Demand Side Electric Energy Consumption Optimization in a Smart Household Using Scheduling and Model Predictive Temperature Control

Energy-efficient operation of the thermal management system in electric vehicles via integrated model predictive control

Discussion on: “Improved MPC Design based on Saturating Control Laws”

Battery is the core equipment of the electrical vehicle (EV), battery life, and performance are critical to EV. Only motor variables are considered in most model predictive control (MPC) strategy in which battery indexes are neglected. A model predictive speed control (MPSC) strategy with adjustable C-rate of battery is proposed in this paper on EV with permanent magnet synchronous machine (PMSM). The rest capacity of battery is involved in the cost function, and the operating speed is related to the rest capacity of battery which adjusts the C-rate of the discharging currents further to protect battery. As the EV runs, the C-rate is decreased step by step according to the improved cost function, and the energy is saved to prolong mileage. The benefits of the proposed method including discharging capacities and operating mileage are corroborated by the simulation and repetitive experimental results under same conditions.

Brain-machine interfaces (BMIs) are human-machine integration systems that provide an interface between the brain and a machine to sense cortical neuronal activity for the purpose of restoring impaired motor tasks. In our previous work [1], an optimal design of BMIs based on artificial sensory feedback was developed using model predictive control which relied on neuronal activity in the form of spiking. From a real implementation perspective, a more generalized framework that utilizes spiking is proposed in this paper. Specifically, a charge-balanced intra-cortical micro-stimulation (ICMS) current and a network of spiking neurons are adopted to compensate the lost feedback information. Next, an artificial sensory feedback framework using the network of spiking neurons is designed based on model predictive control (MPC) strategy, and an optimization problem is formulated according to this framework. Since the charge-balanced ICMS current is composed of several integer parameters, the optimization problem also includes some integer decision variables and is hard to be solved. In this paper, a heuristic population-based search algorithm called particle swarm optimization (PSO) algorithm is used to solve this optimization problem. Considering the updated particles may violate the input constraints, additional constraints are designed to guarantee that the decision variables can satisfy the input constraints. Finally, simulation results show the effectiveness of the designed closed-loop BMIs during recovery of natural performance.

This work investigates the event‐driven model predictive control problem for perturbed nonlinear systems with input and state constraints. Firstly, a new event‐driven triggering condition, considering the gradient and accumulation of the error between the optimal and actual states at adjacent moments with a state‐dependent relative threshold is designed with a guaranteed positive inter‐event time to avoid Zeno behaviour. Secondly, an error gradient and accumulation type event‐driven model predictive control framework is designed based on the new triggering condition and the dual‐mode strategy, for effectively reducing the calculation and communication burden of the model predictive control controller while ensuring desired control performance. Furthermore, the feasibility and the input‐to‐state practical stability (ISpS) of gradient and accumulation‐type event‐driven model predictive control are rigorously guaranteed theoretically. At last, the simulation results are provided to validate the this algorithm.

This paper presents an event-triggered model predictive control (MPC) strategy with learning terminal cost for robotic manipulators containing model uncertainty and input constraints. In the proposed MPC structure, an adaptive predictive model for the robotic system is established by radial basis function neural networks (RBFNNs) firstly. Then, a terminal cost adjusted by the global learning mechanism is constructed. Both global steady-state optimization and transient fast convergence are achieved by adding the learning terminal cost to the MPC scheme. After that, a triggering condition of the MPC solving is developed based on the predictive model’s weights and the predictive tracking error. Besides, the condition to avoid Zeno behavior is obtained. The recursive feasibility of the proposed MPC strategy is verified, and the ultimately uniformly boundedness (UUB) of all variables is proved according to the Lyapunov theorem. Finally, experiments based on an xMate7 Pro robot are conducted to demonstrate the effectiveness of the presented method. Note to Practitioners—The tracking control of robotic manipulators is a common and important problem in industrial applications, such as grasping, loading, unloading, et al. There exist some limitations in existing control methods. For example, general control strategies such as adaptive control, sliding mode control neglect the balance between control costs and expected performance; existing optimal control approaches rarely consider global steady-state optimization and transient fast convergence under the influence of model uncertainty. This paper is motivated by these limitations of robotic control design and inspired by model predictive control and optimal control theory. It develops a novel strategy for tracking control of robotic manipulators, which includes three following items: (1) adopt an approximation model constructed by neural networks as the predictive model for estimating the robotic system; (2) introduce a critic network into the terminal cost of the objective function of MPC for learning global optimal solution; (3) establish an event-triggered mechanism for solving of the optimization problem. The proposed method is verified by experimental results. In our future work, the proposed control strategy will be applied to more industrial processes.

Direct Contact Membrane Distillation (DCMD) is an emerging sustainable desalination technology that can utilize solar energy to desalinate seawater. The low water production rate associated with this technology prevents it from becoming commercially feasible. To overcome this challenge, advanced control strategies may be utilized. An optimization-based control scheme termed Model Predictive Control (MPC) provides a natural framework to optimally operate DCMD processes due to its unique control advantages. Among these advantages are the flexibility provided in formulating the objective function, the capability to directly handle process constraints, and the ability to work with various classes of nonlinear systems. Motivated by the above considerations, this paper proposes two MPC schemes that can maximize the water production rate of DCMD systems. The first MPC scheme is formulated to track an optimal set-point while taking input and stability constraints into account. The second MPC scheme termed Economic Model Predictive Control (EMPC) is formulated to maximize the distilled water flux while meeting input, stability and other process operational constraints. To illustrate the effectiveness of the two proposed control paradigms, the total water production under both control designs is compared. Simulation results show that the DCMD process produces more distilled water when it is operated by EMPC than when it is operated by MPC.

Model-based network-wide optimizations in real-time emergency control of power systems are largely bottlenecked by the fast computational requirements. Furthermore, the operational aspects of modern power and energy systems are going through transformative changes to tackle global renewable energy expansion-related issues. Decentralized operations in power systems are becoming essential with the influx of distributed energy resources (DERs), dynamic loads, and electric vehicles (EVs). This research considers a well-known model-based strategy, Model Predictive Control (MPC), for real-time operation and controls of power systems and proposes novel methods utilizing machine learning, data-driven methods, and operator theory to solve computational challenges by making those real-time for the first time. Additionally, we develop a distributed MPC framework to address the recent needs of power systems' operation and control. In Part I, we first present a machine learning (ML)-accelerated MPC implementation in emergency voltage control problems. This method computes an optimal control strategy for the nominal system model offline and performs successive online control corrections at each control instant to adapt the offline computed controls for real-time scenarios. In the online phase, the required voltage trajectory prediction and its sensitivity computation to control inputs are achieved by machine learning-based approaches, thereby accelerating the overall control computation multiple times. Next, we present an alternative data-learned linear Koopman embedding approach for model predictive emergency voltage control in a power grid. This approach involves a "basis-dictionary free" method to lift the nonlinear power system dynamics to a higher dimensional linear space where MPC is exercised in a real-time fashion. We propose a novel data-driven encoder-decoder architecture, Koopman-inspired deep neural network (KDNN), which supports end-to-end learning from trajectory data. The MPC optimization in linear space makes the problem scalable and rapid for real-time implementations. Part II of this research focuses on distributed MPC design for quasi-static power flow-based voltage control problems under short-term (15-minute) variations of load and generation profiles. To realize the proposed distributed control, first, a centralized voltage control problem assuming grid observability is formulated and next transferred to the distributed versions based on both bus-wise and area-wise decomposition of the network. Distributed versions are solved via the alternating direction method of multipliers (ADMM), utilizing only neighbor-to-neighbor communications to attain optimal solutions. The implementation, efficacy, performance, and robustness of proposed research approaches are validated with IEEE test system benchmarks.

The active battery thermal management system is critical for the security of electric vehicles. In this article, a novel battery thermal management system and the control strategy based on thermoelectric cooling are proposed. A coupling model between the thermoelectric cooler and the battery pack is built by MATLAB/Simscape software. The model precision is verified through the experimental bench test, with a maximal deviation of 0.56 °C (the accuracy of the temperature sensor is ±0.1 °C). Further, a battery thermal management strategy with model predictive control (MPC) is proposed. In the results, it is elucidated that the MPC strategy has a superiority over the proportional‐integral‐derivation (PID) strategy in both the response time and energy consumption. Notably, the MPC strategy achieves a 35.17% reduction in response time and a 28.65% decline in energy consumption under a constant current of 2 C. Furthermore, during the variable H_N_U_F cycle conditions, the control effects are also evident, with an overshoot diminution of 12.2%, a maximum temperature error decrease of 23.85%, a response time reduction of 31.15%, and an energy utilization decline of 31.85%. In this study, a novel perspective in advancing battery thermal management systems through the application of thermoelectric cooler is provided.

Modeling and model predictive control of the BioPower combined heat and power (CHP) plant

This paper presents a model predictive voltage control method for single-inductor multiple-output (SIMO) DC/DC converters operating in the continuous conduction mode (CCM). The state-space model for a SIMO DC/DC converter system is formulated and a Model Predictive Control (MPC) strategy, which has the capability to track the reference voltage automatically and reduce the impact of cross regulation problem, is proposed to regulate the system. Considering that the switches in the converter only have ON/OFF modes, the quadratic programming algorithm is employed to obtain the MPC's solution. Simulation studies are conducted in MATLAB/Simulink to verify the performance of the proposed method.

This paper describes a real-time model predictive control (MPC) scheme for an overhead crane subject to state and input constraints. The constraints are taken into account by means of a transformation technique which transforms the system dynamics with the corresponding constraints into a new unconstrained representation. This method allows to reformulate the underlying optimal control problem (OCP) of the MPC scheme into an unconstrained counterpart. The unconstrained OCP is then solved by means of a fast MPC algorithm that uses a finite number of iterations per MPC step in order to ensure real-time feasibility. Simulation as well as experimental results demonstrate the computational performance of the MPC scheme.

In order to solve the problems of low efficiency and short battery life of single energy pure electric vehicle, the super capacitor is integrated into the energy storage system of electric vehicle, and the energy management strategy based on model predictive control is adopted. The model predictive controller solves the minimum value of the objective function within the predetermined constraint range, and obtains the optimal distribution of load power between the battery and the super capacitor, so as to give full play to the maximum benefit of the hybrid power system and improve the battery life. The simulation results under UDDS, NEDC and NYCC conditions show that compared with the rule-based control strategy, the capacity attenuation of lithium battery under the model predictive control strategy is less and the system efficiency is higher.

This letter presents a novel model predictive control strategy without involving any proportional-integral-differential regulators for practical renewable energy based ac microgrids. The proposed method consists of a model predictive power control (MPPC) scheme and a model predictive voltage control (MPVC) scheme. By controlling the bidirectional buck-boost converters of the battery energy storage systems based on the MPPC algorithm, the fluctuating output from the renewable energy sources can be smoothed, while stable dc-bus voltages can be maintained as the inverters' inputs. Then, the parallel inverters are controlled by using a combination of the MPVC scheme and the droop method to ensure stable ac voltage output and proper power sharing. Compared with the traditional cascade control, the proposed method is simpler and shows better performance, which is validated in simulation on MATLAB/Simulink and on real-time laboratory platform.

In this paper, a “third generation” benchmark problem that focuses on the control of wind excited response of a tall building, using the Model Predictive Control (MPC) scheme, is presented. A 76 story, 306 m tall concrete office tower proposed for the city of Melbourne, Australia, is being used to demonstrate the effectiveness of MPC. The MPC scheme is based on an explicit use of a prediction model of the system response to obtain the control actions by minimizing an objective function. Optimization objectives in MPC include minimization of the difference between the predicted and desired response trajectories, and the control effort subjected to prescribed constraints. By incorporating input/output hard constraints, the MPC scheme provides an optimal control force that satisfies the prescribed constraints.