Advanced Control Algorithms Research Articles

Mixed Logic Dynamic Models for MPC Control of Wind Farm Hydrogen-Based Storage Systems

The use of electric power by wind generation in actual grids is hampered by its inherent stochastic nature and the penalty deviations adopted in several electricity regulation markets with respect to power quality requirements. Coupling wind farms with advanced Energy Storage Systems (ESS) can help their integration within grids. In this direction, several studies have been conducted, but the problem is still open due to the constraints and limitations regarding the ESSs time autonomy, time response, degradation issues and overall costs. In order to take into account these relevant aspects, advanced control algorithms are needed. In this paper, a Model-Based Predictive Controller (MPC) is presented. Such a controller minimizes the degradation of the ESS and the load tracking error while fulfilling the operational constraints and dynamics. The ESS considered is hydrogen-based and the study has been developed within the EU-FCH 2 JU (European Union Fuel Cells and Hydrogen 2 Joint Undertaking) funded project HAEOLUS aiming at building and integrating advanced control strategies for a hydrogen-based ESS within a wind farm fence. Numerical simulations show the feasibility and the effectiveness of the proposed approach.

Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine

With increasing size and flexibility of modern grid-connected wind turbines, advanced control algorithms are urgently needed, especially for multi-degree-of-freedom control of blade pitches and sizable rotor. However, complex dynamics of wind turbines are difficult to be modeled in a simplified state-space form for advanced control design considering stability. In this paper, grey-box parameter identification of critical mechanical models is systematically studied without excitation experiment, and applicabilities of different methods are compared from views of control design. Firstly, through mechanism analysis, the Hammerstein structure is adopted for mechanical-side modeling of wind turbines. Under closed-loop control across the whole wind speed range, structural identifiability of the drive-train model is analyzed in qualitation. Then, mutual information calculation among identified variables is used to quantitatively reveal the relationship between identification accuracy and variables’ relevance. Then, the methods such as subspace identification, recursive least square identification and optimal identification are compared for a two-mass model and tower model. At last, through the high-fidelity simulation demo of a 2 MW wind turbine in the GH Bladed software, multivariable datasets are produced for studying. The results show that the Hammerstein structure is effective for simplify the modeling process where closed-loop identification of a two-mass model without excitation experiment is feasible. Meanwhile, it is found that variables’ relevance has obvious influence on identification accuracy where mutual information is a good indicator. Higher mutual information often yields better accuracy. Additionally, three identification methods have diverse performance levels, showing their application potentials for different control design algorithms. In contrast, grey-box optimal parameter identification is the most promising for advanced control design considering stability, although its simplified representation of complex mechanical dynamics needs additional dynamic compensation which will be studied in future.

Comparative assessment of control strategies for the biradial turbine in the Mutriku OWC plant

To be competitive against other renewable energy sources, energy converted from the ocean waves needs to reduce its associated levelised cost of energy. It has been proven that advanced control algorithms can increase power production and device reliability. They act throughout the power conversion chain, from the hydrodynamics of wave absorption to the power take-off to improve the energy yield. The present work highlights the development and test of several algorithms to control the biradial turbine which is to be installed in the Mutriku oscillating water column plant. A collection of adaptive and predictive controllers is explored and both turbine speed controllers and latching strategies are examined. A Wave-to-Wire model of one chamber of the plant is detailed and simulation results of six control laws are obtained. The controllers are then validated using an electrical test infrastructure to prepare the future deployment in the plant. Finally, the control strategies are assessed against criteria like energy production, power quality or reliability.

Design, development, and flight testing of the vertical take-off and landing GNC testbed EAGLE

Precise landing on planets, moons, and other larger celestial bodies requires powered descent, hovering, and vertical landing. Similarly, recent and future concepts for re-usable launch vehicle (RLVs) also involve a vertical take-off and landing. The development of guidance, navigation, and control (GNC) for this type of vehicles is a challenging task. To support this, a vertical take-off and landing vehicle has been developed for demonstrating the capability of conducting soft landings, smooth ascent, and hovering. The focus was put on a platform for testing new and advanced guidance, navigation, and control (GNC) algorithms that employ a base set of sensors and actuators typically present on such vehicles. It should represent a dynamics similar to a thrust vector controlled planetary lander or RLV, and should allow fast turn-around times as well as rapid prototyping capabilities for testing. In addition, the platform should provide the option for an additional small payload, e.g., enhancing the on-board avionics with different or more precise sensors. The result of this platform development is EAGLE (environment for autonomous GNC landing experiments). Its lift-off mass is about \(30\, \text{kg}\) and it is powered by a jet engine with a maximum thrust of \(400\, \text{N}\).

Electromagnetic Hysteresis Based Dynamics Model of an Electromagnetically Controlled Torque Coupling

This paper proposes a novel computationally efficient, easy-to-implement electromagnetic hysteresis based dynamics model of a kind of intelligent electromagnetic torque controlled coupling (EMTC), which, with drag torque under consideration, first models the electromagnetic hysteresis existing in the primary clutch with the classical Preisach model, and then models the transferred torques in the three friction elements of the center coupling in the slipping and locked modes, respectively. The performance of the model is verified by simulation and experiment jointly, which lays the basis for the development of advanced control algorithm.

Indoor Multi-Camera-Based Testbed for 3-D Tracking and Control of UAVs

Flight testbeds with multiple unmanned aerial vehicles (UAVs) are especially important to support research on multi-vehicle-related algorithms. The existing platforms usually lack a generic and complete solution allowing for software and hardware design. For such a purpose, this paper presents the design and implementation of a comprehensive multi-camera-based testbed for 3-D tracking and control of UAVs. First, the testbed software consists of a multi-camera system and a ground control system, which performs image processing, camera calibration, 3-D reconstruction, pose estimation, and motion control. In the multi-camera system, the positions and orientations of UAVs are first reconstructed by using epipolar geometric constraints and triangulation methods and then filtered by an extended Kalman filter (EKF). In the ground control system, a classical proportional–derivative (PD) controller is designed to receive the navigation data from the multi-camera system and then generates control commands to the target vehicles. Then, the testbed hardware employs smart cameras with field-programmable gate array (FPGA) modules to allow for real-time computation at a frame rate of 100 Hz. Lightweight quadcopter Parrot Bebop drones are chosen as the target UAVs, which does not require any modification to the hardware. Artificial infrared reflective markers are asymmetrically mounted on target vehicles and observed by multiple infrared cameras located around the flight region. Finally, extensive experiments are performed to demonstrate that the proposed testbed is a comprehensive and complete platform with good scalability applicable for research on a variety of advanced guidance, navigation, and control algorithms.

Identification of a control-oriented energy model for a system of fan coil units

In recent years, application of advanced control, fault detection and diagnosis algorithms for building heating and cooling systems has been intensively investigated with the aim to improve their energy efficiency and bring the buildings sector into the smart city arena. Hindering the trend, hysteresis and proportional–integral–derivative controllers are still a common practice for temperature control in buildings with Fan Coil Units (FCUs). Introduction of more sophisticated controllers for additional savings requires a cost-effective approach for identification of an energy model which accurately resembles thermal and hydraulic performance of a system of FCUs. In the present work, the control-oriented energy model of a system of FCUs is developed and accompanied with replicable, robust and simple methodologies for its identification derived by consolidating the advantages of physical modelling, identification methods and manufacturer’s catalogue data. The validity of the developed approach is tested on the 248-office living-lab. The introduced simple and accurate dynamic characterization of energy transmitted from a FCU to zone air fills the gap between thermal and energy management for buildings. This enables implementation of predictive building controls and unleashes significant energy and cost-saving potentials of a smart building in a smart city.

A review of reinforcement learning for autonomous building energy management

The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. Energy savings of greater than 20% are reported in the literature for more complex building energy management problems when implementing reinforcement learning. The main direction for future research and challenges in reinforcement learning are also outlined.

The Velociprobe: An ultrafast hard X-ray nanoprobe for high-resolution ptychographic imaging.

Motivated by the advanced photon source upgrade, a new hard X-ray microscope called "Velociprobe" has been recently designed and built for fast ptychographic imaging with high spatial resolution. We are addressing the challenges of high-resolution and fast scanning with novel hardware designs, advanced motion controls, and new data acquisition strategies, including the use of high-bandwidth interferometric measurements. The use of granite, air-bearing-supported stages provides the necessary long travel ranges for coarse motion to accommodate real samples and variable energy operation while remaining highly stable during fine scanning. Scanning the low-mass zone plate enables high-speed and high-precision motion of the probe over the sample. With an advanced control algorithm implemented in a closed-loop feedback system, the setup achieves a position resolution (3σ) of 2 nm. The instrument performance is evaluated by 2D fly-scan ptychography with our developed data acquisition strategies. A spatial resolution of 8.8 nm has been demonstrated on a Au test sample with a detector continuous frame rate of 200 Hz. Using a higher flux X-ray source provided by double-multilayer monochromator, we achieve 10 nm resolution for an integrated circuit sample in an ultrafast scan with a detector's full continuous frame rate of 3000 Hz (0.33 ms per exposure), resulting in an outstanding imaging rate of 9 × 104 resolution elements per second.

Mitigating ground effect on mini quadcopters with model reference adaptive control

Mitigating ground effect becomes a big challenge for autonomous aerial vehicles when they are flying in close proximity to the ground. This paper aims to develop a precise model of ground effect on mini quadcopters, provide an advanced control algorithm to counter the model uncertainty and, as a result, improves the command tracking performance when the vehicle is in the ground effect region. The mathematical model of ground effect has been established through a series of experiments and validated by a flight test. The experiments show that the total thrust generated by rotors increases linearly as the vehicle gets closer to the ground, which is different from the commonly-used ground effect model for a single rotor vehicle. In addition, the model switches from a piecewise linear to a quadratic function when the rotor to rotor distance is increased. A control architecture that utilizes the model reference adaptive controller (MRAC) has also been designed, where MRAC is added to the altitude loop. The performance of the proposed control algorithm has been evaluated through a set of flight tests on a mini quadcopter platform and compared with a traditional proportional–integral–derivative (PID) controller. The results demonstrate that MRAC dramatically improves the tracking performance of altitude command and can reduce the rise time by $$80\%$$ under the ground effect.

Framework of simulation-based vehicle safety performance assessment of highway system under hazardous driving conditions

Vehicles are extremely vulnerable to single-vehicle accidents under some hazardous driving conditions (i.e. strong wind, icy or snowy road surface). An integrated framework is proposed to assess single-vehicle traffic safety performance of stochastic traffic flow under hazardous driving conditions. Different from most existing studies focusing on a single vehicle moving at a constant speed, for the first time, the proposed work evaluates individual vehicle safety performance based on the time-dependent simulation results of stochastic traffic flow, including instantaneous speeds and positions of each vehicle as a part of simulated traffic flow. Simultaneously, complex geometric and other environmental conditions of the highway system are also considered realistically, not only during the safety assessment process, but also in quantifying the wind loads applied on the vehicles. Finally, with the safety information of each individual vehicle, an overall safety performance index of the whole traffic flow on the highway system is further introduced, which serves as a potential traffic safety performance measure and resilience indicator of transportation infrastructure systems under various hazards. This study has potential applications to not only regular vehicles, but also advanced traffic management and control algorithms for connected and autonomous vehicles in hazardous driving environments.

Multisampled Digital Average Current Controls of the Versatile Buck–Boost Converter

The coupled-inductor buck–boost converter is a versatile dc–dc converter, which is suitable for performing different roles within the hybrid power systems. Its multiple advantageous features include noninverting voltage step-up/step-down characteristic, high efficiency, wide bandwidth control, ability to control the input and output voltages and currents, and so on. All the existing current control approaches for this type of converter are based on analog control implementation, which allows achieving high closed-loop bandwidth. However, analog control limits the possibilities for applying the advanced control schemes and algorithms for achieving smooth operating mode transitions. This paper proposes two digital current control strategies for a versatile buck–boost converter: a predictive average current control and a multisampled average current control. Both the proposed digital control strategies ensure fast tracking of the control set point, low steady-state error, and a practically undegraded dynamic response of the system in comparison with the analog average current control. The theoretical analyses have been validated by means of simulations and experimental tests performed on a 48-V 600-W purpose-built prototype.

A New Method of Applying Data Engine Technology to Realize Neural Network Control

This paper presents a novel diagonal recurrent neural network hybrid controller based on the shared memory of real-time database structure. The controller uses Data Engine (DE) technology, through the establishment of a unified and standardized software architecture and real-time database in different control stations, effectively solves many problems caused by technical standard, communication protocol, and programming language in actual industrial application: the advanced control algorithm and control system co-debugging difficulties, algorithm implementation and update inefficiency, and high development and operation and maintenance costs effectively fill the current technical gap. More importantly, the control algorithm development uses a unified visual graphics configuration programming environment, effectively solving the problem of integrated control of heterogeneous devices; and has the advantages of intuitive configuration and transparent data processing process, reducing the difficulty of the advanced control algorithms debugging in engineering applications. In this paper, the application of a neural network hybrid controller based on DE in motor speed measurement and control system shows that the system has excellent control characteristics and anti-disturbance ability, and provides an integrated method for neural network control algorithm in a practical industrial control system, which is the major contribution of this article.

Decentralized High-Performance Control of DC Microgrids

Direct current (dc) microgrids have been widely used in many critical applications. Such systems avoid unnecessary ac/dc conversions and can simplify control design. To achieve high-performance control of such system, advanced control algorithm needs to be designed. This paper presents a novel decentralized output constrained control algorithm for single-bus dc microgrids. The control objectives are to realize high-performance control of dc bus voltage, user-defined load sharing, and circulating current minimization. Unlike conventional control algorithms, the control algorithm can guarantee not only convergence but also bounded transient tracking error. By transforming the constrained system into an unconstrained system, the transient response of dc bus voltage can always stay within user-defined, time-varying bounds. Convergence of the transformed system can meet any transient performance requirement of the original system. Through proper control effort distribution, overall load demand can be shared among the distributed generators (DGs) according to predefined percentages. Accurate load sharing can indirectly minimize the harmful circulating currents among the DGs. Switch-level simulation and hardware experimentation with both single-bus and multiple-bus dc microgrids demonstrate the effectiveness of the proposed control design.

Design and implementation of a test bed for building controls

Advanced controls play an essential role toward the improvement of building operational efficiency and the integration of responsive loads in buildings for grid services. Ideally, control algorithms must be sufficiently tested and validated before they are applied on real systems. This paper presents the development and current state of such an evolving test bed to support and enable experiments on advanced controls for buildings. The test bed presented in this paper consists of nine operating buildings—which possess various types of equipment and systems having different control systems and communication mechanisms (e.g., media and protocols) used in building automation systems—on the Pacific Northwest National Laboratory campus. The test bed architecture is developed in such a way that (1) it supports interactions among the buildings and heterogeneous building components and systems, including both virtual and physical devices, e.g., heating, ventilating, and air-conditioning and lighting systems; (2) it can be easily reconfigured for different control topologies and methodologies, e.g., centralized and distributed; (3) it allows selection of communication protocols, communication media, and computation resources; and (4) it is part of a larger cyber-physical test bed that includes both physical and virtual assets on distributed renewable generation, energy storage, and power system assets. Practical application: The test bed presented in this paper can be used by industry to develop and evaluate the performance of advanced control algorithms on real systems for buildings and buildings-to-grid applications. This provides practitioners an opportunity to test the applications and modify them accordingly based on their use cases and selection criteria, e.g., a controller modulating the temperature in a hospital will have different criterion as compared to an office building.

Dynamic modeling and control of the SPIRAL2 cryomodules

SPIRAL 2 (Caen, France) facility aims at delivering high intensity of rare isotope beams. The linear accelerator (LINAC) of the SPIRAL 2 facility is composed of 26 superconducting accelerating cavities distributed into 19 cryomodules cooled down with liquid helium at 4.4 K. A dynamic model of the cryomodules and their associated shields and valves thermodynamic behavior is proposed. This dynamic model is validated through comparisons between simulation and experimental data. Since the model is developed with the Simcryogenics library for MATLAB/Simulink environment, It is convenient for control loops design. Using the model, advanced control algorithms have been developed in order to achieve the cryomodule’s pressure stability required for beam acceleration. This paper presents the dynamic model, experimental versus simulation results as well as the control outcome.

An Advanced Digital Predictive Valley Current Control Algorithm for a Boost Converter

An advanced digital predictive valley current control algorithm for a boost converter is proposed. The control algorithm predicts the inductor current in a future period by sampling the voltages including input and output, and inductor current of the current period, which overcomes the problem of hardware periodic delay. Under the premise of ensuring the stability of the system, the control algorithm has a faster response speed as compared to the traditional digital proportional-integral (PI) control algorithm. In addition, it is not only algorithm simple, but also easy to implement on DSP of digital control chips. Finally, the proposed control algorithm is verified using simulations software and experimental measurements.

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.

A Robotic Recording and Playback Platform for Training Surgeons and Learning Autonomous Behaviors Using the da Vinci Surgical System

This paper describes a recording and playback system developed using a da Vinci Standard Surgical System and research kit. The system records stereo laparoscopic videos, robot arm joint angles, and surgeon–console interactions in a synchronized manner. A user can then, on-demand and at adjustable speeds, watch stereo videos and feel recorded movements on the hand controllers of entire procedures or sub procedures. Currently, there is no reported comprehensive ability to capture expert surgeon movements and insights and reproduce them on hardware directly. This system has important applications in several areas: (1) training of surgeons, (2) collection of learning data for the development of advanced control algorithms and intelligent autonomous behaviors, and (3) use as a “black box” for retrospective error analysis. We show a prototype of such an immersive system on a clinically-relevant platform along with its recording and playback fidelity. Lastly, we convey possible research avenues to create better systems for training and assisting robotic surgeons.

Exploration on the Construction of Experimental System of Heating Furnace Based on Advanced Control Algorithm

This paper focuses on the advanced control algorithm in the heating furnace experimental system, and discusses the control of temperature inertia and large hysteresis. It is proposed to analyze the working principle of data acquisition and temperature control of electric heating furnace. As the object of IPC control system for data acquisition, the I/O board adopts the mechanism modeling method to identify the dichotomy method, obtains the analysis structure of the temperature object model of the electric heating circuit, and identifies the temperature control model of the electric heating circuit. The model is finally controlled by PID control, and experiments and simulations are performed using MATLAB.