Real-time Optimal Control Research Articles

Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain

Real-time energy management strategy (EMS) plays an important role in reducing fuel consumption and maintaining power for the hybrid electric vehicle. However, real-time optimization control is difficult to implement due to the computational load in an instantaneous moment. In this paper, an Approximate equivalent consumption minimization strategy (Approximate-ECMS) is presented for real-time optimization control based on single-shaft parallel hybrid powertrain. The quadratic fitting of the engine fuel consumption rate and the single-axle structure characteristics of the vehicle make the fitness function transformed into a cubic function based on ECMS for solving. The candidate solutions are thus obtained to distribute torque and the optimal distribution is got from the candidate solutions. The results show that the equivalent fuel consumption of Approximate-ECMS was 7.135 L/km by 17.55% improvement compared with Rule-ECMS in the New European Driving Cycle (NEDC). To compensate for the effect of the equivalence factor on fuel consumption, a hybrid dynamic particle swarm optimization-genetic algorithm (DPSO-GA) is used for the optimization of the equivalence factor by 9.9% improvement. The major contribution lies in that the Approximate-ECMS can reduce the computational load for real-time control and prove its effectiveness by comparing different strategies.

Comparative assessment of alternative MPC strategies using real meteorological data and their enhancement for optimal utilization of flexibility-resources in buildings

Model predictive control (MPC) method shows superiority in enhancing system performance. But its actual performance in operation is hampered by forecast uncertainties. Different methods have been proposed to mitigate the impacts of these uncertainties, such as shrinking horizon MPC (SHMPC) and stochastic MPC (SMPC). However, the year-round performance of these methods has rarely been investigated, and little is known about their relative performance, particularly in utilization of building flexibility-resources. In this study, the year-round performance of conventional MPC (CMPC), SHMPC and SMPC strategies when used to optimize utilization of flexibility-resources in buildings with PV power generation and battery storage systems are compared using real-time optimal control (RTC) as the benchmark, and their enhancement is ultimately proposed. Forecast uncertainties are quantified based on real meteorological data. Different optimization horizon start times and shrinking intervals are tested. Results show that there is no one control strategy which is superior to the other strategies under all operating conditions. The SMPC strategy has a higher probability of achieving daily cost saving than CMPC and SHMPC strategies, but still has higher daily costs than the RTC strategy in the winter. Hybrid control strategies with proper control schemes implemented under different operating conditions are therefore recommended.

Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence

In recent years, due to the strengthening of our country’s comprehensive strength, the rapid development of science and technology and artificial intelligence has also attracted people’s attention. Artificial intelligence is a highly applicable subject, which has very good applications in power systems. In the experiment, the open circuit voltage method and the ampere-hour integration method are used to estimate the SOC of the lithium battery and the particle swarm energy management algorithm is used to allocate the output power of the fuel cell and the lithium battery. The particle swarm algorithm module calls the dual source hybrid power system module through the sim function to convert the actual value input in the system into a fuzzy quantity suitable for fuzzy control. The energy management strategy based on particle swarm optimization and fuzzy control was tested based on working conditions under the comprehensive test bench. Finally, the matching of the hybrid system is analyzed from the structure, component parameters, control strategy, and driving cycle of the vehicle. The experimental data show that the total fuel consumption of the three sets of experiments is averaged to get a fuel consumption rate of 26.3 m3/100 km for the hybrid city bus under the optimized energy management strategy. The results show that the real-time energy management strategy based on particle swarm algorithm can significantly improve the real-time performance of traditional instantaneous energy management strategies while reducing fuel consumption.

Research on the Application of Artificial Intelligence Technology in Power System Intelligent Dispatching Automation

From the perspective of meeting the power quality requirements of users, the article analyses the characteristics of traditional voltage and reactive power control mode and the regional power grid reactive voltage optimization centralized closed-loop control mode (AVC system) based on the dispatch automation system (SCADA/EMS) from the perspective of technical management. Combining the reactive power/voltage real-time optimization control model, a real-time optimization control method of the regional power grid based on the improved differential evolution algorithm is proposed. The particle swarm algorithm is combined with the characteristics of reactive power/voltage control to improve the initial particle quality, reduce the optimization space, and introduce a crossover operator to improve the calculation speed and efficiency of the algorithm. Taking an actual regional power grid as an example, the simulation calculation of reactive power/voltage real-time optimization is carried out. The results show that the proposed algorithm and control strategy are feasible and effective.

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

Optimal adaptive computed torque control for haptic-teleoperation system with uncertain dynamics

This study aimed to eliminate dynamic uncertainty, one of the main problems of haptic teleoperation robotic systems. The optimal adaptive computed torque control method was used to overcome this problem. As is known, excellent stability and transparency are required in teleoperation systems. However, dynamic uncertainty that causes stability problems in the control of these systems also causes poor performance. In conventional adaptive computed torque control methods, updating the parameters of the system is generally discussed, but updating the control coefficients of vital importance in the control of the system is not considered. In the proposed method, an adaptation rule has been created to update uncertain parameters. In addition, the gray wolf optimization algorithm, one of the current optimization algorithms, has been proposed and applied to obtain the control coefficients of the system. The position tracking stability of the system was examined by using Lyapunov stability analysis method. As a result, both simulation and real-time optimal adaptive computational torque control method were used and bilateral position and force control was performed and the performance results of the system are obtained graphically and examined. Optimal adaptive computed torque control method obtained using the gray wolf optimization algorithm was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of both simulation and real time with the optimal adaptive computed torque control method.

Model predictive control strategy of head rice yield in paddy rice intermittent drying

This paper proposes a paddy rice model predictive control (MPC) drying strategy to achieve the real-time rolling optimization control of paddy rice intermittent drying process for the maximization of head rice yield (HRY). In the rolling optimization process, the adjusted evaluation index of predicted HRY ( AEHRY ) is used as the performance index to calculate the optimal control law in real-time. By comparing the simulation results of proposed drying strategies, the optimal drying strategy was chosen. Intermittent drying experiments of proposed MPC drying strategy were carried out to evaluate its performance. Experimental results showed that the proposed MPC drying strategy had the greatest AEHRY and HRY. In terms of Nanjing 9108 with initial moisture content of 22.88% wet basis, the average AEHRY s of 50 °C, 55 °C, 60 °C constant temperature drying, and MPC drying were 0.0773, 0.2130, 0.2283 and 0.3147, with average HRYs of 0.7121, 0.7129, 0.7163 and 0.7202, respectively. The feasibility of the proposed paddy rice MPC drying strategy was proved. The paddy rice intermittent drying processes under different application conditions have different drying requirements and restrictions. By modifying constraint part of the proposed MPC drying strategy, the ranges of drying parameters and the relationship during the drying parameters can be constrained to realize the real-time online optimization of these intermittent drying processes.

A real-time optimization control method for coagulation process during drinking water treatment

A real-time optimization control method for coagulation process during drinking water treatment

Based on minimum fuel consumption mode integrated optimal control of fixed-wing UAV flight propulsion system

Purpose In this paper, with the goal of reducing the fuel consumption of UAV, the engine performance optimization is studied and on the basis of aircraft/engine integrated control, the minimum fuel consumption optimization method of engine given thrust is proposed. In the case of keeping the given thrust of the engine unchanged, the main fuel flow of the engine without being connected to the afterburner is optimally controlled so as to minimize the fuel consumption. Design/methodology/approach In this study, the reference model real-time optimization control method is adopted. The engine reference model uses a nonlinear real-time mathematical model of a certain engine component method. The quasi-Newton method is adopted in the optimization algorithm. According to the optimization variable nozzle area, the turbine drop-pressure ratio corresponding to the optimized nozzle area is calculated, which is superimposed with the difference of the drop-pressure ratio of the conventional control plan and output to the conventional nozzle controller of the engine. The nozzle area is controlled by the conventional nozzle controller. Findings The engine real-time minimum fuel consumption optimization control method studied in this study can significantly reduce the engine fuel consumption rate under a given thrust. At the work point, this is a low-altitude large Mach work point, which is relatively close to the edge of the flight envelope. Before turning on the optimization controller, the fuel consumption is 0.8124 kg/s. After turning on the optimization controller, you can see that the fuel supply has decreased by about 4%. At this time, the speed of the high-pressure rotor is about 94% and the temperature after the turbine can remain stable all the time. Practical implications The optimal control method of minimum fuel consumption for the given thrust of UAV is proposed in this paper and the optimal control is carried out for the nozzle area of the engine. At the same time, a method is proposed to indirectly control the nozzle area by changing the turbine pressure ratio. The relevant UAV and its power plant designers and developers may consider the results of this study to reach a feasible solution to reduce the fuel consumption of UAV. Originality/value Fuel consumption optimization can save fuel consumption during aircraft cruising, increase the economy of commercial aircraft and improve the combat radius of military aircraft. With the increasingly wide application of UAVs in military and civilian fields, the demand for energy-saving and emission reduction will promote the UAV industry to improve the awareness of environmental protection and reduce the cost of UAV use and operation.

Energy Management Strategy of a Hybrid Power System Based on V2X Vehicle Speed Prediction.

Energy consumption in vehicle driving is greatly influenced by traffic scenarios, and the intelligent traffic system (ITS) has a key role in solving the real-time optimal control of hybrid vehicles. To this end, a new energy management control strategy based on vehicle-to-everything (V2X) communication for vehicle speed prediction was proposed to dynamically adjust the engine and motor power output according to the traffic conditions. This study is based on intelligent network connectivity technology to obtain forward traffic state data and use a deep learning algorithm to model vehicle speed prediction using the traffic state data. The energy economy function was modeled using the MATLAB/Sinumlink platform and validated with a plug-in hybrid vehicle model simulation. The results indicate that the proposed strategy improves the vehicle energy economy by 13.02% and reduces CO2 emissions by 16.04% under real vehicle driving conditions, compared with the conventional logic threshold-based control strategy.

Real-time optimal quantum control of mechanical motion at room temperature.

The ability to accurately control the dynamics of physical systems by measurement and feedback is a pillar of modern engineering1. Today, the increasing demand for applied quantum technologies requires adaptation of this level of control to individual quantum systems2,3. Achieving this in an optimal way is a challenging task that relies on both quantum-limited measurements and specifically tailored algorithms for state estimation and feedback4. Successful implementations thus far include experiments on the level of optical and atomic systems5-7. Here we demonstrate real-time optimal control of the quantum trajectory8 of an optically trapped nanoparticle. We combine confocal position sensing close to the Heisenberg limit with optimal state estimation via Kalman filtering to track the particle motion in phase space in real time with a position uncertainty of 1.3times the zero-point fluctuation. Optimal feedback allows us to stabilize the quantum harmonic oscillator to a mean occupation of 0.56±0.02quanta, realizing quantum ground-state cooling from room temperature. Our work establishes quantum Kalman filtering as a method to achieve quantum control of mechanical motion, with potential implications for sensing on all scales. In combination with levitation, this paves the way to full-scale control over the wavepacket dynamics of solid-state macroscopic quantum objects in linear and nonlinear systems.

Comparative Assessment of Alternative Mpc Strategies Using Real Meteorological Data and Their Enhancement for Optimal Utilization of Flexibility-Resources in Buildings

Model predictive control (MPC) method shows superiority in enhancing system performance. But its actual performance in operation is hampered by forecast uncertainties. Different methods have been proposed to mitigate the impacts of these uncertainties, such as shrinking horizon MPC (SHMPC) and stochastic MPC (SMPC). However, the year-round performance of these methods has rarely been investigated, and there is little knowledge on their relative performance, particularly in optimal utilization of flexibility-resources in buildings. In this study, the year-round performance of conventional MPC (CMPC), SHMPC and SMPC strategies when used to optimize utilization of flexibility-resources in buildings are compared using real-time optimal control (RTC) as the benchmark, and their enhancement is ultimately proposed. Forecast uncertainties are quantified based on real meteorological data. Different optimization horizon start times and shrinking intervals are tested. Results show that there is no one control strategy which is superior to the other strategies under all operating conditions. The SMPC strategy has a higher probability of achieving daily cost saving than CMPC and SHMPC strategies, but still has higher daily costs than the RTC strategy in the winter. Hybrid control strategies with proper control schemes implemented under different operating conditions are therefore recommended.

A Two-Layer Real-Time Optimization Control Strategy for Integrated Battery Thermal Management and HVAC System in Connected and Automated HEVs

With the increase in the capacity and power rating of batteries in today's HEVs and EVs, the battery thermal management (BTM) system bears growing importance in vehicle safety and efficiency. A practical BTM system is commonly coupled with the passenger cabin heating-ventilation-air-conditioning (HVAC) system, which makes a major energy consumer and a challenging control object. Thanks to the connected and automated vehicle (CAV) technology, predictions of the vehicle speed profile and power trajectory can be obtained, providing the possibility for predictive control of the BTM and HVAC coupled system in order to maintain battery safety, passenger comfort, and to reduce energy consumption. However, the tradeoff among higher energy saving potential and wider control range from a long and sparse horizon, and higher accuracy from a short and dense horizon is inevitable in the conventional predictive control. In this paper, a two-layer predictive control strategy for warm/hot weather is proposed to address the aforementioned tradeoff. The upper layer controller firstly plans the optimized battery temperature trajectory according to the look-ahead speed preview and battery power profiles provided by the CAV network and the integrated BTM and HVAC system efficiency surface from off-line data. Then the lower layer model predictive controller tracks the planned trajectory and cabin temperature reference while enforcing the energy consumption optimization. Simulation results demonstrate that the proposed strategy exhibits accurate battery and passenger cabin temperature reference tracking while achieving up to 10.47% HVAC energy saving comparing to a baseline control strategy with UDDS profile and typical driving conditions. Implementation of the proposed strategy on a real-time vehicle emulator based on rapid control prototyping (RCP) and hardware-in-the-loop (HIL) platforms demonstrates the real-world implementation capability of the proposed two-layer framework.

Soft Sensor Model Based on IBA-LSSVM for Photosynthetic Bacteria Fermentation Process

It is difficult to measure the key biological process variables of photosynthetic bacteria fermentation in real-time, and offline measurement has a large time lag and cannot meet the needs of real-time optimization control. In this paper, a soft sensor model based on least square support vector machine with an improved bat algorithm (IBA-LSSVM) was proposed. The velocity equation of the bat algorithm (BA) was improved and the random variation operation in differential evolution algorithm was introduced into BA algorithm. Thus, the diversity of the population can be increased, and the global and local searching ability of the BA algorithm can be enhanced. Furthermore, the IBA-LSSVM soft sensor model was established for the living cell concentration and compared with BA-LSSVM soft sensor model. Finally, the simulation results show that the improved model was the better learning ability and prediction performance than BA-LSSVM, the measurement error is 0.1358. The improved model could provide accurate guidance for the photosynthetic bacteria fermentation control optimization. This model has certain practical value.

Optimization Analysis of AVC Adjustment Strategy for Power System with Large-scale Wind Power

As a kind of economical and clean renewable energy, wind power generation is more and more widely used. Wind power plants are far away from the load center and the output power is highly random. Therefore, the control of reactive power, voltage and network loss at public connection points are more difficult. The automatic voltage control system uses the real-time operating data of the power grid to select the best reactive voltage control scheme from the perspective of the entire system. It takes the voltage safety and quality as the conditional limit, takes the economy of the system as the goal, and optimizes the voltage in real-time control. This article gives a control strategy for the problem of inability to smooth transition and repeated oscillations in the signal processing of the abnormal state of the AVC system. The upper and lower limit planes of voltage and reactive power are divided into 27 areas, and each area corresponds to different control strategies. This method can be used to achieve smooth transition of control strategy, real-time voltage optimization control.

Real-time energy-efficient optimal control of high-speed electric train

This paper studies the real-time energy-efficient optimal control of high-speed electric trains within a given trip time. In particular, we focus on the re-optimization of the driving strategy, in the case that the actual train speed deviates from the planned optimal speed profile due to uncertainties. The proposed optimization algorithm has a double-loop structure with simultaneous action implementation. It results in reduced computation complexity and shortened control period, compared to the conventional double-loop algorithm with delayed action implementation. Existence and uniqueness of the solution to the re-optimization problem are discussed, and the convergence of the proposed simultaneous action method is proved. Moreover, it is shown by perturbation analysis that the resulting energy consumption during speed switching process is reduced. The effectiveness of the proposed method is demonstrated by the simulation study on two tracks of Dongguan–Huizhou intercity railway in China.

Mechanism Analysis and Real-time Control of Energy Storage Based Grid Power Oscillation Damping: A Soft Actor-Critic Approach

In this paper, the mechanism of energy storage (ES)-based power oscillation damping is derived by the small signal and the classical electric torque method. And then, by cooperating PI with an integral reduction loop, a controller is designed to form a novel PI-IR controller to guarantee that the energy variation of ES damper is zero at the end of one oscillation. Furthermore, for the controller parameters tuning, the conventional model-based methods require a forecasting model on the uncertainty disturbances. To this end, this problem is formulated as a finite Markov decision process with unknown transition probability, and introduce a deep reinforcement learning (DRL) based model-free agent, the soft actor-critic, to obtain the real-time optimal control strategy. After numerous training, the well-trained agent can act as an experienced decision maker to provide the real-time near-optimal parameters setting for PI-IR control under different operating conditions. Time-domain and eigenvalue analysis results demonstrate the effectiveness of the proposed PI-IR controller and the superiority of the employed DRL based model-free method.

Dynamic Modelling and Simulation of a Multistage Flash Desalination System

As the leading thermal desalination method, multistage flash (MSF) desalination plays an important role in obtaining freshwater. Its dynamic modeling and dynamic performance prediction are quite important for the optimal control, real-time optimal operation, maintenance, and fault diagnosis of MSF plants. In this study, a detailed mathematical model of the MSF system, based on the first principle and its treatment strategy, was established to obtain transient performance change quickly. Firstly, the whole MSF system was divided into four parts, which are brine heat exchanger, flashing stage room, mixed and split modulate, and physical parameter modulate. Secondly, based on mass, energy, and momentum conservation laws, the dynamic correlation equations were formulated and then put together for a simultaneous solution. Next, with the established model, the performance of a brine-recirculation (BR)-MSF plant with 16-stage flash chambers was simulated and compared for validation. Finally, with the validated model and the simultaneous solution method, dynamic simulation and analysis were carried out to respond to the dynamic change of feed seawater temperature, feed seawater concentration, recycle stream mass flow rate, and steam temperature. The dynamic response curves of TBT (top brine temperature), BBT (bottom brine temperature), the temperature of flashing brine at previous stages, and distillate mass flow rate at previous stages were obtained, which specifically reflect the dynamic characteristics of the system. The presented dynamic model and its treatment can provide better analysis for the real-time optimal operation and control of the MSF system to achieve lower operational cost and more stable freshwater quality.

Adaptive real-time optimal control for energy management strategy of extended range electric vehicle

The equivalent fuel consumption minimization strategy (ECMS), as an instantaneous optimization energy management strategy (EMS), is one of the method used to realize real-time optimal control for extended range electric vehicles (EREVs). However, owing to the complexity of the fuel consumption and battery consumption models, the fuel economy optimization problem of EREVs is nonlinear and non-convex; thus, an equivalent factor (EF), i.e., the key parameter of ECMS, can only be solved by numerical iteration methods, such as the shooting method. This paper rewrites the output and state equations of the optimization problem by polynomial fitting and variable substitution of fuel and battery consumption models, transforms the fuel economy problem of an EREV into convex optimization, proposes a quadratic programming-based analytical solution method to solve EF, and provides an adaptive updating rule for terminal SOC to correct the influence of the prediction error of the driving cycle. Thereafter, an adaptive ECMS (A-ECMS) for the real-time optimal control of an EREV is presented and verified. Simulation results show that the proposed A-ECMS can maintain a terminal SOC close to the target value, and in terms of fuel economy, the difference between the proposed A-ECMS and dynamic programming-based global optimization EMS is less than 2%. Compared with the shooting method based on A-ECMS, the proposed A-ECMS achieves better results in all the performance of state of charge, including maintenance, fuel economy, and real-time performances. Especially for real-time performance, the mean computational efficiency of the proposed method is 13–23 times higher than that of the shooting method.

Deep neural networks-based real-time optimal navigation for an automatic guided vehicle with static and dynamic obstacles

In this paper, a hybrid-intelligent real-time optimal control approach based on deep neural networks (DNNs) is proposed to improve the autonomy and intelligence of automatic guided vehicles (AGVs) navigation control. We first formulate the motion planning problem of an AGV with static and dynamic obstacles as a nonlinear optimal control problem (OCP). Subsequently, a direct method incorporating a smooth transformation technique is utilized to obtain the high-fidelity optimal solutions off-line. The optimal state-action samples are then extracted from the optimal trajectories generated by the OCP from perturbed initial states and stored as a large optimal trajectory data set. Thereafter, the DNNs architecture is designed and trained off-line through the collected data set to learn the optimal state-action relationship. As a result, the well-trained DNNs are used to produce the corresponding to optimal feedback actions on-board. The main advantage of our approach is that the time-consuming computation process is only carried out off-line and thus the traditional repeated off-line planning for the AGV due to changes such as the perturbed initial conditions in the system can be effectively avoided. Numerical experimental results are carried out to demonstrate our designed DNNs-based optimal control approach can generate the optimal control instructions on-board to steer the AGV to the desired location with high robustness to initial conditions as well as satisfying different obstacle constraints.