Real-time Optimization Method Research Articles

Autonomous navigation and steering control based on wireless non-wheeled snake robot

Abstract This paper mainly studies an autonomous path-planning and real-time path-tracking optimization method for snake robot. Snake robots can perform search and rescue, exploration, and other tasks in a variety of complex environments. Robots with visual sensors such as LiDAR can avoid obstacles in the environment through autonomous navigation to reach the target point. However, in an unstructured environment, the navigation of snake robot is easily affected by the external environment, causing the robot to deviate from the planned path. In order to solve the problem that snake robots are easily affected by environmental factors in unstructured environments, resulting in poor path-following ability, this paper uses the Los algorithm combined with steering control to plan the robot in real time and control the robot’s steering parameters in real time, ensuring that the robot can stably follow the planned path.

A real-time optimization method in multi-terminal DC distribution system based on adaptive model predictive control

An efficient measure to eliminate voltage oscillation of a multi-terminal DC distribution system (MTDCDS) is locating oscillation source timely and adjusting its control parameters. Combined with oscillation source judgment indexes (OSJIs), a control parameters optimization method based on adaptive model predictive control (A-MPC) is proposed. Specifically, based on the static model, a new adaptive updating mechanism of weighting coefficients is proposed. The mechanism adjusts the weighting coefficients according to the real-time states and OSJIs adaptively. Furthermore, taking the minimum weighted square sum of the voltage tracking error and control parameters increment as objective. Compared with the traditional model predictive control (MPC), the proposed method directly adjusts the control parameters of the oscillation source adaptively. which can significantly improve the system response performance and reduce the control cost. The performance has been evaluated using the MATLAB/Simulink and RT Box experimental platform and the results prove the superiority of the proposed method.

A real-time multi-objective optimization method in energy efficiency for plug-in hybrid electric vehicles considering dynamic electrochemical characteristics of battery and driving conditions

The dynamic performance of power batteries and complex driving conditions significantly impact the energy efficiency of plug-in hybrid electric vehicles (PHEVs). To further enhance the energy utilization of PHEVs, this paper proposes a real-time multi-objective optimization method based on the adaptive equivalent consumption minimization strategy (ECMS) considering the dynamic characteristics of the battery and various driving conditions. First, a second-order RC battery model is established and calibrated based on the experimental data. To characterize the dynamic battery performance and facilitate the real-time adjustment of constraints within the energy management strategy (EMS), the state of charge (SOC) estimation method is developed and the instantaneous and long-term state of power is estimated by considering multiple constraints such as SOC, maximum current and voltage. Subsequently, a data-driven model for recognizing driving conditions is introduced to achieve the adaptive adjustment of equivalent factors. Moreover, to mitigate the impact of the EMS on battery life, a model for battery life degradation is developed and integrated into the optimization. The results indicate that the proposed method reduces battery life degradation by 15.8 % compared with the optimized rule-based strategy and has only a 1.7 % cost differential compared with the offline-optimized ECMS. Furthermore, the hardware-in-the-loop experiment demonstrates the practical applicability of the proposed method.

Real-Time Optimization of Heliostat Field Aiming Strategy via an Improved Swarm Intelligence Algorithm

Optimizing the heliostat field aiming strategy is crucial for maximizing thermal power production in solar power tower (SPT) plants while adhering to operational constraints. Although existing approaches can yield highly optimal solutions, their considerable computational cost makes them unsuitable for real-time optimization in large-scale scenes. This study introduces an efficient, intelligent, real-time optimization method based on a meta-heuristic algorithm to effectively and reliably manage SPT plant operations under varying solar conditions, such as cloud shadowing variations. To minimize redundant calculations, the real-time optimization problem is framed in a way that captures the operational continuity of the heliostat, which can be utilized to streamline the solution process. The proposed method is tested in a simulation environment that includes a heliostat field, cylindrical receiver, and cloud movement model. The results demonstrate that the algorithm presented in this paper offers higher intercept efficiency, improved robustness, and reduced optimization time in more complex scenes.

Iterative real-time optimization of a reductive amination process in a thermomorphic multiphase system

In this paper, we discuss the optimization of the operation of a reductive amination (RA) reaction process in a miniplant without an accurate process model using iterative real-time optimization (RTO). The rhodium-catalyzed RA of undecanal with diethylamine produces a tertiary amine from a long-chain aldehyde and is performed in a thermomorphic multiphase system (TMS) to recover and reuse the expensive catalyst efficiently. An iterative RTO method called modifier adaptation with quadratic approximation (MAWQA) is used in combination with guaranteed model adequacy (GMA) to drive the RA process to its optimum iteratively. MAWQA utilizes online measurements to overcome model deficiencies. GMA ensures that the model used in MAWQA satisfies the model adequacy conditions. The optimal operating conditions of the RA process in the miniplant were identified during an experimental run of the plant, thereby validating the applicability and efficiency of MAWQA with GMA. The results illustrate the benefits of process optimization using iterative RTO methods without accurate process models.

Active uncertainty reduction for safe and efficient interaction planning: A shielding-aware dual control approach

The ability to accurately predict others’ behavior is central to the safety and efficiency of robotic systems in interactive settings, such as human–robot interaction and multi-robot teaming tasks. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as other agents’ goals, attention, and willingness to cooperate. Dual control theory addresses this challenge by treating unknown parameters of a predictive model as stochastic hidden states and inferring their values at runtime using information gathered during system operation. While able to optimally and automatically trade off exploration and exploitation, dual control is computationally intractable for general interactive motion planning, mainly due to the fundamental coupling between the robot’s trajectory plan and its prediction of other agents’ intent. In this paper, we present a novel algorithmic approach to enable active uncertainty reduction for interactive motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of stochastic dynamic programming, leading to a model predictive control problem that can be readily solved by real-time gradient-based optimization methods. The resulting policy is shown to preserve the dual control effect for a broad class of predictive models with both continuous and categorical uncertainty. To ensure the safe operation of the interacting agents, we use a runtime safety filter (also referred to as a “shielding” scheme), which overrides the robot’s dual control policy with a safety fallback strategy when a safety-critical event is imminent. We then augment the dual control framework with an improved variant of the recently proposed shielding-aware robust planning scheme, which proactively balances the nominal planning performance with the risk of high-cost emergency maneuvers triggered by low-probability agent behaviors. We demonstrate the efficacy of our approach with both simulated driving studies and hardware experiments using 1/10 scale autonomous vehicles.

A real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on intelligent optimization algorithm and machine learning

Offshore large-scale cluster extended reach wells (ERWs) are widely used to develop offshore oil & gas resources. Due to the complex downhole environments and complicated geological conditions, drilling parameters real-time optimization is challenging in offshore large-scale cluster ERWs drilling. In this paper, a real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on an intelligent optimization algorithm and machine learning (IOA-ML) is proposed. The method takes ROP as the objective function, establishes a ROP model based on long short-term memory (LSTM) neurons, and obtains drilling parameters optimization results asynchronously by combining the genetic algorithm, differential evolution algorithm, and particle swarm algorithm. The results show that ROP has been improved by 33.33% on average after the optimization by this real-time intelligent optimization method with an optimization time within 60s, which meets the requirements of real-time optimization of drilling parameters.

Steady-state real-time optimization using transient measurements and approximated Hammerstein dynamic model: A proof of concept in an experimental rig

An attractive method for steady-state real-time optimization (RTO) using transient measurements consists of a persistent parameter adaptation throughout a dynamic parameter estimation followed by a static economic optimization. The method is called RTO with persistent parameter adaptation (ROPA) or Hybrid RTO (HRTO). Although such a method avoids the long steady-state wait to trigger the optimizations, it adds extra complexity to the RTO project by including the requirement of developing a rigorous dynamic model, which can hinder the industrial-scale applicability of the methodology. To overcome this requirement, a Hammerstein dynamic approximation model has been proposed to replace the rigorous dynamic model in a Hammerstein ROPA (HROPA) framework. Previous work has demonstrated that the proposed model is adequate for use in RTO since it preserves the parameter observability property of the static model. In this paper, two HROPA approaches are implemented in an experimental rig and compared to a previously implemented ROPA approach. The experimental results confirmed that HROPA presents a similar economic performance to ROPA with much less effort required in the modeling stage. Finally, a comprehensive literature review of the topic is presented, and general guidelines for the Hammerstein dynamic matrix identification are provided.

Coordination Optimization of Real-Time Signal Priority of Self-Driving Buses at Arterial Intersections Considering Private Vehicles

Transit Signal Priority (TSP) is a system designed to grant right-of-way to buses, yet it can lead to delays for private vehicles. With the rapid advancement of network technology, self-driving buses have the capability to efficiently acquire road information and optimize the coordination between vehicle arrival and signal timing. However, the complexity of arterial intersections poses challenges for conventional algorithms and models in adapting to real-time signal priority. In this paper, a novel real-time signal-priority optimization method is proposed for self-driving buses based on the CACC model and the powerful deep Q-network (DQN) algorithm. The proposed method leverages the DQN algorithm to facilitate rapid data collection, analysis, and feedback in self-driving scenarios. Based on the arrival states of both the bus and private vehicles, appropriate actions are chosen to adjust the current-phase green time or switch to the next phase while calculating the duration of the green light. In order to optimize traffic balance, the reward function incorporates an equalization reward term. Through simulation analysis using the SUMO framework with self-driving buses in Zhengzhou, the results demonstrate that the DQN-controlled self-driving TSP optimization method reduces intersection delay by 27.77% and 30.55% compared to scenarios without TSP and with traditional active transit signal priority (ATSP), respectively. Furthermore, the queue length is reduced by 33.41% and 38.21% compared to scenarios without TSP and with traditional ATSP, respectively. These findings highlight the superior control effectiveness of the proposed method, particularly during peak hours and in high-traffic volume scenarios.

A Real-Time Adaptive Energy Optimization Method for Urban Rail Flexible Traction Power Supply System

In urban rail traction power supply systems, the optimal power flow method is mostly utilized for energy optimization. Conventional power flow optimization adopts offline calculation methods to obtain the optimal strategy under given scenarios. However, this method cannot continuously realize the ideal optimization effect for the time-varying flexible traction power supply system. For this reason, this paper proposes a real-time adaptive energy optimization method to solve the problem above. Firstly, the control objective of the optimal power flow is determined based on analyzing the system power flow. Secondly, a hierarchical control system is designed to achieve adaptive optimal control for the flexible traction power supply system. Moreover, a swarm intelligence optimization algorithm-based optimal power flow calculation method is employed to realize the real-time optimal power flow. Finally, some simulations are performed to verify the effectiveness and accuracy of the proposed real-time optimal power flow. It is demonstrated that the real-time optimal power flow method makes it possible to consider the low delay and high accuracy of control commands at the same time. Compared with the conventional offline method, the proposed real-time method can reduce the input electricity consumption by 1.47%, which is valuable in practical applications.

Real-Time Optimization of Gear Shift Trajectories Using Quadratic Programming for Electric Vehicles With Dual Clutch Transmission

The main focus of this paper is a method for real-time optimization of the gear shift trajectories for electric vehicles (EVs) with dual clutch transmissions. First, a driveline model was arranged for each gear shift process. The states in each gear shift process can be predicted through these models. An objective function is composed of a frequency-shaped jerk to minimize the shift shock and take into account the bandwidth limit of the lower-level controller. Equality constraints are defined for smooth model changes during the gear shift processes. Moreover, the conditions needed to reflect the driver's pedal input (which can be changed in real-time) is composed of an equality constraint. In addition, inequality constraints are constructed to limit the maximum value of the torque, torque rate, and jerk during gear shift processes. Finally, the problem is formulated in quadratic programming (QP) form. The gear shift trajectories and feedforward inputs are generated by obtaining an optimal solution through the QP solver. The performance of the proposed algorithm is verified through testbench experiments.

On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data

Frequent aviation safety accidents of civil aircraft misses and crashes lead to an urgent need for flight safety assurance. Due to long-time flights over different backgrounds, accompanied by the changes in flight altitude and speed, it is difficult for a conventional space-based infrared detection system to use a set of fixed parameters to meet the stable detection requirement. To enhance the awareness of civil aircraft surveillance, a real-time parameter optimization method based on Automatic Dependent Surveillance-Broadcast (ADS-B) data is proposed. According to the background spectral characteristics and the real-time flight data, the most reasonable spectral band is analyzed, using the joint signal-to-noise/clutter ratio (JSNCR) as the evaluation criteria. Then, an automatic parameter adjustment is used to maximize the integration time and switch the integration capacitor gear. Numerical simulation results show that the JSNCR increased by 1.16 to 1.31 times, and the corresponding noise equivalent target radiant intensity (NET) reduced from 2.4 W/Sr to 1.2 W/Sr compared with a conventional fixed-parameter detection system. This study lays a solid theoretical foundation for the spectral band analysis of space-based AVD system design. Meanwhile, the proposed method can be used as a standard procedure to improve on-board performance.

AI-aided power electronic converters automatic online real-time efficiency optimization method

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future.

The Design and Application of Microgrid Supervisory System for Commercial Buildings Considering Dynamic Converter Efficiency

This paper presents a supervisory system that considers converter efficiency for local microgrids of commercial buildings to solve the uncertainty problem of the sources and loads while also optimizing local microgrid operating costs and maintaining power supply quality for commercial buildings. The supervisory system includes an energy management layer and a power management layer. In the energy management layer, a long-term optimization approach is used to reduce the operating costs by considering the dynamic converter efficiency. In the power management layer, a real-time power optimization method is structured to deal with the uncertainty problem of the sources and loads, and to ensure that the direct current bus power is balanced while also guaranteeing the power quality by considering the dynamic converter efficiency. Four cases are proposed for the supervisory system, and these cases are simulated in MATLAB/Simulink under three typical weather conditions: cloud, sunshine, and rain. The comparison of simulation results for cases 1 and 2 illustrates the impact of converter efficiency on energy coordination in microgrids. The simulation results of cases 3 and 4 verify that the performance—in terms of the power supply quality and the operating costs—of the proposed microgrid supervisory system considering dynamic converter efficiency outperforms that of the microgrid supervisory system considering fixed converter efficiency.

Dynamic load modeling for bulk load-using synchrophasors with wide area measurement system for smart grid real-time load monitoring and optimization

Bulk data modeling in a smart grid dynamic network has been performed using an automated load modeling tool (ALMT), an on-load tap changer, and exponential dynamic load modeling. However, studies have observed that a small parameter variation may lead to considerable variations in measuring grid big data. Therefore, this study presents dynamic real-time load modeling, monitoring, and optimization method for the bulk load. The case study was conducted on Sarawak Energy Berhad (SEB), Malaysia. The grid system's real-time data and load modeling achieved the objectives. Dynamic load model was achieved by using load response in MATLAB Simulink environment. This paper also includes new parameter estimations of the load composition at the selected bus. The simulation results of load models were compared with the recorded data by applying an event of bus tripping time interval. The Least Square Error Method was used to converge the estimated parameter values on load composition and compared with the actual recorded data until optimized load models were achieved. This work is a precious and significant contribution to utility research to identify, monitor, and optimize the most appropriate representation of system loads.

Hybrid dynamic modeling and receding horizon speed optimization for liner shipping operations from schedule reliability and energy efficiency perspectives

Uncertainties in port handling efficiency can cause port delays in the liner shipping system. Furthermore, policies on carbon emission reduction, such as EEXI standards, restrict the potential for speed optimization in liner shipping operations. Traditional tactical planning speed optimization is unsuitable for operational-level decision making, leading to unreliable schedules. From a schedule-reliability and energy-efficiency perspective, we propose a real-time speed optimization method based on discrete hybrid automaton (DHA) and decentered model predictive control (DMPC). We use a dynamic adjustment of sailing speed to offset the disturbance caused by port handling efficiency uncertainties. First, we establish a DHA model that describes each ship’s hybrid dynamics of state switching between sailing and berthing; then, we develop a prediction model for the DMPC controller, which is analogous to the DHA model. The schedule is transferred into time–position coordinates as controller reference trajectories in the receding horizon speed optimization framework. We consider determining tracking errors, carbon emissions, and fuel consumption as our objectives, and we carry out engine power limitation (EPL) analysis for the sample ship, which turns the EEXI standards into constraints. We attain the recommended speed by solving a mixed-integer optimization. We carry out a case study, and our results indicate the effectiveness of our proposed DHA-DMPC scheme in lowering port delays and achieving the best trade-off between schedule reliability and energy efficiency. Additionally, we conduct further experiments to analyze the impacts of various carbon reduction policies on the performance levels of liner shipping operations.

Real-Time Structure Generation Based on Data-Driven Using Machine Learning

Topology optimization results are highly dependent on the given design constraints and boundary conditions. Moreover, small changes in initial design conditions can result in different topological configurations, which makes topology optimization time-consuming in a given design constraint domain and inefficient in structural design. To address this problem, a data-driven real-time topology optimization framework and method coupled with machine learning by using a principal component analysis algorithm combined with a feedforward neural network are developed in this paper. Meanwhile, through the offline training, the mapping relationship between initial design conditions and topology optimization results is obtained. From this mapping, we estimate the optimal topologies for novel loading configurations. Numerical examples display that the online prediction results are consistent with the results of the topology optimization method. Furthermore, the network parameters are calibrated, and accurate structure prediction is achieved based on the algorithm. In addition, this method ensures the accuracy of high-resolution structural prediction on the premise of small samples.

Model‐free real‐time optimization of process systems using safe Bayesian optimization

AbstractConventional real‐time optimization (RTO) requires detailed process models, which may be challenging or expensive to obtain. Model‐free RTO methods are an attractive alternative to circumvent the challenge of developing accurate models. Most model‐free RTO methods are based on estimating the steady‐state cost gradient with respect to the decision variables and driving the estimated gradient to zero using integral action. However, accurate gradient estimation requires clear time scale separation from the plant dynamics, such that the dynamic plant can be assumed to be a static map. For processes with long settling times, this can lead to prohibitively slow convergence to the optimum. To avoid the need to estimate the cost gradients from the measurement, this article uses Bayesian optimization, which is a zeroth order black‐box optimization framework. In particular, this article uses a safe Bayesian optimization based on interior point methods to ensure that the setpoints computed by the model‐free steady‐state RTO layer are guaranteed to be feasible with high probability (i.e., the safety‐critical constraints will not be violated at steady‐state). The proposed method can thus be seen as a model‐free variant of the conventional two‐step steady‐state RTO framework (with steady‐state detection), which is demonstrated on a benchmark Williams‐Otto reactor example.

Extremum seeking for infinite-dimensional systems

The last year was the centennial of the 1922 invention, in the context of maximizing the power transfer to an electric tram car, of the method for real-time model-free optimization called extremum seeking (ES). Forgotten after a few decades of practical use and attempts at theoretical study, ES returned to life when the stability of its operation for not only static input–output maps but for dynamic systems modeled by general nonlinear ordinary differential equations (ODEs) was proven in 1997. Then it is natural to ask “why limit the use and the theoretical advances of ES to ODE systems?” So many—if not the majority of physical systems—involve delays or are modeled by partial differential equations (PDEs); why not pursue the application of ES in the presence of delays and for PDE systems? With this tutorial paper, we show a small portion of the vast space of possibilities of designing ES algorithms for infinite-dimensional systems governed by transport hyperbolic PDEs as well as diffusion parabolic PDEs.

Energy capture optimization of heave oscillating buoy wave energy converter based on model predictive control

The optimal constant damping control based on the peak frequency of irregular wave is commonly adopted to improve the wave energy capture efficiency for the oscillating buoy wave energy converter (WEC) while the influence of the wave frequency change cannot be considered. In this paper, a real-time PTO force control optimization method is proposed based on model predictive control (MPC). Firstly, the dynamic model of the buoy is built based on the Cummins equation. Secondly, the additional penalty terms which may make the optimization problem convex are introduced into the original maximized energy objective, and their effects on the efficiency are investigated. Then, the deterministic sea wave prediction method is used to predict the future wave force aiming to make up the shortage that the classical MPC cannot compensate the external disturbance. Finally, the energy capture performance of MPC for the oscillating buoy WEC was compared with the resistive control. The results show that the MPC method can improve the energy capture by about 40%–60% under the irregular wave conditions.