Advanced Control Design Research Articles

Floating offshore wind turbine nonlinear model predictive control optimisation method

This paper presents a novel control parameter optimisation methodology for nonlinear model predictive control for floating offshore wind turbine operation, computing optimisation weights as environment conditions dependent variables. The main objective is to reduce the required time to define the optimal control parameters for the nonlinear control strategy, using an automated approach. To achieve this, an optimisation methodology based on extreme operational gust conditions is applied by employing a Random Walk-type Monte Carlo procedure. The primary aim is to introduce an advanced control design approach that addresses concerns related to the efficient power generation and longevity of floating systems, particularly considering the growing scale of wind turbines and the dynamic behaviour of floating platforms, which increase the system overall costs. The resulting optimised controller is also evaluated against state-of-the-art feedback-based control strategies in different operational environmental conditions.

Advanced controller design based on interval type-2 fuzzy neural network for elementary super-lift Luo converter

The elementary super-lift Luo converter is a third-order DC/DC step-up converter developed using the super-lift method that increases input voltage with low current and voltage ripples and a high voltage gain. On account of the nonlinearity and uncertainty incorporated with voltage, load changes, and switching operation of elementary super-lift Luo (ESLL) converter, it is a challenging task to design an efficient controller. To overcome these problems, this study proposes a new control approach based on interval type-2 fuzzy neural network (IT2FNN) to regulate a DC output current and voltage of ESLL converter. The parameters of IT2FNN were trained offline using a gradient descent algorithm. Mean square error (MSE), one of the most used statistical performance indicators, was used to evaluate the convergence accuracy performance of gradient descent algorithm. The DC output voltage regulation performance of IT2FNN was evaluated via a comparative simulation with interval type-2 fuzzy controller (IT2FC) and proportional + integral (PI) controller in MATLAB/Simulink environment in terms of settling time, average computational time, and recovery time under three different operational conditions: various reference voltages, input voltage, and load. Moreover, the DC output current regulation performance of IT2FNN was evaluated via a comparative simulation with IT2FC and PI controller in MATLAB/Simulink environment in terms of settling time and steady-state error under various reference current changes. The detailed simulation results obtained from comparative simulation validated the effectiveness of IT2FNN.

Advanced controller design for D-FACTS device in grid-connected photovoltaic system controller

Advanced controller design for D-FACTS device in grid-connected photovoltaic system controller

Design of Advanced Controllers for Speed Control of DC Motor

In this research work, an open-loop experimental DC motor from National Instruments is investigated for speed control applications in the nuclear industry with higher accuracy. For performance analysis, the DC motor is mathematically modeled, and advanced control techniques are established. The two design approaches are therefore hardware in loop which is an experimental facility and mathematical in nature which is a simulation model. An adequate mathematical model of the DC motor is developed using the first principle. The hardware realization is accomplished by the DC motor experimental setup. Different advanced control algorithms such as PID, SMC, and MPC are developed for both model-based and hardware-based DC motor research studies. Model development, control synthesis, simulation, and analysis are carried out in Simulink and LabVIEW programming environments. Closed loop performance analyses of nonlinear and predictive controllers are evaluated and found much better, smoother, and robust than linear controllers for speed control of DC motors under different conditions.

An advanced observer-based nonlinear model predictive controller design for distribution networks in bushfire prone areas

An advanced observer-based nonlinear model predictive controller design for distribution networks in bushfire prone areas

In-context learning of state estimators

State estimation has a pivotal role in several applications, including but not limited to advanced control design. Especially when dealing with nonlinear systems state estimation is a nontrivial task, often entailing approximations and challenging fine-tuning phases. In this work, we propose to overcome these challenges by formulating an in-context state-estimation problem, enabling us to learn a state estimator for a class of (nonlinear) systems abstracting from particular instances of the state seen during training. To this end, we extend an in-context learning framework recently proposed for system identification, showing via a benchmark numerical example that this approach allows us to (i) use training data directly for the design of the state estimator, (ii) not requiring extensive fine-tuning procedures, while (iii) achieving superior performance compared to state-of-the-art benchmarks.

Improved design of advanced controller for a step up converter used in photovoltaic system

Abstract The expanding need for electricity has stimulated research and development of novel supply sources for energy production, conversion, and storage. Many studies are being done to increase the effectiveness of conversion systems as renewable energy is integrated into power networks on a larger scale. The global challenge is to minimize manufacturing costs and increase the use of sustainable resources. In this sense, photovoltaics is viewed as a particularly promising source due to the low cost of implementation and the wide range of applications it could be used for. This research focuses on the analysis, modeling, and simulation of a smart controller for a step-up converter that uses an artificial neural network (ANN) as a maximum power point tracking (MPPT) technique to provide maximum power. The proposed ANN-based algorithm is performed in Matlab/Simulink software, and its effectiveness has been demonstrated under varying climatic conditions.

Rigid-flexible coupling multi-body dynamics modeling of a semi-submersible floating offshore wind turbine

A 14-degree-of-freedom (14-DOF) flexible multibody dynamics model is developed and verified for a semi-submersible floating offshore wind turbine (FOWT). The model considers the coupled dynamics of the platform, tower, nacelle, blades, and mooring subjected to external wind and wave loads. The platform is simplified as a rigid body connected to the seabed by the mooring system. The tower and blade are modeled as flexible cantilever beams. The energy method is used to derive the governing equations of motion, where the kinetic energy, potential energy, and work done by external wind-wave forces are all deduced in a global coordinate system. The 5-MW baseline semi-submersible FOWT is used to verify the derived model against the results simulated from FAST developed by the US National Renewable Energy Laboratory (NREL) at two scenarios: free decay state and different wind-wave load cases. Results show that the established FOWT model can well reflect the vibration characteristics of FAST model. The application of the model to the control of platform pitch with a tuned mass damper is studied. The simplified model could provide a low-order method for the structural dynamics analysis and advanced vibration control design for the multi-body components of the semi-submersible FOWT in the future.

Heat exchanger control using model predictive control with constraint removal

Climate change enforces the implementation of sustainable industrial production with a special focus on pollution reduction, resource management, and energy savings. These goals are addressed by designing advanced control methods using the solution of an adequately formulated optimization problem. Heat exchangers represent particularly energy-demanding plants that are challenging from the advanced controller design point of view. Model predictive control (MPC) is a suitable control strategy to address the relevant control tasks. The complexity of the real-time implementation of MPC directly depends on the number of inequality constraints in the corresponding optimization problem. Therefore, the real-time computational effort can be reduced by removing inactive constraints. Since removing inactive constraints does not change the optimal solution, it is desirable to detect inactive constraints corresponding to the current system state measurement and remove them from the formulation of the MPC problem before running the optimization solver. However, external disturbances, parametric uncertainties, and setpoint changes often impact real plants, limiting the application range of the conventional constraint removal MPC approach. In this paper, we propose a modification of the conventional constraint removal approach to address this issue. The modified constraint removal approach achieves the robustness required for a practical application to a laboratory-scaled heat exchanger. The control performance of the heat exchanger is analyzed from the industrial perspective considering the computational time and energy consumption by implementing the control approach on a 32-bit microcontroller.

Modeling and dynamic safety control of compressed air energy storage system

Energy process system positively contributes to the energy utilization efficiency, the energy complement, and the construction of a low-carbon sustainable energy system. The multiple energy subsystems are deep interdependent, therefore, significant operational risks exist in the energy process system. To avoid system risk and fulfill operation requirement, we propose the Safety-Index based model predictive control that coordinates the controller design and system safety in this work. The innovation of the method is to integrate the state-space model of process system with advanced controller design while accounting for system safety. Since the state-space model of the energy process system is described by coupled partial differential equations and ordinary differential equations, which leads to difficulties in modeling and solving, the main contribution of this work is to simplify the constrained optimization problem formed by model predictive controller. The paper addresses the compressed air energy storage system as case study. From the numerical simulations of the safety controller performance, it shows that the system safety can be guaranteed by control strategy which realizes the system operation target and reject the system external disturbances, which caused by environmental or operation change.

Intelligent Quadcopter Control Using Artificial Neural Networks

An advanced controller architecture and design for quadcopter control implementation is proposed in this study. Instead of using only the error information as input to the controller, reference and measured outputs are used separately independent from each other. This enhances the performance of the controller of quadcopter being a highly non-linear platform. In this study single layer neural network is directly used as a controller. A complex controller is grown from an initially simple PID controller. This elevates the need for time consuming search in huge parameter space due to very high dimensions. About ten percent improvement over state-of-the-art controllers is observed and results are reported both numerically and graphically. Promising results encourage to use the type of controller proposed for various real applications.

The multi-point hydraulic control method for advanced controller design of the open canal irrigation systems

Abstract Most studies about the automatic control of open canal irrigation systems only focus on the distant downstream water level, which ignores the fact that the offtakes may be located anywhere along the canal. Such a simplified control strategy is likely to result in uncontrollable and inefficient water delivery. Therefore, a multi-point hydraulic control method is proposed, in which a simplified Saint-Venant model is formulated to describe the hydraulic states of multiple controlled points. Then, it is underlined that the controlled points with and without the offtakes may have different control objectives. It is suggested to implement soft constraints to the downstream end when there is no offtake, meaning that moderate water level fluctuations are acceptable. By comparing with the common model predictive control (MPC) controller, where the Integrator Delay model and hard constraint are used for distant downstream water level control, the proposed MPC controller successfully improved the water level control stability before the offtakes and the water supply reliability by 91 and 69.5% under the conventional condition and by 54.9 and 27.1% under the water-deficient condition. Accordingly, the proposed multi-point hydraulic control method shows great potential for the precision irrigation of large irrigation districts.

The Integrator Dual-Delay model for advanced controller design of the open canal irrigation systems with multiple offtakes

The Integrator Delay (ID) model is widely used for advanced controller design in canal automation projects, assuming that all offtakes are aggregated into one at the most downstream end for each canal pool. However, such an unrealistic assumption differs greatly from the actual location distribution of the offtakes in most open canal irrigation systems (OCIS), leading to increased uncertainties and inaccurate canal operations. For this reason, this study introduced the Integrator Dual-Delay (IDD) model, capable of innovatively considering the lag-effect of the offtake flow changes alongside the irrigation canal. To investigate the effectiveness of the modified control-oriented model in upgrading the canal automatic operating systems, two centralized model predictive controllers were designed with the ID model and the IDD model, respectively. Then the simulation tests were carried out on the Third Main Canal of the Zhanghe Irrigation System in Southern China. Two performance indicators of Non-dimensional Integrated Square of Error (NISE) and Non-dimensional Integrated Square of Discharge Change (NISQ) were adopted to measure the water level control stability and flow control stability. The results show that with the IDD model, the NISE is reduced from 3.53 × 10-5 to 1.88 × 10-5 and the NISQ is reduced from 3.23 × 10-6 to 2.88 × 10-6, indicating that the proposed IDD model can help the control stabilities of water level and flow to be improved by 46.7 % and 10.8 %, respectively. The IDD model constructs a more accurate state-space prediction process for advanced controller design and contributes to more efficient canal automatic operations, showing great potential in precision irrigation for various OCIS.

An efficient hybrid MPA-MOA control approach based on DC microgrid connected constant power loads

The increased use of DC microgrid for complex application leads to the need for advanced control design for stable operation of the system. Loads connected to a DC microgrid are controlled by power electronic devices and exhibit constant power load (CPL) behavior, which is a serious challenge for stability as it enhances nonlinearity and reduces effective damping. This manuscript proposes an effective hybrid approach based on DC micro grid (MG) connected constant power loads. The proposed control approach is the consolidation of Marine Predators Algorithm (MPA) and mayfly optimization algorithm (MOA), hence it is named as hybrid MPA-MOA approach. The DC microgrid system contains the sources, like two photovoltaic (PV), two wind turbine (WT), grid, battery. The major objective of the proposed approach is “to find the problems while interfacing the sources of the microgrid and increase the security of the system”. The proposed approach contains two controllers, they are primary and secondary. The primary controller is based on droop controller that shares the current and limits the oscillations because of the constant power loads (CPL). The secondary controller is used to regulate the voltage of the system from a single area. The secondary control is executed using the proposed MPA-MOA method. The proposed method is executed on MATLAB/Simulink platform; its performance is analyzed with the existing methods. The THD (%), efficiency (%) and Eigen value of the proposed technique achieves 1.4%, 92% and -9.3541±j2.4209.

Composite Adaptive Synchronous Control of Dual-Drive Gantry Stage With Load Movement

In gantry systems, load usually moves according to different production tasks. The continuous change of the beam centroid position is a non-negligible factor for achieving high-performance synchronous control. Therefore, it cannot be simply treated as an external disturbance. In addition, advanced controller design and model parameter identification are very important for precision motion control systems. This article addresses the synchronous control of dual-drive gantry platforms with load movement to improve their steady-state and transient performance. Based on the analysis of the rigid and flexible characteristics of gantry mechanical components, a rigid-flexible coupling dynamic model is proposed, which provides a theoretical basis for the control system design. To ensure zero steady-state tracking error and parameter convergence in the presence of parameter uncertainties and unknown disturbances, a composite adaptive control method integrating excellent output tracking performance and parameter estimation is presented. Furthermore, the bridge between load movement and centroid position is established through frequency domain identification, which facilitates reasonable thrust distribution. Comparative experimental results are presented that confirm the effectiveness and advantages of the proposed synchronous control scheme.

Mechanism Development and Pulse-Width Modulation Advanced Controller Design of Low Torque Manipulator

Mechanism Development and Pulse-Width Modulation Advanced Controller Design of Low Torque Manipulator

A symbolic framework to obtain mid-fidelity models of flexible multibody systems with application to horizontal-axis wind turbines

Abstract. The article presents a symbolic framework (also called computer algebra program) that is used to obtain, in symbolic mathematical form, the linear and nonlinear equations of motion of a mid-fidelity multibody system including rigid and flexible bodies. Our approach is based on Kane's method and a nonlinear shape function representation for flexible bodies. The shape function approach does not represent the state of the art for flexible multibody dynamics but is an effective trade-off to obtain mid-fidelity models with few degrees of freedom, taking advantage of the separation of space and time. The method yields compact symbolic equations of motion with implicit account of the constraints. The general and automatic framework facilitates the creation and manipulation of models with various levels of complexity by adding or removing degrees of freedom. The symbolic treatment allows for analytical gradients and linearized equations of motion. The linear and nonlinear equations can be exported to Python code or dedicated software. There are multiple applications, such as time domain simulation, stability analyses, frequency domain analyses, advanced controller design, state observers, and digital twins. In this article, we describe the method we used to systematically generate the equations of motion of multibody systems and present the implementation of the framework using the Python package SymPy. We apply the framework to generate illustrative land-based and offshore wind turbine models. We compare our results with OpenFAST simulations and discuss the advantages and limitations of the method. The Python implementation is provided as an open-source project.

Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems’ performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI’s and Elsevier’s databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields.

Advanced controller design for uncertain linear systems with time‐varying delays via augmented zero equality approach

This paper deals with the stability analysis and controller design for linear systems with time‐varying delays and parameter uncertainties. By choosing appropriate augmented Lyapunov–Krasovskii functionals, a set of linear matrix inequalities is derived to get advanced feasible region of stability and controller gain matrices that guarantee the asymptotic stability of the concerned systems within maximum bound of time delays and its time derivative. To further reduce the conservatism of stabilization criterion, a recently developed mathematical technique that constructed a new augmented zero equality is applied. Finally, two numerical examples are utilized to show the validity and superiority of the proposed methods.

Energy Management in Buildings: Lessons Learnt for Modeling and Advanced Control Design

This paper presents a comparative analysis of different modeling and control techniques that can be used to tackle the energy efficiency and management problems in buildings. Multiple resources are considered, from generation to storage, distribution and delivery. In particular, it is shown what are the real needs and advantages of adopting different techniques, based on different applications, type of buildings, boundary conditions. This contribution is based widely on the experience performed by the authors in the recent years in dealing with existing residential, commercial and tertiary filed buildings, with application ranging from local temperature control up to smart grids where buildings are seen as an active node of the grid thanks to their ability to shape the thermal and electrical profile in real time. As for control models, a wide range of modeling techniques are here investigated and compared, from linear time-invariant models, to time-varying, to nonlinear ones. Similarly, control techniques include adaptive ones and real-time predictive ones.