Accurate Plant Model Research Articles

Advanced fault detection and diagnosis in spacecraft attitude control systems: Current state and challenges

A review of advanced fault detection and diagnosis (FDD) techniques in attitude control systems (ACSs) of spacecraft is presented. In the first part of the paper, several types of ACS failure scenarios with their practical solutions are presented. Next, the existing approaches to FDD are considered and classified based on different criteria, including applications and design techniques. The literature of this part showed that to enhance ACS operational safety, predictability of failure of an ACS and/or of its components as well as reducing the possibility of failure occurrence is imperative. In addition, fast FDD of various kinds of failures is necessary to guarantee the required reliability of an ACS. The second part of this study highlights challenges involved with different FDD approaches, emphasizing their practical applicability. Current research gaps in FDD techniques such as insensitive residual signal, process monitoring methods, accurate plant model design, easy-to-use software development, FDD tuning process, dealing with noisy sensor measurements, time taken for fault management, the sensitivity of FDD system to faults, and FDD robustness are further elaborated on. Subsequently, the state-of-the-art FDD and its future needs are reflected on. The results of this study could direct spacecraft manufacturers and ACS providers to focus on future needs and improve ground testing for enhanced operational reliability and redundancy.

Closed-loop identification of multi-axis active magnetic bearing systems based on decentralized and decoupling control

In response to the open-loop instability of magnetic bearing systems, this paper develops a closed-loop identification method to obtain an accurate plant model for controller design. The identification method is based on a decentralized and decoupling control architecture, which allows the decoupling of the multi-input-multi-output (MIMO) system into two single-input-single-output (SISO) systems for simplifying the identification process. The identification uses a pseudo-random binary sequence as the excitation signal. A parameterized model is obtained through a parameter estimation algorithm based on the output error model. To ensure that the model prediction error can converge during the computation process, a filter is incorporated into the estimation algorithm. The filter can be synthesized systematically by solving a set of linear matrix inequalities. The performance of the proposed closed-loop identification method is firstly verified by simulations. Then it is implemented on a five-axis magnetic bearing platform and the parameterized model obtained from the identification is compared to the experimental frequency response.

Research on DC Line Protection Method of Large PV Plant Based on Transient Direction

Due to the complex fault transients associated with DC convergence in photovoltaic (PV) plants and the large number of power electronics used in systems, the protection device is unable to determine the location of faults, and thus the protection is inadequate. This paper proposes a protection scheme for the DC system of a large PV plant. The accurate model of the PV plant is built in consideration of the DC system, including the current limiter. The differential voltage and current are utilized to realize the location of the fault section. The voltage transients are used to locate the fault within the section accurately. Study cases in PSCAD/EMTDC show the proposed protection scheme is effective and practically useful for PV plants.

New directions in weed management and research using 3D imaging

AbstractRecent innovations in 3D imaging technology have created unprecedented potential for better understanding weed responses to management tactics. Although traditional 2D imaging methods for mapping weed populations can be limited in the field by factors such as shadows and tissue overlap, 3D imaging mitigates these challenges by using depth data to create accurate plant models. Three-dimensional imaging can be used to generate spatiotemporal maps of weed populations in the field and target weeds for site-specific weed management, including automated precision weed control. This technology will also help growers monitor cover crop performance for weed suppression and detect late-season weed escapes for timely control, thereby reducing seedbank persistence and slowing the evolution of herbicide resistance. In addition to its many applications in weed management, 3D imaging offers weed researchers new tools for understanding spatial and temporal heterogeneity in weed responses to integrated weed management tactics, including weed–crop competition and weed community dynamics. This technology will provide simple and low-cost tools for growers and researchers alike to better understand weed responses in diverse agronomic contexts, which will aid in reducing herbicide use, mitigating herbicide-resistance evolution, and improving environmental health.

Research on the Desired Dynamic Selection of a Reference Model-Based PID Controller: A Case Study on a High-Pressure Heater in a 600 MW Power Plant

Nowadays, the proportional–integral–derivative (PID) controller dominates industrial process control. Because of the compromise between parameters, its tuning is still a challenge for practitioners. A reference model (RM)-based PID controller—the desired dynamic equational (DDE) PID controller—is regarded as a viable alternative since it can readily eliminate the compromise. However, how to design its desired dynamic equation remains an unsolved problem which limits the application of DDE PID controllers in large-scale industrial systems. Therefore, this paper studies the desired dynamic selection of DDE PID controllers and proposes a simple and practical selection procedure without using the accurate plant model. Simulations, experiments and filed tests demonstrate the convenience and advantages of the proposed method, thus making DDE PID an effective controller type which is specifically appealing to engineers. Moreover, the successful application of DDE PID controllers to a high-pressure (HP) heater in a coal-fired power plant shows their promising prospects in the future power industry with the increasing demand to integrate more renewables into the grid.

Frequency-Domain Data-Driven Adaptive Iterative Learning Control Approach: With Application to Wafer Stage

The feedforward control is becoming increasingly important in ultra-precision stages. However, the conventional model-based methods cannot achieve expected performance in new-generation stages since it is hard to obtain the accurate plant model due to the complicated stage dynamical properties. To tackle this problem, this article develops a model-free data-driven adaptive iterative learning approach that is designed in the frequency-domain. Explicitly, the proposed method utilizes the frequency-response data to learn and update the output of the feedforward controller online, which has benefits that both the structure and parameters of the plant model are not required. An unbiased estimation method for the frequency response of the closed-loop system is proposed and proved through the theoretical analysis. Comparative experiments on a linear motor confirm the effectiveness and superiority of the proposed method, and show that it has the ability to avoid the performance deterioration caused by the model mismatch with the increasing iterative trials.

Designing a Virtual Arboretum as an Immersive, Multimodal, Interactive, Data Visualization Virtual Field Trip

This paper describes a virtual field trip application as a new type of immersive, multimodal, interactive, data visualization of a virtual arboretum. Deployed in a game engine, it is a large, open-world simulation, representing 100 hectares and ideal for use when free choice in navigation and high fidelity are required. Although the computer graphics are photorealistic, it is different and unique from other applications that use game art or 2D 360-degree video, because it reflects high information fidelity as a result of the domain expert review, and the integration of geographic information system (GIS) data with drone images. Combined in-game as a data visualization, it is ideal for generating past or future worlds, in addition to representations of the present. Fusing information from many data sources—terrain data, waterbody data, plant inventory, population density data, accurate plant models, bioacoustics, and drone images—its design process and methods could be repeated and used in a wide range of augmented reality (AR) and virtual reality (VR) applications and devices. Results on presence, embodiment, emotions, engagement, and learning are summarized from prior pilot studies for context on use, and are relevant to schools, museums, arboretums, and botanical gardens interested in developing immersive informal learning applications.

Current cycle feedback iterative learning control for tracking control of magnetic levitation system

This paper presents the current cycle feedback iterative learning control (CCF-ILC) augmented with the modified proportional integral derivative (PID) controller to improve the trajectory tracking and robustness of magnetic levitation (maglev) system. Motivated by the need to enhance the point to point control of maglev technology, which is widely used in several industrial applications ranging from photolithography to vibration control, we present a novel CCF-ILC framework using plant inversion technique. Modulating the control signal based on the current tracking error, CCF-ILC reduces the dependency on accurate plant model and significantly improves the robustness of the closed loop system by synthesizing the causal filters to counteract the effect of model uncertainty. To assess the stability, we present a maximum singular value based criterion for asymptotic stability of linear iterative system controlled using CCF-ILC. In addition, we prove the monotonic convergence of output sequence in the neighbourhood of reference trajectory. Finally, the proposed control framework is experimentally validated on a benchmark magnetic levitation system through hardware in loop (HIL) testing. Experimental results substantiate that synthesizing CCF-ILC with the feedback controller can significantly improve the trajectory tracking and robustness characteristics of maglev system.

Resonance compensation in dual-stage hard disk drive servo system

To enhance the speed and precision, piezoelectric micro-actuator made form lead zirconate titanate (PZT) is used as the secondary actuator in dual-stage hard disk drive (HDD) where a voice coil motor (VCM) is used as the primary actuator. Both VCM actuator and PZT micro-actuator (MA) have the problem of mechanical resonance. Conventional approach is to use a pre-compensator notch filter to suppress the resonance. This method needs an accurate plant model, which is always difficult to obtain. On the other hand, a simple controller in the feedback path is a good solution for suppressing the resonance which does not require a very precise plant model. This paper presents a feedback resonant controller designed in simple way to suppress the resonance of PZT actuator. The design is based on a combined approach where M-norm minimisation and mixed passivity, negative-imaginary, small-gain theorem are used. Simulation results substantiate the effectiveness of the proposed controller.

Robust Missile Autopilot Design Using Two Time-Scale Separation

In this paper, time-scale separation, feedback linearization (FL), and extended state observer (ESO) based new design for an acceleration tracking pitch autopilot of a tail controlled, skid-to-turn tactical missile is presented. The pitch plane dynamics has been split into pitch rate dynamics as fast, and the acceleration dynamics as a slow subsystem by exploiting the naturally existing time-scale separation between them. FL-based controllers are then designed for the subsystems separately. To achieve robustness in the presence of uncertainties and disturbances, an ESO is designed for each of the subsystem separately. The ESOs estimate the effect of uncertainties in the respective subsystem and the estimate is used to robustify the feedback linearizing controller designed for the respective nominal subsystem. The design neither requires accurate plant model nor any information about the uncertainty. Closed-loop stability for the overall system is established. The effectiveness of the design in meeting specified tracking performance in the presence of significant uncertainties, unmodeled dynamics, measurement noise, control input, and rate saturation, and in varying missile velocity and altitude scenario is illustrated by simulation. Furthermore, to analyze the performance for different initial conditions and parameter perturbations, Monte Carlo simulation study is carried out and the results are presented. Finally, performance comparison of the proposed design with some existing controllers is presented to showcase the efficacy of the proposed design.

Nonlinear Control of Marine Surface Vessels

In the present study, a robust yaw control law design derived from nonlinear extended state observer (NESO) based nonlinear state error feedback controller (NSEFC) in conjunction with nonlinear tracking differentiator (NTD) for marine surface vessels is presented. As marine vessel operates in an environment where significant uncertainties and disturbances are present, an NESO is used to estimate the effect of the uncertainties and disturbances along with the plant states leading to a robust design through disturbance estimation and compensation. Convergence of NESO and NTD is demonstrated. The notable feature of the formulation is that to achieve robustness, accurate plant model or any characterization of the uncertainties and disturbances is not needed. Efficacy of the design is illustrated by simulation. Further, performance of the proposed design is compared with some existing controllers to showcase the effectiveness of the proposed design.

Real-time modeling, control and optimization of turbo-machinery auxiliary plants

In the Oil & Gas industry, the testing of auxiliary lubrication plants represents a mandatory preliminary activity before the whole turbo-machinery train (including the auxiliary lubrication plant) can be put in operation. To this end, the employment of both efficient and accurate plant models becomes very important to synthesize satisfactory control strategies for both testing and normal operation purposes. This way, many benefits (e.g. in terms of safety, times and costs of execution) can be achieved. Therefore, this article focuses on the development of innovative and efficient real-time models and control architectures to describe and regulate the auxiliary lubrication systems. All the new strategies have been validated through accurate real experimental campaign and real-time hardware. According to the Bond-Graph modeling strategy, an efficient lumped parameter model of the lube oil console has been built to properly optimize the description of such system and the efficiency of the control strategies...

Active disturbance rejection controller design for dynamically positioned vessels based on adaptive hybrid biogeography-based optimization and differential evolution

Vessels with a dynamic positioning system (DPS) are widely applied in ocean resource exploration. Because of the inaccuracy and coupling of the vessel dynamic model, it is important to design a controller that performs well in an oceanic environment. The active disturbance rejection controller (ADRC) is introduced in this study to control the vessel movement and positioning in the DPS. The merit of the ADRC is that it does not need an accurate plant and disturbance model. In the proposed method, an adaptive hybrid biogeography-based optimization (BBO) and differential evolution (DE) are developed. The orthogonal learning (OL) mechanism is employed to achieve adaptive switching to different searching mechanisms between BBO and DE. The proposed adaptive hybrid BBO-DE (AHBBODE) algorithm is then used to optimize the parameters of ADRC; these parameters are not easy to determine by using the trial and error method. Finally, the proposed method is compared with the BBO- and DE-based methods. The results show that better performance is obtained by the proposed method.

Robust control of nonlinear resonance in a clamped rectangular plate

This work is concerned with the control of nonlinear oscillations and resonance in a thin rectangular clamped plate representative of an aircraft panel with forcing frequency near to that of the first linear mode. The response of the plate to unsteady pressure force is modeled using the nonlinear von Kármán plate equation, the solution of which is obtained using modal expansion with the linear eigen modes. To suppress the vibrations, an input–output linearization (IOL) and uncertainty and disturbance estimator (UDE) based robust active vibration controller are proposed. The UDE is employed to estimate the composite uncertainty that comprises the effects of system nonlinearities and external disturbances and the estimate is used to augment the IOL controller to achieve robustness. The resulting controller, however, requires output derivatives for its implementation and to address the issue, an observer that employs the UDE estimated uncertainties for robustness is designed. Closed-loop stability for the overall system is established using Lyapunov’s direct method. The notable feature of the proposed design is that it neither requires an accurate plant model nor any detailed information about the external disturbances. Also, the design needs only the measurement of the center displacement of the plate for its implementation. Simulations are carried out for different disturbances and the results are presented to demonstrate the efficacy of the proposed controller. Lastly, performance comparison with some existing vibration control designs is carried out to showcase the effectiveness of the proposed formulation.

Robust Steering Autopilot Design for Marine Surface Vessels

In this paper, a novel design based on an extended state observer (ESO) technique for yaw control of marine surface vessels is proposed. A marine vessel by virtue of its operation at sea is inherently under the influence of uncertainties and disturbances. Uncertainties arise because of parametric variation in hydrodynamic coefficients due to trim, loading, ballast, speed, and changes in underwater depth, where external disturbances due to winds and waves are present. The uncertainties and disturbances pose a great challenge in designing an effective autopilot for such vehicles. In this work, a robust design for steering autopilot is proposed wherein the effect of uncertainties and disturbances is estimated using ESO and the estimate is used to augment an input–output linearization (IOL)-based controller designed for nominal system to achieve robustness. The notable feature of the design is that while achieving robustness, it neither requires accurate plant model nor any information about the uncertainties and disturbances. The effectiveness of the ESO in estimation of the total disturbance and states and in meeting the specified performance in the presence of significant uncertainties, disturbances, and measurement noise is illustrated by simulation. Further, to demonstrate its efficacy, performance comparison of the proposed controller with some well-known existing designs in literature is carried out and the results are presented.

Generalized extended state observer-based H∞ control design for a missile longitudinal autopilot

In this paper, a novel inner/outer-loop control framework for the pitch autopilot of a tail-controlled, skid-to-turn missile is proposed. The generalized extended state observer (GESO)-based inner-loop design is constructed to estimate the disturbances and uncertainties and simultaneously compensate them in the input channel. In doing so, the uncertainty of the model can be reduced within an appropriate range for robust control design. The outer-loop [Formula: see text] design based on mixed sensitivity method acquires the desired closed-loop performance and robustness. The presented [Formula: see text]-GESO design is novel in that it combines the powerful disturbance rejection methodology with the effective robust [Formula: see text] control technique. Moreover, it neither requires accurate plant model nor any information about the uncertainty and disturbance. Nonlinear simulations are performed to demonstrate the effectiveness of the proposed method. In order to further evaluate the robustness of the presented method, a performance comparison with equivalent-input-disturbance-based control and conventional gain-scheduled [Formula: see text] design is also implemented in the presence of parameter perturbations, wind disturbance, actuator characteristics, and measurement noise.

Robust aircraft control based on UDE theory

In this work, the Uncertainty and Disturbance Estimation (UDE) approach is employed for the design of a robust flight controller for high performance aircraft. The UDE estimated uncertainty is used to robustify an input–output linearization-based controller designed for nominal system. The UDE theory employing a first-order filter and α-filter is used for the estimation of the composite uncertainty. To address the issue of output derivatives, an observer which too employs the UDE estimated uncertainties for the purpose of robustness is designed. Closed-loop stability for the overall controller-observer system is established. Uncertainties and disturbances in terms of parametric variations, wind gust, unmodeled actuator dynamics and measurement noise are considered to evaluate and compare performances of the two filter-based designs. Further, a performance comparison with some of the existing designs is carried out and the results are presented to demonstrate the efficacy of the proposed work. A notable feature of the proposed design is that the approach neither requires an accurate plant model nor any information about the uncertainties and disturbances.

Control design, simulation and validation of a turbo-machinery auxiliary plant

In the oil and gas industry, the testing of auxiliary lubrication plants represents an important preliminary activity before the whole turbo machinery train (including the auxiliary lubrication plant) can be put in operation. Therefore, the employment of both efficient and accurate plant models becomes mandatory to synthesize satisfactory control strategies both for testing and normal operation purposes. For this reason, this paper focuses on the development of innovative real-time models and control architectures to describe and regulate auxiliary lubrication plants. In particular, according to the Bond-Graph modelling strategy, a novel lumped parameter model of the lube oil unit has been developed to properly optimize the behaviour of this unit if it is controlled. The code has been compiled and uploaded on a commercial real-time platform, employed to control the pressure control valve of the physical plant, for which a new controller has been developed. The comparison between the data obtained from the simulated system and acquired from the physical plant shows good agreement and the good performance and reliability of the proposed model and control strategy. The modelling approach and the control strategy have been developed in collaboration with GE Nuovo Pignone S.p.a. while the experimental data were acquired in a plant located in Ptuj (Slovenia).

Suppression of resonance in hard disk drive servo system with feedback resonant controller: a benchmark model study

In order to achieve the precision desired, the bandwidth of the read-write head positioning servomechanism of hard disk drive (HDD) is required to be very high to minimise the effects of external disturbances. However, any attempt to increase the bandwidth is hindered by increased gain in frequencies of mechanical resonances leading to oscillatory response. If not compensated properly, excessive or unbounded oscillation may even lead to instability. Cascade compensation is the popular method for compensating multiple resonant modes. However, this method requires a very precisely identified plant model. On the contrary, feedback controller works better in such situation which does not require a very accurate plant model. This paper presents a feedback resonant controller designed in a simple way to suppress the resonant modes of voice coil motor (VCM) actuator used in HDD servo system. Simulation results substantiate the effectiveness of the proposed design methodology.

Robust control of a supermarket refrigeration system using multi-stage NMPC

Using Nonlinear Model Predictive Control (NMPC), real world systems that are subject to stringent constraints can be controlled efficiently when accurate plant models are employed. Model uncertainties however may lead to poor performance of the controller and to constraint violations because of the wrong predictions. As in real applications, there often is a significant plant model mismatch, the controller must be robust to plant-model mismatch without deteriorating the performance of the controller significantly. Multi-stage NMPC is a robust scheme which has been proven to be less conservative than open loop worst case solutions because of the presence of feedback information at the future time stages is explicitly accounted in the problem formulation. In this paper, we study the application of this NMPC technique to a supermarket refrigeration system under uncertainty and show that the presence of uncertainties in the model lead to constraint violations when standard NMPC scheme is applied. The robust multi-stage NMPC scheme improves the controller performance by accounting for the uncertainties in the prediction and results in a reliable operation of the system.