Closed-loop Prediction Research Articles

A Model and Data Dual-Driven Framework for Lithium-Ion Battery Cycle Life Prediction Integrating Uncertainty Model

Abstract To address the shortcomings of single model-based or data-driven methods in battery life prediction, this paper proposes a model and data dual-driven framework for lithium-ion battery (LIB) cycle life prediction integrating uncertainty model. First, a cloud model is used to quantify the uncertainty inherent in the empirical model and construct a capacity degradation uncertainty model. Next, health features highly correlated with capacity are extracted from historical data to train a convolutional neural network, forming an online estimation model that guides subsequent parameter correction. Then, based on the prediction discrepancy between the two models, the error-feedback gate recurrent unit with improved attention model is developed for the online dynamic correction of empirical model parameters, realizing a closed-loop prediction framework. Finally, a rolling correction strategy based on statistical error is proposed to dynamically adjust the cloud model parameters, with the corrections fed back to the capacity degradation uncertainty model to further refine prediction uncertainty and accuracy. Experimental results indicate that the proposed method achieves high accuracy across various battery datasets, with relative error remaining below 2%, and provides uncertainty-quantified remaining useful life prediction, which effectively supports the online health monitoring and maintenance strategy formulation of LIBs in diverse scenarios.

Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting

Application-Driven Learning: Closing the Loop Between the Application and the Estimation of Forecast Models This paper introduces a closed-loop framework called application-driven learning, where the best forecast model is tailored to the application cost structure. Our methodology employs two-stage optimization schemes to derive multivariate point forecasts. The estimation problem is conceived as a bilevel model, and we propose two solution methodologies: an exact one using KKT conditions and a scalable decomposition heuristic. This approach offers a scientifically grounded alternative to ad hoc demand biasing approaches and reserve requirement rules currently adopted by power system operators worldwide. Testing with real data and large-scale systems demonstrates that our methodology consistently outperforms traditional open-loop methods, providing significant potential benefits for energy system operations.

Hierarchical Coordinated Predictive Control of Multiagent Systems for Process Industries

Focusing on the requirements for efficient and accurate control in large-scale process industries with integrated “distributed-decentralized” characteristics, a novel hierarchical coordinated predictive control strategy for process industries is proposed with a multiagent system as the computational paradigm. This approach comprehensively considers the overall state of the system, the interactions of control actions among agents, the constraints of processes and energy consumption to solve the problems of poor flexibility of agent decision-making in the narrow consensus strategy and strong interaction of parts of the system. The proposed hierarchical control strategy requires each agent to perform three tasks at each time step. First, each agent iteratively obtains a consistent basis for closed-loop prediction in a distributed way. Then, each agent independently proposes a control scheme and determines its own priority by playing games based on the economic performance of the scheme. Next, each agent calculates its own optimal dynamic predictive control sequence in order of priority based on the system’s dynamic process model. Finally, by considering the temperature-control process of heating an alumina ceramic block in a high-power microwave reactor with six microwave sources, the effectiveness of the proposed hierarchical coordinated predictive control strategy is verified under different communication topologies by comparing it with the centralized model predictive control strategy.

Intelligent forecasting temperature measurements of solar PV cells using modified recurrent neural network

For microgrids to operate optimally and minimize the effects of uncertainty, anticipating solar PV measurements is essential. For residential and commercial microgrids that use solar PV, the predicting of solar energy over a short period is crucial for managing grid-connected PVeffectively. Therefore, this work develops a Recurrent Neural Network (RNN) for forecasting temperature measurements as time series records, where a combination of long short-term memory (LSTM) architecture with RNN is used to process input measurements by updating the RNN state and winding over time degrees. Data from the entire prior time steps is stored in the RNN state. A dataset of temperature waveform measurements is used, which includes 2000 unnaturally produced signals of three channels with varying length. An LSTM neural network can be used to expect future values of a time series or sequence utilizing data from earlier time steps as input. Training of a regression LSTM neural network through the output of a sequence is performed, where the goals are the training sequence with records shifting one-time step, for training theLSTM neural architecture with time series forecasting. In other words, the weights of the LSTM neural structure learn to predict the following time step values of the input sequence at every time step. By considering the past forecasts as inputs, the closed-loop prediction forecasts the next time steps of sequences. The model makes the forecast without using the true data. The cross-entropy loss serves as the loss function. It is found that the mean RMSE overall test observations were about 0.5080 which promises to make better predictions from learning the temporal context of input sequences

Autonomous Emergency Landing for Fixed-Wing Aircraft with Energy-Constrained Closed-Loop Prediction

This paper presents a new approach for autonomous motion planning for aircraft suffering from a loss-of-thrust emergency. Specifically, we show how modifications to the Closed-Loop Rapidly exploring Random Trees (CL-RRT) framework combined with controlled energy dissipation can enable rapid and effective kinodynamic motion planning. This CL-RRT Glide algorithm uses closed-loop prediction not only for node connections but also to estimate the remaining energy and prune infeasible paths. This greatly speeds up the search process, which is essential for emergency situations. In addition, we improve the ability of the gliding aircraft to reach a goal position and energy state. We do so by creating a Dissipative Total Energy Control Scheme (TECS). Dissipative TECS enables the glider to lose excess altitude in order to reach a desired energy level. Simulation results illustrate how the proposed methods enable faster motion planning. We also integrate the system into a small unmanned aerial vehicle system and experimentally demonstrate autonomous glide planning and execution during a motor-failure event. This type of algorithm can primarily benefit unmanned aircraft but can also serve to assist pilots in stressful emergency situations.

Online Learning of the Kalman Filter With Logarithmic Regret

In this paper, we consider the problem of predicting observations generated online by an unknown, partially observable linear system, which is driven by Gaussian noise. In the linear Gaussian setting, the optimal predictor in the mean square error sense is the celebrated Kalman filter, which can be explicitly computed when the system model is known. When the system model is unknown, we have to learn how to predict observations online based on finite data, suffering possibly a non-zero regret with respect to the Kalman filter's prediction. We show that it is possible to achieve a regret of the order of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{poly}\log (\mathsf {N})$</tex-math></inline-formula> with high probability, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> is the number of observations collected. This is achieved using an online least-squares algorithm, which exploits the approximately linear relation between future observations and past observations. The regret analysis is based on the stability properties of the Kalman filter, recent statistical tools for finite sample analysis of system identification, and classical results for the analysis of least-squares algorithms for time series. Our regret analysis can also be applied to other predictors, e.g. multiple step-ahead prediction, or prediction under exogenous inputs including closed-loop prediction. A fundamental technical contribution is that our bounds hold even for the class of non-explosive systems (including marginally stable systems), which was not addressed before in the case of online prediction.

The design of an inkjet drive waveform using machine learning

A drive waveform, which needs to be optimized with ink’s fluid properties, is critical to reliable inkjet printing. A generally adopted rule of thumb for its design is mostly dependent on time-consuming and repetitive manual manipulation of its parameters. This work presents a closed-loop machine learning approach to designing an optimal drive waveform for satellite-free inkjet printing at a target velocity. Each of the representative 11 model inks with different fluid properties was ink-jetted with 1100 distinct waveform designs. The high-speed images of their jetting behaviors were acquired and the big datasets of the resulting drop formation and velocity were extracted from the jetting images. Five machine learning models were examined and compared to predict the characteristics of jetting behavior. Among a variety of machine learning models, Multi-layer Perceptron affords the highest prediction accuracy. A closed-loop prediction algorithm that determined the optimal set of waveform parameters for satellite-free drop formation at a target velocity and employed the most superior learning model was established. The proposed method was confirmed through the printing of an unknown model ink with a recommended waveform.

Particle-Swarm-Optimization-Based 2D Output Feedback Robust Constraint Model Predictive Control for Batch Processes

For the input and output constraints and uncertainties in batch processes, a 2D output feedback robust constrained model predictive control (MPC) method is designed by combining iterative learning control (ILC), MPC and output feedback. Firstly, an equivalent 2D-FM closed-loop prediction model is established by combining with the proposed output feedback controller. Then an optimization performance index function with terminal constraint is constructed to study its control optimization. According to the designed optimization performance index and Lyapunov stability theory, the feasible MPC problem is obtained by solving the linear matrix inequalities (LMIs). At the same time, the gain of the new output feedback control law is given to ensure that the performance index reaches the minimum upper bound under the constraints of input and output. In order to solve the manual adjustment problem of some parameters in the performance index function, the particle swarm optimization (PSO) algorithm is introduced, and a better solution is found near the controller by using the search optimization method. Finally, taking the injection molding process as an example and comparing with the existing method without using PSO algorithms, it is proved that the above method is more feasible.

A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN

Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time.

CNN-LSTM architecture for predictive indoor temperature modeling

Indoor temperature modeling is a crucial part towards efficient Heating, Ventilation and Air Conditioning (HVAC) systems. Data-driven black-box approaches have been an attractive way to develop such models due to their unique feature of not requiring detailed knowledge about the target zone. However, the noisy and non-linear nature of the problem remains a bottleneck especially for long prediction horizons. In this paper, we introduce a Convolutional Neural Networks-Long Short Term Memory (CNN-LSTM) architecture to combine the exceptional feature extraction of convolutional layers with the Long Short Term Memory (LSTM)’s capability of learning sequential dependencies. We experimentally collected a dataset and compared three approaches: Multi-Layer Perceptron (MLP), LSTM and CNN-LSTM. Models are evaluated and compared with 1-30-60-120 min horizons with a closed-loop prediction scheme. The CNN-LSTM outperformed all other models for all prediction horizons and showed a better robustness against error accumulation. It managed to predict room temperature with R2>0.9 in a 120-min prediction horizon.

Trends and Challenges of Processing Measurements from Wearable Devices Intended for Epileptic Seizure Prediction

The rapid contemporary development of wearable devices offers non-invasive and effective approaches for monitoring the human brain. Recent studies have investigated the prediction of epileptic seizures (ESs) using wearable measurements, such as scalp electroencephalography and functional near-infrared spectroscopy. The signal processing tasks are the core component of emerging closed-loop ES prediction (ESP) systems. Various research groups have introduced many state-of-the-art signal processing techniques to improve ESP performance. Wearable measurements consider low frequency and low spatial resolution characteristics. In this paper, we provide a comprehensive review of signal processing techniques including preprocessing, feature extraction, dimensionality reduction and classification schemes for ESP systems. Trends and concerns of ESP studies at the end of the manuscript.

Robust model predictive control via multi-scenario reference trajectory optimization with closed-loop prediction

This paper presents a two-layer control structure to address parameter uncertainty within a plant. The lower layer is formulated as a nominal MPC that computes control actions to regulate the underlying plant, and the upper layer computes optimal set-point trajectories for the lower level to track. The upper layer is formulated as an optimization problem that takes into account the closed-loop behavior of uncertain plant scenarios under the action of nominal MPC. The upper layer facilitates the lower layer in avoiding constraint violations and producing less conservative control actions by assigning time-varying set-point trajectories to the nominal MPC. The benefits of this approach are illustrated through application to linear single-input–single-output transfer function case studies, and a nonlinear multi-input multi-output evaporator process.

Robust model predictive control with embedded multi-scenario closed-loop prediction

Robust MPC seeks to mitigate the effects of uncertainty which can lead to suboptimal MPC performance. Previous work on robust MPC includes a stochastic multi-scenario approach to simulate multiple plant realizations and create a control scheme to optimize a performance metric based on the plant scenarios. This paper seeks to combine a scenario-based approach with embedded closed-loop prediction under future MPC control action by directly incorporating MPC subproblems into the overall robust MPC formulation. This allows the current MPC to predict closed-loop MPC responses to a range of uncertain future plant realizations. The resulting multilevel programming problem is solved by reformulating the inner MPC optimization subproblems as algebraic constraints corresponding to their first-order optimality conditions, resulting in a single level mathematical program with complementarity constraints (MPCC). The performance of the robust MPC scheme is evaluated against a standard MPC formulation in linear and nonlinear case studies.

IMPROVED CONSTRAINT HANDLING APPROACH FOR PREDICTIVE FUNCTIONAL CONTROL USING AN IMPLIED CLOSED-LOOP PREDICTION

Predictive Functional Control is a simple alternative to the traditional PID controller which has the capability to handle process constraints more systematically. Nevertheless, the most basic form of PFC has suffered from ill-posed prediction due to its simplicity in formulation and assumption of constant future input dynamics. Although some constraints can be satisfied, nevertheless the performance may be very conservative due to this issue. The main objective of this paper is to improve the constrained performance of a PFC controller with a minimum modification of the existing formulation. Specifically, a novel constraint handling approach for PFC is proposed based on an implied closed-loop prediction. Instead of assuming a constant input as deployed in the conventional open-loop prediction, the implied closed-loop input dynamics are utilised to detect future constraint violations. In addition, a future perturbation is introduced into the prediction structure as an extra degree of freedom for satisfying the constraints. Two simulation results confirm that the proposed approach gives far less conservative constraint handling and thus better control performance compared to the nominal PFC. Furthermore, this novel implementation also alleviates the well-known tuning difficulties and prediction inconsistency issues that are associated with conventional PFC when handling constraints. ABSTRAK: Kawalan Kefungsian Ramalan adalah alternatif mudah kepada kawalan tradisional PID yang mempunyai kekangan keupayaan bagi mengawal proses secara lebih tersusun. Namun, keadaan paling asas pada kesan PFC adalah daripada ramalan tak teraju-rapi yang disebabkan oleh formula ringkas dan anggapan dinamik input yang sama bagi masa depan. Walau kekangan ini dapat diatasi, namun prestasi akan berubah secara konservatif disebabkan oleh isu ini. Objektif utama kajian ini adalah bagi membaiki kekangan prestasi kawalan PFC dengan modifikasi minimum formula yang ada. Secara spesifik, pendekatan nobel kawalan PFC dicadangkan berdasarkan ramalan lingkaran-tertutup. Selain anggapan input tetap seperti yang dilakukan pada ramalan lingkaran-terbuka yang konservatif, dinamik input yang dibuat pada lingkaran-tertutup telah digunakan bagi mengesan kekangan masa depan yang bertentangan. Tambahan, gangguan yang bakal berlaku pada masa depan telah diperkenalkan ke dalam struktur ramalan sebagai tambahan darjah pada kebebasan bagi mengatasi kekangan. Dua dapatan simulasi kajian menyetujui pendekatan yang dicadangkan dan menyebabkan sangat kurang kekangan pengendalian pada sistem konservatif, oleh itu kawalan yang lebih bagus pada prestasi berbanding pada PFC nominal. Selain itu, pendekatan nobel ini juga menghilangkan kesukaran pelarasan yang dikenali ramai dan ramalan isu tidak konsisten yang terdapat pada PFC konvensional apabila mengendali kekangan.

Production scheduling in dynamic real-time optimization with closed-loop prediction

Process plants are operating in an increasingly dynamic environment, fueled largely by globalization and deregulation of energy markets, resulting in fluctuating market conditions and large variations in electricity prices. Such conditions pose challenges for traditional hierarchical plant decision-making systems, leading to efforts toward integration across the decision-making layers. This paper proposes a formulation for integration of production scheduling decisions within a dynamic real-time optimization (DRTO) framework. The DRTO formulation utilizes a closed-loop prediction of the plant response under the action of constrained model predictive control (MPC). The integrated scheduling and DRTO system communicates decisions to the underlying MPC system through time-varying set-point trajectories, thereby permitting the standard MPC implementation to be retained. The efficacy of the prosed system is illustrated through application to both single-input single-output (SISO) and multi-input multi-output (MIMO) case studies.

Faster-Than-Real-Time Dynamic Simulation of AC/DC Grids on Reconfigurable Hardware

Dynamic simulation of integrated AC and DC grids is paramount to address real-time operation challenges in energy control centers, such as available transfer capacities, relieving grid congestion, and taking effective control actions for improving the integrated grid system stability and reliability. This paper proposes a faster-than-real-time (FTRT) dynamic simulation of integrated AC/DC grids on the reconfigurable parallel hardware architecture of the field programmable gate array (FPGA). A fine-grained relaxation algorithm (FGRA) is proposed for a more efficient solution of the nonlinear differential algebraic equations of the integrated system model, including the detailed nonlinear models of the synchronous generators in the AC system which can be solved in parallel without matrix on the FPGA. The system solution is massively parallelized and pipelined in hardware to realize the lowest latencies and minimum utilization of hardware resource. Two case studies are used to illustrate the efficacy of the proposed algorithm and demonstrated a closed-loop prediction scenario for improving grid stability. Computational acceleration of up to 134 times faster than real-time are reported for the two case studies, and the accuracy of the dynamic interaction is validated using the off-line transient stability simulation tool TSAT of the DSATools package.

Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems

As an innovative mobility strategy, public bike-sharing has grown dramatically worldwide. Though it provides convenient, low-cost, and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems for both users and operators. The primary issue is the uneven distribution of bikes caused by ever-changing usage and (available) supply. This imbalance necessitates efficient bike rebalancing strategies, which depends highly on bike mobility modeling and prediction. In this paper, a trace-driven simulation-based prediction approach is proposed by simultaneously taking user mobility demand and real-time status of stations into consideration. We extensively evaluate the performance of our design with the dataset from one of the world’s largest public bike-sharing systems located in Hangzhou, China, which owns more than 2800 stations. The evaluation results show an 85 percentile relative error of 0.6 for checkout and 0.4 for checkin prediction. The preliminary results on how the predictions can be used for bike rebalancing are also provided. We believe that this new mobility modeling and prediction approach can improve the bike-sharing system operation algorithm design and pave the way for rapid deployment and adoption of bike-sharing systems across the globe.

Dynamic real-time optimization of distributed MPC systems using rigorous closed-loop prediction

A dynamic real-time optimization (DRTO) formulation with closed-loop prediction is used to coordinate distributed model predictive controllers (MPCs) by rigorously predicting the interaction between the distributed MPCs and full plant response in the DRTO formulation. This results a multi-level optimization problem that is solved by replacing the MPC quadratic programming subproblems by their equivalent Karush-Kuhn-Tucker (KKT) first-order optimality conditions to yield a single-level mathematical program with complementarity constraints (MPCC). The proposed formulation is able to perform both target tracking and economic optimization with significant performance improvement over decentralized control, and similar performance to centralized MPC. A linear dynamic case study illustrates the performance of the proposed strategy for coordination of distributed MPCs for different levels of plant interaction. The method is thereafter applied to a nonlinear integrated plant with recycle, where its performance in both set-point target tracking and economic optimization is demonstrated.

Approximation techniques for dynamic real-time optimization (DRTO) of distributed MPC systems

A dynamic real-time optimization (DRTO) strategy has recently been proposed for coordination of distributed MPC systems. The scheme utilizes a prediction model that accounts for the impact of the distributed controllers on the plant response. Two techniques are presented for approximating the closed-loop prediction within the DRTO formulation - a hybrid closed-loop and an input clipping formulation. The hybrid formulation generates closed-loop predictions for a limited number of time intervals along the DRTO prediction horizon, followed by an open-loop optimal control formulation for the rest of the horizon. The input clipping formulation utilizes an unconstrained MPC optimization formulation for each distributed MPC, coupled with an input saturation mechanism. The performance of the approximation techniques is evaluated through application to case studies based on linear and nonlinear dynamic plant models respectively. The approximation techniques are demonstrated to be more computationally efficient than the rigorous counterpart without significant loss in performance.

Hybrid algorithm for accumulated error suppression in open‐loop Doppler receiver

A hybrid algorithm is proposed to decrease the integration phase measurement error accumulation in open-loop Doppler receiver which is designed for space orbit determination and positioning applications when cooperated closed-loop system is unavailable. Firstly, adaptive-neuro-fuzzy inference system (ANFIS) module α is implemented for the interrupted closed-loop measurement data prediction. Then to improve the prediction accuracy of the ANFIS module α, an adaptive Kalman filter is employed for data fusion with predicted data from ANFIS module α and open-loop measurement data for complementation and corrections. Meanwhile, ANFIS module β is embedded in the Kalman filter for adaptive error compensation to optimise the filter performance. Finally, the time costly ANFIS computations are accelerated by reconfigurable software–hardware co-design module implemented in the receiver system to improve the computing capability and efficiency of system. Experiment results are analysed to demonstrate the effectiveness of proposed hybrid algorithm. It suppresses error accumulation in open-loop receiver phase measurement by 85.89% compared to the directly integration results when closed-loop system is off-line and has 61.02% enhancement compared to the algorithm without adaptive error compensations. Thus, the whole combinatory system accuracy is improved.