Optimal Control Rules Research Articles

A day-ahead operation regulation method for solar water heating based on model predictive control

The operation performance of a solar heating system is largely influenced by the coupling of outdoor climate conditions and building energy operation patterns, and is characterized by strong randomness and large heating fluctuations, which makes the optimal control rules for solar heating systems in dynamic state. Although the traditional RBC(rule-based control), whose rules is preset, has a fast response speed, the fixed-rules are hardly to make the solar heating system in optimal state. Furthermore, solar heating systems is generally characterized by a large time delay in the heat transfer process. The control point lag occurs in the control signal of the dynamic control strategy. To address these issues, this paper proposes a model predictive control-based (MPC) control strategy for solar heating systems. The method uses seq2seq-LSTM to predict major operating parameters including ambient temperature, solar radiation, the load inside the building for the next 24 h and combines the heating system operation model to simulate and obtain the control signal at the next moment. The results showed that, compared with traditional control strategies including RBC and PID control strategies, the efficiency of solar collector regulated by MPC increased by 9%. Taken the tank temperature at the simulation end as the energy calculation reference point, the solar heating system’s total energy consumption employing MPC is 34.2% lower than that employing traditional control strategy.

Cybersecurity threats mitigation in Internet of Vehicles communication system using reliable clustering and routing

Security and privacy while considering vehicular networks are important research topics. Appropriate clustering algorithms enable long-distance communications in highly dynamic Internet of Vehicles (IoV) systems. IoV system QoS requires constant, low-cost clustering. However, to prevent malicious behaviors, sensitive vehicle networks need an access control system for data security and privacy. The existing solutions suffered from higher complexity and complete reliability. Thus, the main objective of this paper is to propose a lightweight routing technique to express network access control policies to ensure higher reliability with minimum latency and overhead. The Trust Aware Clustering-based Routing Protocol (TACR) solves IoV communication security and reliability issues while reducing computational costs and latency. TACR functionality relies on trust-management methods for optimal Cluster Head (CH) and relay (data forwarder) selection. Each car's direct and indirect trust scores are calculated during clustering. The fitness function of the Ant Colony Optimisation (ACO) algorithm uses each vehicle's hybrid trust value. The ACO algorithm chooses stable vehicles for the best CH. In the routing phase, we select forwarding relays and apply trust-based access control rules to ensure safe and reliable data transmission. Fuzzy logic selects the optimal relay node and sets access control rules based on trust evaluation. The trust criteria consider each vehicle's expertise, knowledge, and suggestions throughout. The simulation findings reveal that the TACR protocol outperforms state-of-the-art protocols significantly. Average throughput and PDR are increased by 14.17 % and 14.34 % using TACR, respectively. Average delay and communication overhead were reduced by 33.84 % and 14.28 % using the TACR protocol, respectively.

A comprehensive functional form of the optimal harvest control rule for multiple fishery management objectives

For many of the world's fisheries, harvest control rules (HCRs) are the main tools for supporting decision-making. We previously clarified the optimal shape of the HCR to achieve multiple fisheries management objectives (maximising average catch, reducing variation in yields, and avoiding stock collapse) and ensure robustness to estimation errors in biomass by numerically estimating the optimal values of the 21 biological reference points (BRPs) comprised in the HCR. However, for actual management, a simple but comprehensive functional form to emulate the optimal HCR is desirable, as numerical HCR optimisation with many BRPs is time-consuming. Here, we introduced three objective utility functions ( U1– U3) representing HCR performance for composite management objectives: mean–variance utility functions, where the performance indicator for variation in yields is the standard deviation ( U1) or the annual average variance ( U2) of yields, and the constant relative risk aversion utility function ( U3). We derived two equations to emulate the optimal HCRs with three adjusting parameters corresponding to the management objectives and different magnitudes of estimation errors. These equations will help stakeholders discuss desired management strategies by showing expected catch and risk by adjusting the parameter values.

Electrical, thermal and degradation modelling of PEMFCs for naval applications

This article presents a semi-empirical model of Proton Exchange Membrane Fuel Cell (PEMFC), combining electrical, thermal and ageing phenomena. This modelling approach links activation and diffusion phenomena in order to create a new PEMFC model, close to reality in steady state and enabling fast simulation (since the dynamics are of first order). The purpose of this model is to enable energy management studies to optimise the design, lifetime and consumption of a PEMFC system. The degradation model is defined and fitted according to a PEM fuel cell datasheet. The design aims to study fault-tolerant multi stack Fuel Cell (MFC) systems, to consider their interactions with power converters and to design optimal control and adaptive management rules. The degradation model helps to define management rules according to the state of health and to the degradation speed of each stack. This work also presents an analysis of the existing models available in the literature and more specifically models compatible with the needs of Modular Fuel Cells (MFC) studies.

OPTIMAL TRACKING CONTROL FOR ROBOT MANIPULATORS WITH ASYMMETRIC SATURATION TORQUES BASED ON REINFORCEMENT LEARNING

This paper introduces an optimal tracking controller for robot manipulators with asymmetrically saturated torques and partially - unknown dynamics based on a reinforcement learning method using a neural network. Firstly, the feedforward control inputs are designed based on the backstepping technique to convert the tracking control problem into the optimal tracking control problem. Secondly, a cost function of the system with asymmetrically saturated input is defined, and the constrained Hamilton-Jacobi-Bellman equation is built, which is solved by the online reinforcement learning algorithm using only a single neural network. Then, the asymmetric saturation optimal control rule is determined. Additionally, the concurrent learning technique is used to relax the demand for the persistence of excitation conditions. The built algorithm ensures that the closed-loop system is asymptotically stable, the approximation error is uniformly ultimately bounded (UUB), and the cost function converges to the near-optimal value. Finally, the effectiveness of the proposed algorithm is shown through comparative simulations.

Accelerating Optimal Control Strategy Generation for HVAC Systems Using a Scenario Reduction Method: A Case Study

Learning an optimal control strategy from the optimized operating dataset is a feasible way to improve the operational efficiency of HVAC systems. The operation dataset is the key to ensuring the global optimality and universality of the operation strategy. Currently, the model-based method is commonly used to generate datasets that cover all operating scenarios throughout the cooling season. However, thousands of iterative optimizations of the model also lead to high computational costs. Therefore, this paper proposed a scenario reduction method in which similar operating scenarios were grouped into clusters to significantly reduce the number of optimization calculations. First, k-means clustering (with dry-bulb temperature, wet-bulb temperature, and cooling load as features) was used to select typical scenarios from operating scenarios for the entire cooling season. Second, the model-based optimization was performed with the typical scenarios to generate the optimal operating dataset. Taking a railway station in Beijing as a case study, the results show that the optimization time for the typical scenarios was only 1.4 days, which was reduced by 93.1% compared with the 20.6 days required to optimize the complete cooling season scenario. The optimal control rules were extracted, respectively, from the above datasets generated under the two schemes, and the results show that the deviation of energy saving rate was only 0.45%. This study shows that the scenario reduction method can significantly speed up the generation of the optimal control strategy dataset while ensuring the energy-saving effect.

An Intelligence-Defined Networking Architecture With Importance-Based Network Resource Control

As network complexity increases because of the diversification of applications and services based on Internet of Autonomous Things (IoAT), it is difficult for humans to design the optimal control rule for software-defined networking (SDN) controllers. Intelligence-defined networking, called IDN, is proposed to overcome this limitation through machine learning (ML) algorithms. Since the existing IDN approaches are mostly designed to optimize only the network Quality of Services (QoS), including throughput, jitter, and latency, the controllers do not consider the importance of data in the applications and the services. This causes the controller to allocate insufficient resources to the crucial data flow, which leads critical problems, such as self-driving car accidents. To prevent this problem, we propose an importance-based IDN architecture that enables network controllers to manage network traffic with an importance levels (ILs) of data flows. First, we devise an importance estimation scheme to set the IL for the flows. Second, a dynamic resource allocation model of the controllers is developed by means of deep learning algorithms in order to make the optimal network resource. Additionally, an online learning mechanism based on weighted auto-labeling is adopted to continue enhancing the adaptability of the resource allocation model on runtime as the network conditions change. The evaluation results of the proposed architecture under various autonomous things scenarios show that the loss rate for data flows of higher importance is reduced by one-quarter compared to the case of a network controller without IL and that the bandwidth waste ratio is reduced by 10% compared to the rule-based model.

Performance of the Machine Learning on Controlling the Pneumatic Suspension of Automobiles on the Rigid and Off-Road Surfaces

To enhance the ride comfort and control performance of the semi-active pneumatic suspension system (PSS) of automobiles on the different road surfaces, a machine learning method (MLM) developed on the optimal control rules of the fuzzy logic control is proposed for the semi-active PSS. A nonlinear dynamic model of the automobile with eight degrees of freedom (DOF) is established to compute the results. The root mean square (RMS) accelerations of the vertical driver’s seat and the pitching angle and rolling angle of the automobile are selected to evaluate the ride comfort of the automobile on the rigid road and off-road terrain surfaces. The research results show that the off-road terrain surfaces remarkably affect the ride comfort of the automobile, especially at a high moving speed range of the automobile over 17.5 m/s. The performance of the MLM in improving the ride comfort of the automobile is better than the fuzzy logic control under various simulation conditions. Particularly, the RMS accelerations of the vertical driver’s seat and the pitching angle and rolling angle of the automobile with the MLM are smaller than that of the fuzzy logic control by 14.6%, 9.6%, and 5.3% on the rigid road surfaces and reduced by 14.9%, 8.7%, and 9.8% on the soil terrain of off-road terrain surfaces, respectively. However, the research results also indicate that the performance of the MLM significantly depends on the data map of the learning process. Thus, to further enhance the performance of the MLM, the data map for the machine learning process should be expanded under different operating conditions of the automobile.

A modified PSO Algorithm based on Cloud Theory for optimizing the Fuzzy PID controller

To obtain a more efficient intelligent controller, this paper presents a modified cloud theory-based particle swarm optimization (CTPSO) algorithm to improve fuzzy PID controller. Cloud evolution and mutation methods based on cloud theory are introduced to tune inertia weight of particle swarm optimization algorithm (PSO), which can improve the optimization accuracy and speed of PSO. The comparative experiments indicate that CTPSO performs better than PSO and other optimization algorithm recently proposed by other researchers. The CTPSO is used to set the initial PID control parameters and optimize control rules of fuzzy PID controller. According to an engineering case of the 180°C-die heater’s temperature control, the novel optimized fuzzy PID controller can suppress the oscillation and overshoot significantly. It also owns smaller adjustment time, static error and better comprehensive performance compared with the initial controller.

Online self-adaptive proportional-integral-derivative control for brushless DC motor based on variable universe fuzzy inference system optimized by genetic algorithm

In this study, we propose a novel robust online self-adaptive Proportional-Integral-Derivative (PID) control design for Brushless DC Motor (BLDCM) speed system under different operating conditions. The online adaptive tuning for PID parameters is realized accurately by optimizing the control rules of variable universe fuzzy inference with a modified genetic algorithm (GA). Based on the variable fuzzy inference theory, the method of solving contraction–expansion factor in real-time through fuzzy inference is proposed. Furthermore, the process to optimize two inference rules by GA is improved to get optimal control rules for adjusting PID parameters. Finally, multiple sets of simulations and experiments are conducted to validate the proposed controller in different conditions by building Simulink models and setting up experiment platforms. The results of this study not only demonstrate the effectiveness of the proposed controller but also provide technical suggestions for the speed control of BLDCM.

Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition

The rapid development of cloud techniques like Vehicle-to-Cloud (V2C) makes it possible to gather more information and develop computationally efficient energy management systems (EMS) for electric vehicles. This paper proposes a novel EMS with low computational cost targeting hybrid battery/ultracapacitor electric buses to reduce energy consumption and battery life degradation. In the offline training process, by applying the K-means clustering method with 10 selected features, 16 typical driving conditions are classified. For each driving condition, dynamic programming is employed offline to generate global optimal results, which are then used in control rule extraction for online operation. During the online operation, the proposed EMS executes the designed driving pattern recognition algorithm with V2C assistance to select optimal control rules. The simulation results indicate that the proposed EMS effectively decreases the battery degradation and energy consumption cost by 13.89%, compared with the conventional rule-based strategy. In addition, it is shown that V2C assistance leads to a 6.81% lower cost. Besides, the robustness of the proposed EMS is validated by testing the EMS with highly randomized input with uncertainties up to 15% and long duration of V2C data packet loss up to 10 s.

Optimal LQG controller to adjust the rudder supplying water to the turbine of small and medium hydro power plants

Introduction: This paper is the result of the research “Optimal LQG controller to open rudder supplying water to turbine of small and medium hydro power plants” developed in the Electric Power University in Vietnam in 2019.
 Problem: To maintain the frequency of the emitted voltage of the generator at the nominal value of 50 Hz, the authors present a solution to apply the optimal control theory to create a command to control the rudder angle to adjust the water flow into the turbine.
 Objective: The article presented about the order value formation algorithm, to stabilize the frequency of transmission voltage at 50 Hz standard value.
 Methodology: In this paper, the laws to control the rudder supplying water to turbine of small and medium hydro power plants are synthesized by optimal control theory. To establish optimal control rules, the paper proposes using the Kalman filter to estimate the state of the object. By that, the frequency of generated voltage will be stabilized under changing load. The efficacy of the steady and dynamic performance of the control strategy was verified using Matlab/Simulink software.
 Results: The proposed system can compensate for power fluctuations and is effective in terms of power regulation.
 Conclusion: The algorithm presented in the paper is the basis for setting up the software when when designing and manufacturing the turbine - generator combination. Applying this algorithm, the process of adjusting the transmitted power according to the required load will be performed with quality.
 Originality: This paper’s contribution lies in its employment of an effective optimal LQG control for varying operating conditions.
 Limitations: The authors need to spend more time to the development and and test this algorithm and and implementing it in practice.

Sizing and control rules of dedicated mechanical subcooler in transcritical CO2 booster systems for commercial refrigeration

Use of Dedicated Mechanical Subcoolers gives rise to the need of an optimisation process involving gas cooler pressure, activation and setpoint temperature values, subcooling degree and subcooler size, with the aim of further improving the energy efficiency while considering costs. In this paper a thermoeconomic analysis is performed on a commercial refrigeration plant, at four climate conditions from warm to hot, with time-dependent refrigerating load depending on the location, resulting in some design and control rules for the DMS. In terms of energy efficiency, the size of the DMS appears to be more crucial at hot climate conditions (difference up to 3.5% in energy saving), with sizes ranging from 35% to 45% of the total cooling capacity. Optimal control rules should be preferentially adopted at mild-warm conditions, suggesting to exploit the highest subcooling rate available. The economic analysis shows that energy use is the most important cost item.

Offline Optimization of Sluice Control Rules in the Urban Water System for Flooding Mitigation

A real-time control (RTC) system can substantially improve the efficiency in urban flooding mitigation by optimizing the capacity of drainage and storage in existing drainage systems. However, few studies have investigated the RTC for an Urban Water System (UWS) in cities with a high water surface ratio for flooding mitigation. In this study, control rules of the actuators in an UWS were developed by an offline optimization system that combines a hydraulic model, the stormwater management model (SWMM) with an optimization model solved by the differential evolution (DE) algorithm. Iteratively simulating only the downstream UWS hydraulic model, the objectives of this study were to (i) minimize the flooding volume from the UWS and (ii) minimize the cumulative water depths above the control lines in real-time. Results showed that the optimal control rules outperformed the current fully open and closed rules, indicating the UWS’s retaining and draining capacity was effectively utilized through the offline optimization. This study also found that the robust control rules might be biased and have little effect under most rainfalls, especially mild storms, as they are derived from the system’s average performance under various rainfall conditions.

A novel optimal power management strategy for plug-in hybrid electric vehicle with improved adaptability to traffic conditions

Adaptability to various driving conditions (TCs) is one of the essential indicators to assess the optimality of power management strategies (PMSs) of plug-in hybrid electric vehicles (PHEVs). In this study, a novel optimal PMS with the improved adaptability to TCs is proposed for PHEVs to achieve the energy-efficient control in momentary scenarios by virtue of advanced internet of vehicles (IoVs), thus contributing to remarkable promotion in fuel economy of PHEV. Firstly, the optimal control rules in the novel PMS, corresponding to diverse driving conditions, are optimized offline by the chaotic particle swarm optimization with sequential quadratic programming (CPSO-SQP), which can effectively endow the global optimization knowledge into the rule inspired method. Then, an online TC identification (TCI) method is designed by cooperatively exploiting multi-dimensional Gaussian distribution (MGD) and random forest (RF), where the MGD based analysis on the macrocosmic state of traffic contributes to valuable inputs for the RF based TC classification, and additionally the super regression ability of RF further improves the identification accuracy. Finally, the numerical simulation validations showcase that the novel optimal PMS can reasonably and instantly manage the power flow within power sources of PHEV under different TCs, manifesting its anticipated preferable controlling performance.

A novel cascade approach to control variables optimisation for advanced series-parallel hybrid electric vehicle power-train

In recent years, road vehicles are being increasingly equipped with hybrid electric power-trains in order to provide significant gains in fuel economy and reductions in greenhouse gases emissions. Since hybrid power-trains consist of two or more different energy sources, many of their variants are present nowadays which leads to many open questions in terms of hybrid electric power-train structure selection, components sizing and energy management control, which all have influence on the power-train purchase cost and efficiency. The control variables optimisation is crucial in order to find the set of optimal control rules for different power-train operating regimes which would yield the minimum possible fuel consumption. Among different control variable optimisation methods, the dynamic programming approach is usually used in literature, because of its unique feature to find the global optimum solution with a certain degree of precision. However, this optimisation method also requires significant computing power and its application is limited to low-order systems. Having this in mind, this paper evaluates the benefits of innovative cascade approach to hybrid electric vehicle control variable optimisation wherein dynamic programming is combined with a gradient-based optimisation algorithm in a systematic and a straightforward way in order to significantly reduce the optimisation execution time and also to increase the precision of the globally-optimal result.

Multi-mode energy management strategy for hydraulic hub-motor auxiliary system based on improved global optimization algorithm

Heavy commercial vehicles equipped with a hydraulic hub-motor auxiliary system (HHMAS) often operate under complex road conditions. Selecting appropriate operating mode and realizing reasonable energy management to match unpredictable road conditions are the keys to the driving performance and fuel economy of HHMAS. Therefore, a multi-mode energy management strategy (MM-EMS) based on improved global optimization algorithm is proposed in this study for HHMAS. First, an improved dynamic programming (DP) algorithm for HHMAS is developed. This improved DP algorithm considers the effect of SOC and vehicle speed, thereby preventing the calculation results from falling into local optimization. This algorithm also reduces the dimension of the control variable data grid, and the calculation time is reduced by 35% without affecting the accuracy. Second, a MM-EMS with hierarchical control is proposed. This strategy extracts the optimal control rules from the results of the improved DP algorithm. Then it divides the system’s operating region into two types, namely, single-mode working region and mixedmode working region. In the single-mode working region, mode switching is realized through fixed thresholds. In the mixedmode working region, a linear quadratic regulator (LQR) is adopted to determine a target mode and realize SOC tracking control. Finally, the designed MM-EMS is verified separately in offline simulation and hardware-in-the-loop (HIL) under actual vehicle test cycles. Simulation results show that the results between HIL and offline simulation are largely coincidence. Besides, in comparison with the engine optimal control strategy, the designed MM-EMS can achieve an approximate optimal control, with oil savings of 3.96%.

Integrated energy-oriented cruising control of electric vehicle on highway with varying slopes considering battery aging

Eco-driving strategies for vehicles with conventional powertrains have been studied for years and attempt to reduce fuel consumption by optimizing the driving velocity profile. For electric vehicles (EVs) with regenerative braking, the speed profile with the best energy efficiency should be different from conventional vehicles. This paper proposes an energy-oriented cruising control strategy for EVs with a hierarchical structure to realize eco-cruising on highways with varying slopes. The upper layer plans the energy-optimized vehicle velocity, and the lower layer calculates the torque allocation between the front and rear axles. However, the resulting speed profile with varying velocity may cause a high charge and discharge rate of the battery, resulting in rapid battery fading. To extend the battery life, we make a tradeoff between the energy consumption and wear of the battery by formulating an optimal control problem, where driving comfort and travel time are also considered. An indirect optimal control method is implemented to derive the optimal control rule. As an extension, the control rule for avoiding rear-end collisions is presented and simulated for driving in the real world.

Is timing of reproduction according to temperature sums an optimal strategy?

Temperature sums are widely used to predict the seasonal timing of yearly recurring biological events, such as flowering, budburst, and hatching. We use a classic energy allocation model for annual plants to compare a strategy for reproductive timing that follows a temperature sum rule (TSR) with a strategy that follows an optimal control rule (OCR) maximizing reproductive output. We show that the OCR corresponds to a certain TSR regardless of how temperature is distributed over the growing season as long as the total temperature sum over the whole growing season is constant between years. We discuss such scenarios, thus outlining under which type of variable growth conditions TSR maximizes reproductive output and should be favored by natural selection. By providing an ultimate explanation for a well‐documented empirical pattern this finding enhances the credibility of temperature sums as predictors of the timing of biological events. However, TSR and OCR respond in opposite directions when the total yearly temperature sum changes between years, representing, for example, variation in the length of the growing season. Our findings have implications for predicting optimal responses of organisms to climatic changes and suggest under which conditions natural selection should favor photoperiod versus temperature control.

A Dual-Mode Energy Management Strategy Considering Fuel Cell Degradation for Energy Consumption and Fuel Cell Efficiency Comprehensive Optimization of Hybrid Vehicle

The energy management strategy is the key to achieve fuel cell hybrid system operation with high-efficiency and low-energy cost. The widely used energy management strategies are established based on the fixed models of power sources energy consumption and efficiency. However, due to the gradual performance degradation of the fuel cell during the service period, some model parameters will change significantly and the fixed models will become increasingly inaccurate over time. Therefore, this paper established time-varying models of power sources energy consumption and efficiency by introducing the fuel cell degradation rate. On this basis, in order to reduce the energy consumption of the system, improve the fuel cell efficiency and relatively maintain the SOC level, a novel dual-mode energy management strategy, DMDEE, for hybrid vehicles was proposed. In order to improve the online operation speed of proposed energy management strategy, the optimal control rules were obtained by offline calculation first. Then through comparison experiments with other two energy management strategies, PMP and PF, it was verified that the proposed strategy can effectively reduce the system energy consumption and improve the efficiency of the fuel cell system, meanwhile could make the SOC regress after its deviating from ideal working area. In addition, the mode switching hysteresis control was also validated. Therefore, the effectiveness and superiority of the proposed energy management strategy in this paper has been verified.