Meta-optimised environmental control for sleep and health enhancement

Introduction of a plug and play model predictive control to predict room temperatures

Sleep quality in patients studied with laboratory-based polysomnography may differ from sleep quality in patients studied at home but remains clinically relevant and important to describe. We assessed objective sleep quality and explored factors associated with poor sleep in patients undergoing laboratory-based polysomnography. We reviewed diagnostic polysomnography studies from a 10-year period at a single sleep center. Total sleep time (TST) and sleep efficiency (SE) were assessed as markers of sleep quality. Poor sleep was defined as TST ≤ 4 hours or SE ≤ 50%. Multivariable analysis was performed to determine associations between objective sleep quality as an outcome and multiple candidate predictors including age, sex, race, body mass index, comorbidities, severity of obstructive sleep apnea, and central nervous system medications. Among 4957 patients (age 53 ± 15 years), average TST and median SE were 5.8 hours and 79%, respectively. There were 556 (11%) and 406 (8%) patients who had poor sleep based on TST and SE, respectively. In multivariable analysis, those who were older (per 10 years: 1.48 [1.34, 1.63]), male (1.38 [1.14,1.68]), and had severe obstructive sleep apnea (1.76 [1.28, 2.43]) were more likely to have short sleep. Antidepressant use was associated with lower odds of short sleep (0.77 [0.59,1.00]). Older age (per 10 years: 1.48 [1.34, 1.62]), male sex (1.34 [1.07,1.68]), and severe obstructive sleep apnea (2.16 [1.47, 3.21]) were associated with higher odds of poor SE. We describe TST and SE from a single sleep center cohort. Multiple demographic characteristics were associated with poor objective sleep in patients during laboratory-based polysomnography. Harrison EI, Roth RH, Lobo JM, et al. Sleep time and efficiency in patients undergoing laboratory-based polysomnography. J Clin Sleep Med. 2021;17(8):1591-1598.

Reinforcement learning-based real-time intelligent energy management for hybrid electric vehicles in a model predictive control framework

Sleep, Plasticity and Memory from Molecules to Whole-Brain Networks

It is known that there is a class of nonlinear systems that cannot be stabilized by a continuous time‐invariant feedback. This class includes systems with interest in practice, such as nonholonomic systems, frequently appearing in robotics and other areas. Yet, most continuous‐time model predictive control (MPC) frameworks had to assume continuity of the resulting feedback law, being unable to address an important class of nonlinear systems. It is also known that the open‐loop optimal control problems that are solved in MPC algorithms may not have, in general, a continuous solution. Again, most continuous‐time MPC frameworks had to artificially assume continuity of the optimal controls or, alternatively, impose some demanding assumptions on the data of the optimal control problem to achieve the desired continuity. In this work we analyse the reasons why traditional MPC approaches had to impose the continuity assumptions, the difficulties in relaxing these assumptions, and how the concept of ‘sampling feedbacks’ combines naturally with MPC to overcome these difficulties. A continuous‐time MPC framework using a strictly positive inter‐sampling time is argued to be appropriate to use with discontinuous optimal controls and discontinuous feedbacks. The essential features for the stability of such MPC framework are reviewed. Copyright © 2003 John Wiley & Sons, Ltd.

Recently, electric vehicles (EVs) using energy storage have gained attention over conventional vehicles using fossil fuels owing to their advantages such as being eco-friendly and reducing the operation cost. In a power system, an EV, which operates through the energy stored in the battery, can be used as a type of load or energy source; hence, an optimal operation of EV clusters in power systems is being extensively studied. This paper proposes an optimal strategy for charging EVs in parking lots. This strategy is based on the model predictive control (MPC) framework due to the uncertainty of loads, renewable energy sources, and EVs, and considers the voltage stability of the distribution systems. EV chargers in the parking lot charge EVs to minimize the charging cost, which results in a sudden increase in charge load at a certain time. As a result, an excessive voltage drop may occur in the power system at that time. Therefore, we need to minimize the charging cost of EVs while preventing an excessive voltage drop in the power system. The parking lot is stochastically modeled to consider EV uncertainty under the MPC framework. In the MPC framework, the charging schedule of an EV charger in the parking lot is optimized by considering both voltage stability and charging cost minimization in real time. The charging constraints on voltage stability are updated through parameters that change in real time, and thus, errors caused by uncertainty can be reduced. Subsequently, this charging strategy is applied to multiple chargers through Monte Carlo simulation. The proposed charging strategy is verified based on MATLAB/Simulink.

For the coupling problem of longitudinal control and lateral control of vehicles, a two-dimensional (2-D) car-following control strategy for an electric vehicle is proposed in this paper. First, a 2-D car-following model for longitudinal following and lateral lane keeping is established. Then, a 2-D car-following control strategy is designed, and the longitudinal following control and lateral lane keeping control are integrated into one model predictive control (MPC) framework. The 2-D car-following strategy can realize the multi-objective coordinated optimization for longitudinal control and lateral control during the 2-D car-following process, and the multiple objectives are: safety, tracking, comfort, lane keeping, lateral stability and economy. In addition, the economy is important for electric vehicles. The weight matrix of the objective function in the MPC framework is symmetric, and the weight coefficients for the weight matrix have a great influence on the control. The contribution of this paper is: in order to adapt to different dynamic processes of lane keeping, the weight coefficients in the MPC framework are optimized in real-time based on the deep Q network (DQN) algorithm. Finally, to verify the 2-D car-following control strategy, a comparison strategy and two experimental scenarios are set, and simulation experiments are carried out. In scenario 1, compared with the comparison strategy, the lane keeping, lateral stability and economy of the proposed strategy are improved by 37.21%, 17.57% and 9.26%, respectively. In scenario 2, compared with the comparison strategy, the lane keeping, lateral stability and economy of the proposed strategy are improved by 36.45%, 16.66% and 18.52%, respectively. Therefore, compared with the comparison strategy, the 2-D car-following control strategy can have better lane keeping, lateral stability and economy on the premise of ensuring other performances during the 2-D car-following process.

Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.

The increasing utilization of renewable energy sources in low-inertia power systems demands advanced control strategies for grid-forming inverters (GFMs). Conventional Model Predictive Control (MPC) methods, which depend on static models and predefined boundaries, often struggle to preserve frequency stability in dynamic grid conditions. This research presents an Adaptive Model Predictive Control (AMPC) framework to enhance GFM performance in Virtual Synchronous Machine (VSM) mode, ensuring robust frequency stability under uncertainties. The primary issue addressed is the inefficiency of traditional MPC in adapting to dynamic grid conditions. To resolve this, the AMPC framework combines offline reinforcement learning for parameter tuning with online MPC using soft constraints. The offline phase employs a novel Hybrid Crayfish Optimization and Self-Adaptive Differential Evolution Algorithm (COA-jDE) to minimize the cost function , deriving optimal control parameters (Q, R) before real-time deployment. This process, termed cost function minimization using COA-jDE in a reinforcement learning framework, enhances GFM performance by adaptively adjusting virtual inertia and damping. Simulations on a 16MW wind-powered DFIG microgrid demonstrate that AMPC outperforms traditional MPC and VSM methods during grid disturbances, symmetrical faults, islanding, and load shifts. Furthermore, AMPC is computationally efficient compared to conventional reinforcement learning techniques, as adaptation is restricted to offline tuning. The framework not only improves compliance with grid codes (e.g., GC0137, IEEE 1547) but also provides a flexible, resilient control strategy for modern low-inertia grids.

Solving a reliability-performance balancing problem for control systems with degrading actuators under model predictive control framework

Wind power intermittency represents one of the major challenges facing the future growth of grid-connected wind energy projects. The integration of wind turbines and energy storage systems (ESS) provides a viable approach to mitigate the unfavorable impact on grid stability and power quality. In this study, an economic model predictive control (MPC) framework is presented for an integrated wind turbine and flywheel energy storage system (FESS). The control objective is to smooth wind power output and mitigate tower fatigue load. The optimal control problem within the model predictive control framework has been formulated as a convex optimal control problem with linear dynamics and convex constraints that can be solved globally. The performance of the proposed control algorithm is compared to that of a baseline wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is investigated. The simulation results, with various wind scenarios, showed the ability of the proposed control algorithm to achieve the aforementioned objectives in terms of smoothing output power and mitigating tower fatigue load with negligible effect on the wind energy harvested.

Integrating strategic, tactical and operational supply chain decision levels in a model predictive control framework

This paper presents a Model-on-Demand (MoD) approach to system identification and its integration with a three-degree-of-freedom Kalman filter-based Model Predictive Control (3DoF-KF MPC) framework. MoD estimation represents a hybrid of local and global modeling techniques, judiciously formulated to take advantage of both while not being computationally demanding. The 3DoF-KF MPC algorithm enables responses to setpoint changes and measured and unmeasured disturbances to be tuned intuitively and independently, thereby providing superior performance and ease of use over tuning with move suppression and error weights as done with conventional MPC algorithms. The algorithm proposed in this paper involves estimating MoD-based predictive models that are seamlessly integrated into 3DoF-KF MPC to generate control actions that vary with operating conditions. This results in notable performance enhancements in the context of both SISO and MIMO control compared to conventional ARX models. Performance and robustness of the 3DoF-KF MoD MPC framework are demonstrated in this paper through two case studies involving (i) epidemic control of a variant of the widely used SISO Susceptible-Infected-Removed (SIR) model and (ii) a nonlinear, highly interactive MIMO Continuous Stirred Tank Reactor (CSTR) model. The second case study further provides guidelines for designing informative databases for effective MoD-based MIMO identification and implementing 3DoF-KF MPC-based control for a demanding class of systems. Overall, this paper demonstrates technological and practical improvements in system identification and control of nonlinear SISO and MIMO systems through the synergistic integration of MoD estimation and 3DoF-KF MPC, providing an effective approach for operating complex nonlinear process systems.

With its superior performance, the unmanned combat air vehicle (UCAV) will gradually become an important combat force in the future beyond-visual-range (BVR) air combat. For the problem of UCAV using the BVR air-to-air missile (AAM) to intercept the highly maneuvering aerial target, an autonomous attack guidance method with high aiming precision is proposed. In BVR air combat, the best launching conditions can be formed through the attack guidance and aiming of fighters, which can give full play to the combat effectiveness of BVR AAMs to the greatest extent. The mode of manned fighters aiming by manual control of pilots is inefficient and obviously not suitable for the autonomous UCAV. Existing attack guidance control methods have some defects such as low precision, poor timeliness, and too much reliance on manual experience when intercepting highly maneuvering targets. To address this problem, aiming error angle is calculated based on the motion model of UCAV and the aiming model of BVR attack fire control in this study, then target motion prediction information is introduced based on the designed model predictive control (MPC) framework, and the adaptive fuzzy guidance controller is designed to generate control variable. To reduce the predicted aiming error angle, the algorithm iteratively optimizes and updates the actual guidance control variable online. The simulation results show that the proposed method is very effective for solving the autonomous attack guidance problem, which has the characteristics of adaptivity, high timeliness, and high aiming precision.

The optimization of the train speed trajectory and the traction power supply system (TPSS) with hybrid energy storage devices (HESDs) has significant potential to reduce electrical energy consumption (EEC). However, some existing studies have focused predominantly on optimizing these components independently and have ignored the goal of achieving systematic optimality from the standpoint of both electric systems and train control. This paper aims to establish a comprehensive coupled model integrating the train control, DC traction power supply, and stationary HESDs to reach the minimum EEC within the integrated system. The original non-convex and time-varying model is initially relaxed and reformulated as a convex program that can be solved quickly. On this basis, a model predictive control (MPC) framework is proposed to derive specifications in the space-domain-based model and overcome the drawbacks of the time-domain-based model. The designed controller solves the optimization problem for the remaining journey through time sampling, guaranteeing real-time and closed-loop performance. The numerical experiments present five case studies based on the real-world scenario i.e. Guangzhou Metro Line No.7. The results demonstrate that the proposed integrated convex model without stationary HESDs can reduce the accumulated EEC by up to 27.99% compared to the existing field test results. In addition, compared to mixed integer linear programming (MILP) method, the convex program proposed in this work obtains the highest energy savings rate (48. 71%) and significant computational efficiency, ranging from milliseconds (0.03 s) to seconds (4.20 s) in the TPSS with stationary HESDs. Additionally, the convex model features satisfactory modeling accuracy by invoking the nonlinear solver to simulate the power flow of the integrated system and recalculate the EEC.