Optimal Control Algorithm Research Articles

Energy Storage Management of a Solar Photovoltaic–Biomass Hybrid Power System

Remote areas that are not within the maximum breakeven grid extension distance limit will not be economical or feasible for grid connections to provide electrical power to the community (remote area). An integrated autonomous sustainable energy system is a feasible option. We worked on a novel multi optimization electrical energy assessment/power management system of a microgrid network that adopted combined dispatch, load-following, and cycle-charging strategies (control system) that acted as a power interface module over the hybrid configuration of energy sources (grid network/downdraft biomass generator/solar photovoltaic), thermal load controller-boiler systems, and hybrid energy-storage technologies (lithium, iron flow, sodium sulfur, and flywheel) to enable the microgrid network to operate in the island (off grid), grid, and island-able (ability to isolate itself when it is connected to the grid network) modes efficiently and effectively. An optimal multitask control algorithm and the storage units of modeled power generation sources were executed with the HOMER software application to improve the energy system’s efficiency, promote effective storage management, minimize energy loss, and improve the lifespan of the microgrid network. The integrated energy system can work for both rural and urban areas.

Two-step optimization algorithm for energy management and active-reactive power commands for real-time operation of hybrid microgrids

This paper proposes a two-step optimization algorithm for energy management and optimal control of activereactive power commands in microgrids. The first step ensures an optimal energy management and provides the active power setpoints for power plants and energy storage. The second step calculates the voltage setpoints for power plants and reactive power flows whilst minimizing power transmission losses. This work focuses on the second step of the algorithm and the framework to combine both. A previous study introduces the first step based on a receding-horizon scheme. It is analysed and discussed the performance of the algorithm against two decentralized methods widely used in the literature. The results show that the proposed algorithm reduces power transmission losses by more than 20% compared to other methods.

Fuzzy logic based adaptive vibration control system for structures subjected to seismic and wind loads

In this study, an attempt has been made to develop a Fuzzy Logic Multi Verse Optimal Control (FLMVOC) system as a new adaptive real-time vibration control mechanism for structures subjected to seismic excitation and wind load by utilizing the capability of the stochastic optimization method and fuzzy logic technique.The magnetorheological damper (MR) is deployed as a controllable vibration damping system in this study due to its excellent damping performance and low energy consumption. Therefore, the analytical model for the MR damper is formulated and integrated with the developed fuzzy logic optimal control (FLOC) algorithm. The story drift and absolute acceleration have been defined as the inputs of the fuzzy logic controller (FLC), while the MR commanding voltage is considered as the controller’s output. Then, the membership functions and fuzzy rule base have been formulated. To derive the optimal controller, the FLC with full parameters has been trained with multi objective multi verse algorithm (MOMVO). For this purpose, the MATLAB program and its Simulinks have been integrated and hybridised with finite element package to simulate and evaluate structure response for various input parameters.The developed FLMVOC system has been implemented in three story shear building subjected to seismic load and 60 story wind induced high rise building in order to evaluate its efficiency in diminishing the dynamic response of the structure.The result revealed that FLMVOC system successfully reduced structural drifts by 60%, 53%, and 41% under the effect of El Centro, Kobe, and Northridge earthquakes, respectively, while the floor absolute acceleration was reduced by 38%, 17%, and 10%, respectively. For the wind induced structure, the proposed system showed the ability to maintain the floor acceleration within people’s comfort criterion in addition to the reduction in story drift.

HYBRID NEURO-FUZZY-GENETIC ALGORITHMS FOR OPTIMAL CONTROL OF AUTONOMOUS SYSTEMS

In recent years, there has been an increasing demand for efficient and robust control algorithms to optimize the performance of autonomous systems. Traditional control techniques often struggle to handle the complexity and uncertainty associated with such systems. To address these challenges, hybrid neuro-fuzzy-genetic algorithms have emerged as a promising approach. This paper presents a comprehensive review of the application of hybrid neuro-fuzzy-genetic algorithms for optimal control of autonomous systems. The proposed algorithms combine the strengths of neural networks, fuzzy logic, and genetic algorithms to achieve adaptive and optimal control in real-time scenarios. The neuro-fuzzy component provides the ability to model and handle complex and uncertain systems, while the genetic algorithm component facilitates the optimization of control parameters. The combination of these techniques enables autonomous systems to adapt and optimize their control strategies based on changing environments and objectives. The paper discusses the underlying principles of hybrid neuro-fuzzy-genetic algorithms, their advantages, and challenges. It also provides a review of the state-of-the-art research in this field, highlighting successful applications and potential future directions. Overall, the integration of neuro-fuzzy-genetic algorithms in autonomous systems holds great promise for achieving optimal control in various domains, including robotics, aerospace, and autonomous vehicles.

A novel optimal control strategy for regenerative active suspension system to enhance energy harvesting

The conventional onboard actuators in the active suspension system (ASS) consume a high amount of external energy, which restricts their application in reducing the effects of vehicle body vibration caused by road roughness. To address this limitation, the energy-regenerative active suspension system (RASS) based on the electromagnetic structure has been introduced. This paper presents a new optimal control algorithm for the RASS that directly considers energy regeneration in combination with ride comfort in its performance index. The proposed algorithm can be tuned to direct the working points towards high energy regeneration zones. Meanwhile, it employs a realistic nonlinear model of the suspension system to achieve high performance. By applying proper operating electric circuits, the performance of the RASS under the proposed controller is studied for various road conditions. The obtained results indicate that the proposed control strategy delivers lower acceleration over the human body-sensitive frequency range based on ISO2631-1, despite harvesting energy. For instance, when the vehicle travels on a random road with a velocity of 12 m/s, the proposed nonlinear control strategy with a suitable weighting factor increases the capacitor voltage from the initial value of 20 V to 40 V during a 15 s interval. This shows a 31% increase compared to the case where the weighting factor is taken to be zero, according to the previous strategies. Additionally, the proposed strategy by the nonlinear controller remarkably increases the harvested energy compared with a linear optimal controller.

Optimal Control Algorithm for Subway Train Operation by Proximal Policy Optimization

With the increasing scale of the urban subway, the total energy consumption of the subway has increased dramatically and poses a great challenge to the comfort of passengers and the punctuality of train operation. In order to ensure on-time train operation and passenger comfort, and at the same time reduce the energy consumption of subway operation, this paper proposes a Proximal Policy Optimization (PPO)-based optimization algorithm for the optimal control of subway train operation. Firstly, a reinforcement learning architecture for optimal control of subway train operation is constructed with the position and speed of train operation as the reinforcement learning state, energy consumption and comfort as the optimization objectives, and train operation time as the constraint. The proposed reinforcement learning model is trained by the PPO algorithm, and the reward scaling is added to the training process to accelerate the training speed and improve the efficiency of the algorithm. The experimental results show that the proposed PPO with reward scaling algorithm can effectively reduce train energy consumption and improve passenger comfort while ensuring on-time train operation.

MPC-ECMS Energy Management of Extended-Range Vehicles Based on LSTM Multi-Signal Speed Prediction

Rule-based energy management strategies not only make little use of the efficient area of engines and generators but also need to perform better planning in the time domain. This paper proposed a multi-signal vehicle speed prediction model based on the long short-term memory (LSTM) network, improving the accuracy of vehicle speed prediction by considering multiple signals. First, various signals were collected by simulating the vehicle model, and a Pearson correlation analysis was performed on the collected multiple signals in order to improve the model’s prediction accurate, and the appropriate signal was selected as the input to the prediction model. The experimental results indicate that the prediction method greatly improves the predictive effect compared with the support vector machine (SVM) vehicle speed prediction method. Secondly, the method was combined with the model predictive control-equivalent consumption strategy (MPC-ECMS) to form a control strategy suitable for power maintenance conditions enabling the equivalent factor to be adjusted adaptively in real-time and the target state of charge (SoC) value to be set. Pontryagin minimum principle (PMP) enables the battery to calculate the range extender output power at each moment. PMP, as the core algorithm of ECMS, is a common real-time optimal control algorithm. Then, taking into account the engine’s operating characteristics, the calculated range extender power was filtered to make the engine run smoothly. Finally, hardware-in-the-loop simulation (HIL) was used to verify the model. The simulation results demonstrate that this method uses less fuel than the equivalent fuel consumption minimum strategy (ECMS) by 1.32%, 9.47% when compared to the power-following control strategy, 15.66% when compared to the SVM-MPC-ECMS, and only 3.58% different from the fuel consumption of the dynamic programming (DP) control algorithm. This shows that this energy management approach can significantly improve the overall vehicle fuel economy.

Lower and upper threshold limit for artificial neural network based chilled and condenser water temperatures set-point control in a chilled water system

In this study, an ANN (artificial neural network) based real-time optimized control algorithm for a chilled water cooling system was developed and applied in an actual building to analyze its cooling energy saving effects through in-situ application and actual measurements. For this purpose, the cooling tower’s CndWT (condenser water temperature) and the chiller’s ChWT (chilled water temperature) were set as system control variables. To evaluate algorithm performance, the electric consumption and the COP (coefficient of performance) were compared and analyzed when ChWT and CndWTs were controlled conventionally and controlled based on the ANN. During the analysis, unexpected abnormal data was observed due to insufficient training data and limited consideration of OWBT (outdoor air wet-bulb temperature) when determining the CndWT set-point. Therefore, it is necessary to further build training data from a wider range of conditions and to set the lower limit of CndWT set-point to at least +3.6 °C above OWBT when the OWBT is higher than 23 °C, so that further energy savings can be achieved.

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations.

This paper presents a hybrid Smell Agent Symbiosis Organism Search Algorithm (SASOS) for optimal control of autonomous microgrids. In microgrid operation, a single optimization algorithm often lacks the required balance between accuracy and speed to control power system parameters such as frequency and voltage effectively. The hybrid algorithm reduces the imbalance between exploitation and exploration and increases the effectiveness of control optimization in microgrids. To achieve this, various energy resource models were coordinated into a single model for optimal energy generation and distribution to loads. The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. The development of SASOS comprises components of Symbiotic Organism Search (SOS) and Smell Agent Optimization (SAO) codified in an optimization loop. Twenty-four standard test function benchmarks were used to evaluate the performance of the algorithm developed. The experimental analysis revealed that SASOS obtained 58.82% of the Desired Convergence Goal (DCG) in 17 of the benchmark functions. SASOS was implemented in the Microgrid Central Controller (MCC) and benchmarked alongside standard SOS and SAO optimization control strategies. The MATLAB/Simulink simulation results of the microgrid load disturbance rejection showed the viability of SASOS with an improved reduction in Total Harmonic Distortion (THD) of 19.76%, compared to the SOS, SAO, and MCC methods that have a THD reduction of 15.60%, 12.74%, and 6.04%, respectively, over the THD benchmark. Based on the results obtained, it can be concluded that SASOS demonstrates superior performance compared to other methods. This finding suggests that SASOS is a promising solution for enhancing the control system of autonomous microgrids. It was also shown to apply to other sectors of engineering optimization.

Seismic Response Control for Bridge Piers with Semi-Active MR Damper Based on Displacement Feedback

ABSTRACT This study investigates the semi-active control of railway bridges using an MR damper to reduce the excessive displacement in the bearings of isolated bridges under severe earthquakes. Within the study context, the variable-oval-control (VOC) rule based on structural response displacement feedback is applied to the MR damper and compared with a passive control and clipped optimal semi-active control algorithm. The simulation results show that the dynamic response of railway bridge piers can be effectively reduced, and seismic safety can be significantly improved using MR dampers based on a variable ellipse control strategy. MR damper, based on the VOC rule, directly adopts structural response displacement to perform control force state change. The VOC method only requires the minimum control force to achieve a good displacement control effect, high control efficiency, and stable control effect.

Model-based linear–quadratic–integral controller for simultaneous regulation of the current profile and normalized beta in NSTX-U

Achieving advanced scenarios that are characterized by steady-state operation, stable plasma confinement, and high-performance plasmas is one of the primary objectives of the National Spherical Tokamak eXperiment-Upgrade (NSTX-U). Active control of the plasma may be necessary to achieve these conditions. In particular, control algorithms that can simultaneously optimize the shapes and values of plasma profiles and scalars, respectively, may play a critical role in robustly achieving and sustaining these advanced scenarios. In this work, a model-based optimal control algorithm is developed for feedback control of the current profile in NSTX-U. The linear, finite-dimensional, control model is derived by discretizing and linearizing the magnetic diffusion equation in combination with empirical correlations for electron density, electron temperature, and noninductive current drives. The linear, time-variant model is then used for designing a linear–quadratic–integral (LQI) controller that is capable of regulating both the safety factor and the normalized beta around desired targets. The controller determines the neutral beam injection powers and the overall plasma current that are needed to achieve the desired current profile and normalized beta. The proposed controller is tested in higher-fidelity nonlinear simulations that employ 1D models for the evolutions of both current and temperature profiles using the Control Oriented Transport SIMulator (COTSIM). The closed-loop simulations show the effectiveness of the controller at shaping the safety factor in NSTX-U while achieving the desired normalized beta.

Learning-based distributed optimal power sharing and frequency control under cyber contingencies

This paper proposes a learning-based distributed optimization framework to enhance the resilience of distributed power system algorithms against communication failures. The autoregressive-moving-average (ARMA) model is used to estimate the missing states and control actions of neighboring agents during communication contingencies. The proposed framework allows agents to predict the future control actions of neighbors. Thus, even during complete loss of communication, agents can efficiently perform distributed optimization. We use the Distributed Optimal Frequency Control (DOFC) algorithm, which includes optimal power sharing to achieve frequency stability, as a benchmark application platform to show the effectiveness of the proposed framework. The theoretical findings are evaluated on two practical power systems. The results show that the ARMA-based DOFC algorithm can asymptotically reach the same convergence rate as the power system without communication interruptions.

QuOCS: The quantum optimal control suite

Quantum optimal control includes a family of pulse-shaping algorithms that aim to unlock the full potential of a variety of quantum technologies. The Quantum Optimal Control Suite (QuOCS) unites experimental focus and model-based approaches in a unified framework. Easy usage and installation presented here and the availability of various combinable optimization strategies is designed to improve the performance of many quantum technology platforms, such as color defects in diamond, superconducting qubits, atom- or ion-based quantum computers. It can also be applied to the study of more general phenomena in physics. In this paper, we describe the software and the toolbox of gradient-free and gradient-based algorithms. We then show how the user can connect it to their experiment. In addition, we provide illustrative examples where our optimization suite solves typical quantum optimal control problems, in both open- and closed-loop settings. Integration into existing experimental control software is already provided for the experiment control software Qudi (Binder et al., 2017 [41]), and further extensions are investigated and highly encouraged.QuOCS is available from GitHub, under Apache License 2.0, and can be found on the PyPI repository. Program summaryProgram Title: QuOCS - Quantum Optimal Control SuiteCPC Library link to program files:https://doi.org/10.17632/wjjch757fk.1Developer's repository link:https://github.com/Quantum-OCS/QuOCSLicensing provisions: Apache-2.0Programming language: PythonExternal routines: NumPy [1], SciPy [1], JAX [2]Nature of problem: Quantum systems are typically controlled by time-dependent electromagnetic fields to perform a certain set of quantum operations. Those operations may in turn provide building blocks for various quantum information processing tasks such as quantum computation, communication, simulation, sensing, and metrology. Numerous control strategies exist to design and improve such operations [3]. While some strategies are constructed to target a rather specific problem with high efficiency, others are more general to solve a wide range of applications [4,5]. To access the different algorithms, one has to download different optimization suites with different input and output parameters, making them hard to compare and combine. To benefit from the variety of algorithms, we have devised a customizable and intuitive optimization suite that simultaneously provides access to some of the most popular quantum optimal control algorithms.Solution method: We combine, in a unified framework, some of the frequently used optimal control algorithms which are the dressed Chopped Random Basis method (dCRAB) [6], and Gradient Ascent Pulse Engineering (GRAPE) [7], with an extension to make use of Automatic Differentiation (AD) [8]. The software is able to connect to both models of quantum dynamics in simulations and real-time quantum experiments to perform open- and closed-loop optimization, respectively. With minimal knowledge of optimal control theory, the user can manage to run optimizations of quantum processes using a variety of additional features such as stopping criteria and drift compensation. Logging and data management of the optimization progress and results are also handled by the suite. Its modular structure allows for extensions that accommodate customized algorithms and can be implemented by the user straightforwardly.Additional comments including unusual features: The connection to the experiments is performed by an extension that enables a direct integration to a laboratory control software Qudi [9].

Research and Design of Hybrid Optimized Backpropagation (BP) Neural Network PID Algorithm for Integrated Water and Fertilizer Precision Fertilization Control System for Field Crops

China’s field crops such as cotton, wheat, and tomato have been produced on a large scale, but their cultivation process still adopts more traditional manual fertilization methods, which makes the use of chemical fertilizers in China high and causes waste of fertilizer resources and ecological environmental damage. To address the above problems, a hybrid optimization of genetic algorithms and particle swarm optimization (GA–PSO) is used to optimize the initial weights of the backpropagation (BP) neural network, and a hybrid optimization-based BP neural network PID controller is designed to realize the accurate control of fertilizer flow in the integrated water and fertilizer precision fertilization control system for field crops. At the same time, the STM32 microcontroller-based precision fertilizer application control system for integrated water and fertilizer application of large field crops was developed and the performance of the controller was verified experimentally. The results show that the controller has an average maximum overshoot of 5.1% and an average adjustment time of 68.99 s, which is better than the PID and PID control algorithms based on BP neural network (BP–PID) controllers; among them, the hybrid optimization of PID control algorithm based on BP neural network by particle swarm optimization and genetic algorithm(GA–PSO–BP–PID) controller has the best-integrated control performance when the fertilizer application flow rate is 0.6m3/h.

Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks

Satellite network is considered a prominent architecture for future space-air-ground integrated networks, where the relay satellite network (RSN) plays an essential role in data transmission. However, the relay satellite network faces practical challenges. First, due to the dynamic network topology and limited resources such as storage capacity, antennas, and bandwidth, optimizing data transmission and resource allocation in a resource-constrained stochastic system is challenging. Second, satellites intermittently enter the eclipse zone, which leads to an unstable energy supply. Therefore, optimal energy management is required to balance energy supply and consumption. In this paper, we proposed an online optimal control algorithm, based on Lyapunov stability theory, to optimize data transmission and energy management jointly. Furthermore, the performance of our proposal is analyzed comprehensively, including the reasonable bounds for data and energy and the optimality of our proposed algorithm. Finally, extensive performance evaluation simulations are performed based on real-world satellite parameters. The simulation results indicate that the proposed method can achieve long-term network stability and energy sustainability. Moreover, the proposed algorithm shows an improvement of 9.6% and 20.3% in system utility compared to the non-optimized transmission method and non-optimized energy management algorithm, respectively.

Active balancing control for distributed battery systems based on cooperative game theory

For the smooth integration of renewable and volatile energy sources in the electricity grid, there is a need for scalable and efficient storage systems. Stationary battery systems provide the required efficiency but lack the scalability to meet varying requirements. In this article, we present a novel distributed control approach for active balancing of battery cells facilitating a scalable and flexible battery system design. For the proposed algorithm, the topology of the underlying battery system is assumed to be not predefined and can change at runtime. In such a system, each battery cell may has its own power electronics and processing unit which are used to manage the control actions for active balancing on cell level. Thus, the battery system is objected as a multi agent system (MAS) with battery cells as agents. Each agent locally computes and optimises its own control actions in a leaderless MAS setup based on cooperative bargaining games and the Nash solution. Each cell is required to find a single partner or multiple partners to form coalitions and exchange energy for active balancing. The working principle and capability of the distributed control algorithm is evaluated by simulations and compared to a passive balancing approach and a central optimal control algorithm.

Inverse optimal control for autonomous carrier landing with disturbances

This work addresses the optimal control of carrier landing affected by deck motion and airwake disturbances. Starting from aircraft dynamics written under an affine form, a novel inverse optimal control approach is designed and then used as the main control technique to provide fast and accurate tracking of the aircraft reference trajectory, as well as to improve the convergence performances and the success rate of the automatic carrier landing. A robust automatic control landing system is proposed by designing a novel inverse optimal control algorithm based on disturbance observers (for the estimation and compensation of airwake), a deck motion compensation block, and fixed-time command filters for computing the reference signals and reducing the tracking errors. The proposed optimal scheme includes a guidance controller, an attitude controller (involving the control of the heading angle, attitude angles, and angular rates), and an approach power compensation system. The proposed architecture is proved to be globally stable by using the Lyapunov theory. Finally, the comparative simulation results prove both the reliability of the control scheme and the superiority of the novel inverse optimal control technique over other nonlinear control approaches employed in the design of automatic control landing systems.

Heating System Control with Neural Network

Building performance has significant impact on humans’ life and ecology as buildings account for 39% of total greenhouse gas emissions and consume about 40% of total global energy consumption. Smart building control is one of the key points to archive high energy efficiency. Each year, the complexity of building state control grows due to the increase in the number of controlled elements that are used to achieve better indoor climate. Therefore, in the manual analysis and implementation of the building control program, an error can easily appear due to the human factor. Artificial intelligence (AI) algorithms could be used as an alternative solution as they could evaluate building dynamics independently. One of strategies for automatic building control adaptation to its dynamic is model based predictive control where neural network is used for different control strategies evaluation. Performance of such control technique is highly dependent on control strategies evaluation accuracy. To achieve top accuracy, several hyperparameters of neural network could be tuned as well as data set for specific construction must be prepared. Preparation of data set could be a problem as random control of building for generation of dataset could be not unacceptable for building users as well as it could damage construction.In this paper authors process optimization of experimental building heating system control algorithm to achieve smaller fluctuations of temperature indoors. For dataset generation were used several data from weather station as well as heating system parameters and temperature indoors while building was controlled by thermostat with build in PID regulation. For evaluation of building dynamics were used temporal convolutional neural network. To achieve high accuracy results of control strategies evaluation, several hyperparameters of neural network were tested. Finally resulting model were tested on physical building. Results indicate that in some cases developed control model could prevent temperature fluctuations which could be caused by limits of heating system power.

Optimal consensus control for multi‐agent systems: Multi‐step policy gradient adaptive dynamic programming method

AbstractThis paper presents a novel adaptive dynamic programming (ADP) method to solve the optimal consensus problem for a class of discrete‐time multi‐agent systems with completely unknown dynamics. Different from the classical RL‐based optimal control algorithms based on one‐step temporal difference method, a multi‐step‐based (also call n‐step) policy gradient ADP (MS‐PGADP) algorithm, which have been proved to be more efficient owing to its faster propagation of the reward, is proposed to obtain the iterative control policies. Moreover, a novel Q‐function is defined, which estimates the performance of performing an action in the current state. Then, through the Lyapunov stability theorem and functional analysis, the proof of optimality of the performance index function is given and the stability of the error system is also proved. Furthermore, the actor‐critic neural networks are used to implement the proposed method. Inspired by deep Q network, the target network is also introduced to guarantee the stability of NNs in the process of training. Finally, two simulations are conducted to verify the effectiveness of the proposed algorithm.

A New Optimization Control Policy for Fuzzy Vehicle Suspension Systems Under Membership Functions Online Learning

Active vehicle suspension systems play a crucial role in isolating vibration between the car body and pavement. In this article, based upon Takagi–Sugeno (T–S) fuzzy model, a novel optimization control algorithm is first proposed to furthest improve driving comfort level for fuzzy suspension systems compared with traditional existing approach. First, sufficient criteria for designing non parallel distribution compensation (non-PDC) fuzzy controller to improve driving comfort level and guarantee corresponding suspension constrained requirements are presented. Then, in the context of ensuring stability of studied systems, considering the feasible areas of membership functions (MFs) of non-PDC controller, a new MFs online optimization strategy is built for fuzzy suspension systems for the first time. By this proposed novel optimization algorithm, the values of controller MFs (CMFs) can be updated in real-time so as to obtain a better driving comfort level while improving suspension-constrained requirements. Sufficient criteria is obtained to guarantee the convergence of the designed cost function with the help of the Lyapunov stability theory. In the end, the usefulness, as well as superiority of the proposed online optimization strategy, are illustrated by contrastive verification.