Optimal Control Scheme Research Articles

Optimal sizing of behind-the-meter battery energy storage systems under optimal battery operation: A case study in Ireland

The global shift toward a sustainable future has led to new policies to promote the use of renewable energy resources (RESs) in residential buildings. In Ireland, the number of installed roof-top photovoltaics (PVs) and home batteries, known as behind-the-meter (BTM) resources, has significantly increased in recent years, accelerated by grants administered by the Sustainable Energy Authority of Ireland (SEAI). In this work, an optimization tool that uses a combination of a Genetic Algorithm (GA) and a multi-objective mixed-integer linear programming (MOMILP) model is developed. This tool integrates an optimal battery control scheme from the MOMILP model aiming to minimize electricity bills and battery degradation to optimize the sizing of BTM batteries with minimum installation costs. Various scenarios have been simulated to assess the potential of the proposed model under different metering and billing systems, and optimization constraints. Compared to a case with a rule-based optimization model, results show that our optimization model can save 5.3 % more on annual bill costs while keeping battery degradation to a minimum of 2.1 % per year.

Adaptive optimal control of switched linear systems with input constraints

In this paper, an adaptive optimal control scheme is proposed for a class of switched linear systems with input constraints and unknown dynamics. Firstly, in the case that the system dynamics is known, the optimal controller and optimal switching condition are determined by introducing a non-quadratic cost function and employing the embedding transformation technique. After that, an adaptive algorithm is established for estimating the unknown dynamics of the switched linear systems. Then, when the system dynamics is unknown, the adaptive optimal controller and optimal switching condition are deduced by resorting to the certainty equivalent principle. A common Lyapunov function is constructed, by using which, it is proved that the closed-loop system is globally uniformly asymptotically stable. Simulations are finally conducted to further illustrate the effectiveness of the proposed method.

Enhancing quantum metrology for multiple frequencies of oscillating magnetic fields by quantum control

Abstract Quantum multi-parameter estimation has recently attracted increased attention due to its wide applications, with a primary goal of designing high-precision measurement schemes for unknown parameters. While existing research has predominantly concentrated on time-independent Hamiltonians, little has been known about quantum multi-parameter estimation for time-dependent Hamiltonians due to the complexity of quantum dynamics. This work bridges the gap by investigating the precision limit of multi-parameter quantum estimation for a qubit in an oscillating magnetic field model with multiple unknown frequencies. As the well-known quantum Cramér–Rao bound is generally unattainable due to the potential incompatibility between the optimal measurements for different parameters, we use the most informative bound instead which is always attainable and equivalent to the Holevo bound in the asymptotic limit. Moreover, we apply additional Hamiltonian to the system to engineer the dynamics of the qubit. By utilizing the quasi-Newton method, we explore the optimal schemes to attain the highest precision for the unknown frequencies of the magnetic field, including the simultaneous optimization of initial state preparation, the control Hamiltonian and the final measurement. The results indicate that the optimization can yield much higher precisions for the field frequencies than those without the optimizations. Finally, we study the robustness of the optimal control scheme with respect to the fluctuation of the interested frequencies, and the optimized scheme exhibits superior robustness to the scenario without any optimization.

Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants

Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants’ operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.

A novel robust nonlinear optimal second-order sliding mode control scheme for power optimization of wind energy conversion systems

In this article, we propose a novel robust nonlinear optimal second-order sliding mode controller using the homotopy perturbation method (RNOSOSMC-HPM) to maximize wind power capture and minimize the mechanical stress on the drive train. To design the nonlinear optimal controller, the homotopy perturbation method (HPM) is applied to compute the approximate solution of the partial differential Hamilton-Jacobi-Bellman (HJB) equation. Next, the nonlinear optimal controller is combined with a second-order sliding mode controller to create robustness and eliminate chattering. The RNOSOSMC-HPM controller can provide safe wind turbine operation under uncertainties and create a good trade-off between maximizing the wind power captured and attenuating the mechanical loads by minimizing the control input. To investigate the effectiveness of the presented the RNOSOSMC-HPM controller, we compare the results of the proposed method with some existing control schemes in two different scenarios. The results indicate that the RNOSOSMC-HPM controller furnishes desired responses.

Off‐policy reinforcement learning algorithm for robust optimal control of uncertain nonlinear systems

AbstractIn this article an optimal control scheme is proposed to solve robust control problem for matched and unmatched system. In the proposed optimal approach the value functions are designed such that the obtained optimal control law guarantees asymptotic stability of the uncertain nonlinear system. Since the proposed robust optimal control problem is not straightforward to solve, an off‐policy reinforcement‐learning algorithm based on neural networks approximation is developed to obtain robust optimal control law iteratively. The robust control law for matched uncertain systems can be achieved via proposed off‐policy learning algorithm without requiring exact knowledge of system's dynamics. The advantages of the proposed robust optimal controller are verified by comparative simulations on an uncertain model of a car suspension system and a mathematical nonlinear model.

Adaptive fuzzy echo state network optimal synchronization control of hybrid–order chaotic systems via reinforcement learning

In this paper, a novel optimal synchronization control scheme for fractional–integer hybrid–order chaotic systems is formulated. To deal with the fractional–order (FO) constraint, a transformation programme is developed and then the master system considered as an FO chaotic system is transformed into an integer–order one. A fuzzy echo state network (FESN) with the advantages of both fuzzy logic system and echo state network is introduced to approximate system uncertainty. Simultaneously, to alleviate the resource pressure, an optimal synchronization control is proposed in the light of the reinforcement learning mechanism, in which critic–actor update laws are constructed through the negative gradient of a positive function with regard to Bellman residual. Compared to typical fuzzy synchronization control, the devised FESN synchronization scheme with the same computational complexity possess superior approximation ability and synchronization performance. Ultimately, three simulation cases are exhibited to check the validity of the proposed approach.

Toward coordinated planning and hierarchical optimization control for highly redundant mobile manipulator

This paper represents a constraint planning and optimization control scheme for a highly redundant mobile manipulator considering a complex indoor environment. Compared with the traditional optimization solution of a redundant manipulator, infinity norm and slack variable are additionally introduced and leveraged by the optimization algorithm. The former takes into account the joint limits effectively by considering individual joint velocities and the latter relaxes the equality constraint by decreasing the infeasible solution area. By using derived kinematic equations, the tracking control problem is expressed as an optimization problem and converted into a new quadratic programming (QP) problem. To address the optimization problem, the two-timescale recurrent neural networks optimization scheme is proposed and tested with a 9 DOFs nonholonomic mobile-based manipulator. Additionally, the BI2RRT∗ path-planning algorithm incorporates path planning in the complex environment where different obstacles are positioned. To test and evaluate the proposed optimization scheme, both predefined and generated paths are tested in the Neurorobotics Platform (NRP) 22https://neurorobotics.net.which is open access and open source integrative simulation framework powered by Gazebo and developed by our team.

Time-Varying Optimal Formation Control for Second-Order Multiagent Systems Based on Neural Network Observer and Reinforcement Learning.

This article addresses a distributed time-varying optimal formation protocol for a class of second-order uncertain nonlinear dynamic multiagent systems (MASs) based on an adaptive neural network (NN) state observer through the backstepping method and simplified reinforcement learning (RL). Each follower agent is subjected to only local information and measurable partial states due to actual sensor limitations. In view of the distributed optimized formation strategic needs, the uncertain nonlinear dynamics and undetectable states may jointly affect the stability of the time-varying cooperative formation control. Furthermore, focusing on Hamilton-Jacobi-Bellman optimization, it is almost incapable of directly dealing with unknown equations. Above uncertainty and immeasurability processed by adaptive state observer and NN simplified RL are further designed to achieve desired second-order formation configuration at the least cost. The optimization protocol can not only solve the undetectable states and realize the prescribed time-varying formation performance on the premise that all the errors are SGUUB, but also prove the stability and update the critics and actors easily. Through the above-mentioned approaches offer an optimal control scheme to address time-varying formation control. Finally, the validity of the theoretical method is proven by the Lyapunov stability theory and digital simulation.

Dynamic optimal control of coal tar chemical looping gasification based on process modelling and intelligent screening

Aiming to solve the problem of low utilization and poor economic efficiency of coal tar, this study proposes a new method to convert coal tar into syngas that can be used for coal-to-oil production through chemical looping gasification reactions, thus reducing the cost of coal-to-oil production. This proposal converts tar to syngas for Fischer-Tropsch reaction through chemical looping gasification cleaning process, which is realized by process simulation and artificial intelligence prediction to ensure the stable products quality under feed jump abnormal conditions. The proposed process model is first built and the optimal operating parameters are determined by sensitivity analysis after the optimization sequence is determined from correlation analysis. A dynamic model based on the optimal steady-state model is second generated to design an optimal control scheme to efficiently deal with the unusual conditions of the feed step. The data-driven deep learning model LSTM is used to intelligently screen the optimal proportional control scheme with the advantage of low time and cost consumption. Finally, the optimal control scheme is obtained by combining the prediction results of the LSTM model and the controller additions of other important parameters, and the control effect is tested by simulating the tar feed leap conditions proves that the control6 scheme is highly resistant to disturbances. The method achieves 84.1 % high carbon conversion rate of medium-low temperature tar and 88.14 % excellent selectivity of syngas. At the same time, the adjusting of feed step condition with long-term and short-term memory network promotes the development of process design with the low time and cost consumption.

Hierarchical Sliding-Mode Surface-Based Adaptive Actor-Critic Optimal Control for Switched Nonlinear Systems With Unknown Perturbation.

This article studies the hierarchical sliding-mode surface (HSMS)-based adaptive optimal control problem for a class of switched continuous-time (CT) nonlinear systems with unknown perturbation under an actor-critic (AC) neural networks (NNs) architecture. First, a novel perturbation observer with a nested parameter adaptive law is designed to estimate the unknown perturbation. Then, by constructing an especial cost function related to HSMS, the original control issue is further converted into the problem of finding a series of optimal control policies. The solution to the HJB equation is identified by the HSMS-based AC NNs, where the actor and critic updating laws are developed to implement the reinforcement learning (RL) strategy simultaneously. The critic update law is designed via the gradient descent approach and the principle of standardization, such that the persistence of excitation (PE) condition is no longer needed. Based on the Lyapunov stability theory, all the signals of the closed-loop switched nonlinear systems are strictly proved to be bounded in the sense of uniformly ultimate boundedness (UUB). Finally, the simulation results are presented to verify the validity of the proposed adaptive optimal control scheme.

Bi-objective simulation optimization for online feedback control of variable speed limits considering uncertain traffic demands and compliance behaviors

Abstract Variable Speed Limits (VSL) stand out as a well-established and effective strategy to alleviate traffic congestion and enhance traffic safety on motorways. It allows Variable Message Signs (VMSs) to dynamically determine the speed limits according to real-time traffic states. This paper introduces an innovative online feedback control approach designed to regulate speed limit values on Variable Message Signs (VMSs), addressing multiple bottlenecks while considering their spatiotemporal constraints. Moreover, we offline optimize the gain coefficients of this feedback control approach in the simulation-based optimization (SBO) framework. Specifically, with average and variance of space-mean speeds as bi-objectives, a stochastic SBO model considering uncertain traffic demands and compliance behaviors is established and solved by a bi-objective surrogate-based promising area search (BOSPAS) algorithm. Real-field experiments conducted in Edmond City demonstrate the well-performing bi-objectives of the proposed approach, especially in handling uncertain compliance behaviors and traffic demands. Compared with the uncontrolled scenario, the feedback control schemes with the offline optimized gain coefficients improve the average and variance of space-mean speeds by up to 16.2% and 20.8%, respectively. Meanwhile, by the comparison of detailed performances, it is found that the optimized control schemes perform better than the uncontrolled scheme from the overall and local aspects. In conclusion, this study puts forward a general framework that applies an online feedback control approach with gain coefficients optimized offline by an SBO method to deal with real-time decision-making problems under uncertainties.

Optimization of a district heating system coupled with a deep open-loop geothermal well and heat pumps

The technical and economic aspects of a district heating system coupled with a deep open-loop geothermal well (DOLGW) and heat pumps were studied in the paper. A typical configuration of such a geothermal district heating system was proposed, and the corresponding thermal analysis was carried out. Based on the data from the DOLGW heat extraction test conducted in Hebei, China, the optimal size selections of heat pumps, plate heat exchangers, and terminal heat radiators for such a system was analyzed theoretically. In addition, an optimal control scheme for such a system with the change of environmental temperature or heating load were given.

Asynchronous distributed optimal power control for fatigue load minimization in wind farms

An asynchronous distributed optimal power control (OPC) scheme is proposed to minimize fatigue load in the wind farms (WFs) in this paper. To better satisfy the grid code, each wind turbine (WT) in the scheme is equipped with an energy storage system (ESS) at the DC link of the WT converter. Based on the WT topology, a WT-ESS control model is established, thus developing an OPC scheme to control the generator torque, pitch angle, and ESS output simultaneously for reducing fatigue load. By formulating the model predicted control (MPC)-based optimal problems, the virtual mechanical power control is proposed to regulate the ESSs output for clarifying the power interactions among WTs and ESSs. In order to achieve more effective control of the above three parameters among multiple WTs-ESSs, an asynchronous distributed alternating direction method of multipliers (AD-ADMM) is developed to simultaneously minimize the variations of thrust force, shaft torque, and flapping moment load of the WTs in a wider range. Considering the impact of communication delays, the proposed AD-ADMM algorithm has the higher time efficiency and system robustness. The convergence of the proposed AD-ADMM algorithm is analyzed, and several cases are used to validate the performance of the OPC scheme.© 2017 Elsevier Inc. All rights reserved.

Neural Optimal Control for Constrained Visual Servoing via Learning From Demonstration

This paper proposes a novel optimal control scheme for constrained image based visual servoing of a robot manipulator. For a robot manipulator with an eye-on-hand configuration, visibility constraint is an essential requirement to avoid servo failure, while robot’s actuator limits must also be satisfied. To ensure this, the constraints are modelled implicitly via learning the task and defining safe regions using expert human demonstrations via mixture of Dynamic Movement Primitives (DMPs). The visual servoing problem is then formulated as a closed-loop optimal control problem using these constraint model where a desired target (possibly time-varying) is obtained by acting upon the feedback from the real-time visual sensors. The visual servo control loop consists of a single network adaptive critic optimal tracking control scheme whose weights are tuned using Lyapunov stability criteria. The stability and the performance of the proposed control scheme is shown theoretically via Lyapunov approach and also verified experimentally using a seven degree of freedom (DOF) Franka Emika and six DOF Universal Robot (UR) 10 manipulator. The approach is also demonstrated on a use case scenarios in mock-up convenience store and warehouse setup. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The applications of robots in busy warehouses, healthcare sectors and convenience store, has a big societal impact. The scenarios in these real-world problems often consist of a dynamic environment. Therefore, sensor-based localization and planning, along with satisfying robot and task constraints are needed. These naturally adds up to the programming costs in addition to the robot platform and actuation. However, modern robots need intuitive and easy programming for more pervasive in a practical societal application. In our proposed method, this sensor-based planning with environmental constraints is modelled implicitly using the Programming by Demonstration framework. The user needs to catch the robot arm by his hand and teach the task at hand, and the framework captures the task and robot constraints while generalizing to new goals. This modelling, along with cost optimal controller, generates real-time constraint aware robot manipulation trajectories for the demonstrated task in dynamic environments.

Research on Risk Assessment and Prevention of Dewing on the Surface of the Thousand-handed Avalokitesvara Statue Based on COMSOL and CFD

The Thousand-handed Avalokitesvara statue in Dazu Stone Carving has been in a high temperature and humidity environment with serious air pollution for a long time. Under the combined effect of various environmental factors, its surface disease is extremely serious. Among them, condensation disease is one of the main diseases of the Thousand-handed Avalokitesvara statue, and also the root cause of other diseases. The surface temperature of the Thousand-handed Avalokitesvara statue was simulated by COMSOL simulation technology, and compared with the dew point temperature of the air near the wall at the same time, the seasonal and daily variation laws of the condensation risk on the image surface were analysed. To effectively prevent surface condensation, a three-level condensation risk assessment level was established based on the difference between the surface temperature and the dew point temperature near the wall. Summer is a period of high risk of condensation, during field investigation, it was found that under the condition that dehumidification measures have been added, a large area of condensation still occurs on the statue surface in summer. According to the actual measurement of the core area where the Thousand-handed Avalokitesvara statue is located, it was found that there were obvious differences between the near wall measuring points and those far away, the near wall air fluidity was poor, and the actual application effect of dehumidification equipment was poor. In order to better create a suitable storage environment and avoid condensation, based on CFD simulation technology, the distribution law of the internal environmental parameters of the Dabei Pavilion under different optimization prevention and control schemes was studied, and the optimal optimization scheme of closing the steam inlet and adding dehumidifiers was proposed, which could reduce the relative humidity and dew point temperature of the air near the wall of the Thousand-handed Avalokitesvara statue by 12.6% and 3ºC respectively, effectively reducing the risk of condensation.

Dynamic Event-Triggered Strategy-Based Optimal Control of Modular Robot Manipulator: A Multiplayer Nonzero-Sum Game Perspective.

Due to the limited computing and processing ability of modular robot manipulator (MRM) components, such as sensors and controllers, event-triggered mechanisms are considered a crucial communication paradigm shift in resource constrained applications. Dynamic event-triggered mechanism is developing into a new technology by reason of its higher resource utilization efficiency and more flexible system design requirements than traditional event-triggered. Therefore, an optimal control scheme of multiplayer nonzero-sum game based on dynamic event-triggered is developed for MRM systems with uncertain disturbances. First, dynamic model of the MRM is established according to joint torque feedback technique and model uncertainty is estimated by data-driven-based neural network identifier. In the framework of differential game, the tracking control problem of MRM system is transformed into the optimal control problem for multiplayer nonzero-sum game with the control input of each joint module as the player. Then, the static event-triggered control problem of MRM system is studied based on adaptive dynamic programming algorithm. On this basis, the internal dynamic variable describing the previous state of the system is introduced, and the characteristics of dynamic trigger rule and its relationship with static rule are revealed theoretically. By designing an exponential attenuation signal, the minimum sampling interval of the system is always positive, so that Zeno behavior is excluded. Lyapunov theory proves that the system is asymptotically stable and the experimental results verify the validity of the proposed method.

An Optimal Control Scheme for Grid-Connected Voltage Source Inverter via Grid Voltage Modulated-Direct Power Control

An Optimal Control Scheme for Grid-Connected Voltage Source Inverter via Grid Voltage Modulated-Direct Power Control

Analysis of stability, energy consumption and [formula omitted] emissions in novel discrete-time car-following model with time delay under V2V environment

This article aims to explore the stability, energy consumption and CO2 emissions of a novel discrete car-following model with time delay. Under the assumption that the preceding two successive vehicles can realize the communication through V2V (Vehicle to Vehicle) communication environment. Moreover, the time-delayed average optimal velocity difference control scheme is also considered. Based on the proposed discrete car-following model, error dynamical system is formulated and asymptotic stability is developed. The sufficient condition for the time-delayed average optimal velocity difference control scheme of the discrete car-following model with time delay is obtained, the asymptotic stability is ensured. In addition, numerical simulations are constructed and further illustrated the cooperative driving by V2V communication can improve stability, reduce energy consumption and CO2 emissions of traffic system. Moreover, the time-delayed average optimal velocity control scheme is also conductive to improve stability, reduce energy consumption and CO2 emissions of traffic system.

Optimizing Nonlinear Control Strategies for Autonomous Vehicles

Maximizing the effectiveness of nonlinear control techniques (NCT) is crucial for the broad acceptance and smooth integration of autonomous vehicles. Navigating the intricacies of real-world settings, boosting vehicle stability, and enhancing overall performance are all greatly impacted by these tactics. For manufacturers to successfully deploy autonomous cars in environments that are both dynamic and unpredictable, it is crucial to strike a balance between rigid control and flexibility. Autonomous vehicles confront a number of obstacles when trying to optimize nonlinear control systems, including changing road conditions, unpredictable traffic, and the requirement to make decisions in real-time. Because of the complexity of these problems, traditional control methods are frequently not up to the task, thus one need to find new ways to make control strategies more resilient and flexible. A Nonlinear Control Optimization Framework (NCOF) is suggested in this research; it makes use of adaptive control mechanisms, high-level optimization methods, and machine learning approaches. NCOF is engineered to enhance the vehicle's performance in a variety of situations by continuously adapting and optimizing control parameters using environmental data and real-time feedback. Beyond conventional navigation, the NCOF finds use in situations including emergency response, complicated urban surroundings, and bad weather. Improved decision-making, navigating, and passenger safety in a variety of situations are all outcomes of NCOF's optimization of nonlinear control techniques for autonomous cars. Thorough simulation assessments are carried out across several virtual scenarios to verify the efficacy of the suggested NCOF. Displaying the flexibility, stability, and efficacy of NCOF in various contexts, the simulations evaluate the optimal nonlinear control schemes' effectiveness in contrast to conventional methods.