System On Chip (SOC) FPGA. The HIL simulation results demonstrate that the proposed control scheme in fixed-point MPC can satisfy the requirements of the MSTA.

Time varying dynamic systems whose underlying changing distribution should be continuously monitored to track abnormal behaviors are one of the most recent challenges in many practical applications. When the data arrive in a continuous way, the target concept to be monitored may change accordingly causing a problem of concept drift. Thus, distinguishing between true alarms and changes due to the nonstationarity of the loading data is required. Traditional control charts cannot handle such processes since they do not use a change dependent procedure and they are not designed for concept drift problems. This article proposes the first two-stage time adjusting control chart for monitoring data stream processes with concept drift. Stage I updates the control limits each time an adjustment condition is satisfied based on an incremental linear combination of the historical and the new data. Stage II validates the shift detected in Stage I by testing whether the two subsamples around the drift belong to the same distribution. Experiments based on several drift situations and using different performance measures show that the proposed adaptive chart is more robust than the most recent competitive time varying charts existing in the literature. Moreover, it is more efficient in terms of shift recognition by reducing the false negative detections in several cases.