A model predictive control for a multi-chiller system in data center considering whole system energy conservation

— The objective of this paper is to investigate the Model Predictive Control (MPC) strategy, analyze and compare the control effects with Proportional-Integral-Derivative (PID) control strategy in maintaining a level of spherical tank system using VFD. An advanced control method, MPC has been widely used and well received in a wide variety of applications in process control, it utilizes an explicit process model to predict the future response of a process and solve an optimal control problem with a finite horizon at each sampling instant. In this paper, an algorithm is developed for controlling the speed of the pump using VFD to maintain the level of the spherical tank system. Next, the system is modeled and linearized for analysis and design. Model is implemented in a simulation environment based on LabVIEW. Both MPC and PID control methods are tried to design the controller for the water level system in a spherical tank and compared the results in terms of peak time, settling time, overshoot, and steady-state error under various operational conditions including time delays. The results showed the advantage of MPC for dealing with the system dynamic over PID and could be designed for more complex and fast system dynamics even in presence of constraints and Reduction of power consumption by the pump due to usage of VFD for controlling level by varying the speed of the pump based on the level of the tank. Keywords: Non Linear - Spherical Tank System – Level Control – Variable Frequency Drive – PID Controller – MPC Controller – Modeling - LabVIEW. Cite this Article R. Dhanasekar, S. Vijayachitra, M. Pravin Savaridass. Controlling of Level Process in Nonlinear System Using Variable Frequency Drive. Current Trends in Information Technology. 2019; 9(1): 24–33p.

Waste heat from a data centre (DC) is a promising heat source because of the evenly distributed load profile and intensive waste heat generation. Many studies have proven the substantial financial benefits for the district heating (DH) operators by integrating DC waste heat with DH systems. However, there is a scarcity of research focusing on the optimal control of the DH system after integrating DC waste heat to further improve the system’s economic performance. Therefore, this study aimed to further improve the economic performance of a DH system with DC waste heat by utilizing a model predictive control (MPC) scheme. This MPC scheme employed an economic-related objective function and formulated technical operational constraints. The proposed MPC scheme was tested on a campus DH system in Norway by simulation. Compared to a traditional rule-based control approach, the MPC scheme reduced the monthly energy cost by 1.8% while providing more stable chilled water for the DC cooling system.

A habituating blood glucose control strategy for the critically ill

The high-quality operation of a superheated steam temperature (SST) system is a core fact of the safety, economy, and stability of thermal power units. How to improve the control performance of an SST system under large-varying operating conditions is becoming a research hotspot. To solve this challenge, this paper proposes a proportional integral derivative (PID) control strategy based on integral gain scheduling. Based on the introduction of the SST system and classical model under typical operating conditions, the control difficulties of the SST system are analyzed theoretically. Then, a PID control strategy, based on integral gain scheduling, is introduced for the cascade control structure, and the stability of the proposed control strategy is analyzed by calculating the PID stability region. Finally, the effectiveness of the proposed method is verified under nominal and uncertain conditions, where the proposed method could obtain satisfactory tracking and disturbance rejection control performance. Simulation results show the valuable application prospects of the proposed method.

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

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

This study aims to examine the use of Proportional Integral Derivative (PID) and Model Predictive Control (MPC) in Load Frequency Control (LFC). A balance between the generation and the load is necessary to ensure the consistency of the electrical supply. These days, MPC techniques are becoming popular due to their merits over conventional PID controllers. In this study, performance of the designed PID and MPC schemes has been tested and compared for two area interconnected power system. This system consists hydro unit in area-1 and three Thermal units in area-2. A step load perturbation scenario in both areas has been obtained to evaluate the effectiveness of the designed control strategies. The results show that both the designed control approaches performed successfully however, the MPC scheme outperform the PID scheme in terms of time domain specifications like reduced oscillation and smaller settling time. It can be concluded that MPC can be used as a secondary controller for LFC applications in future research.

Atmospheric pressure plasma jets (APPJs) have widespread use in materials processing and biomedical applications. Safe and effective operation of hand-held APPJs is, however, highly sensitive to the intrinsic variability of plasma characteristics as well as exogenous disturbances such as variations in the separation distance between the device tip and target surface. This paper investigates feedback control of the thermal effect and intensity of a kHz-excited APPJ in helium using a proportional-integral control strategy and a model predictive control (MPC) strategy. A data-driven model of the APPJ, developed for a linear operating range using the subspace identification method, is utilized in the design of both control strategies. Real-time control experiments reveal that feedback control is crucial for effective operation of the APPJ in the presence of step disturbances in the device tip-to-surface separation distance. It is observed that the MPC strategy can more effectively regulate the multivariable dynamics of the APPJ for effective setpoint tracking and constraint handling in the face of disturbances.

In response to the problems of high thermal inertia, easy condensation at the end of the radiation cooling fresh air system, and high energy consumption of traditional control strategies, this paper proposes a prediction method based on one-dimensional convolutional coupled long short-term memory neural network (CNN-LSTM) to achieve accurate prediction of indoor temperature, dew point temperature, and radiation end surface temperature; Based on this framework, a model predictive control (MPC) strategy is formulated, and deep reinforcement learning (DRL) is introduced to construct a DRL-MPC hybrid control strategy to further optimize control performance. The simulation experiment results show that the MPC strategy exhibits high stability in maintaining indoor thermal comfort, effectively reducing the risk of condensation, and can effectively reduce energy consumption during the cooling period. However, the MPC strategy has insufficient adaptability to dynamic environments and is prone to thermal comfort deviation during nighttime operation, which needs further optimization. The DRL-MPC strategy can effectively alleviate the above problems, reducing the proportion of uncomfortable time from 13.4% to 3.5%, and achieving an additional 8.6% reduction in system energy consumption while maintaining the thermal comfort index (PMV ∈ [-0.5,0.5]). This hybrid control strategy provides a reliable solution for the intelligent and efficient operation of radiative cooling systems.

This paper discusses the difference between two tracking control strategies for a two-link robotic manipulator and compares the tracking results of the two strategies, in terms of accuracy and overshoots prevention. The design and simulation of the control approaches are based on the development of a nonlinear model for the complex dynamics of the manipulator system. The system modeling is based on Euler-Lagrange equations. The first strategy is a Proportional Integral Derivative (PID) control. Its design is based on the trial and error online tuning method, which is applied directly to control the two-joints of the robotic manipulator. On the other hand, the second strategy is a novel Model Predictive Control (MPC) strategy, which its design is based on feedback linearization analysis technique that utilizes the nonlinear model of the manipulator’s complex dynamics to design a feedback linearization control strategy to work as a primary control loop.

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.

In this paper, the online correction model predictive control (MPC) strategy is presented for partial differential equation (PDE) unknown spatially-distributed systems (SDSs). The low-dimensional MIMO models are obtained using principal component analysis (PCA) method from the high-dimensional spatio-temporal data. Though the linear low-dimensional model is easy for control design, it is a linear approximation for nonlinear SDSs. Thus, the MPC strategy is proposed based on the online correction low-dimensional models, where the state at a previous time is used to correct the output of low-dimensional models and the spatial output is correct by the average deviation of the historical data. The simulations demonstrated show the accuracy and efficiency of the proposed methodologies.

Multi-power sources are included in hybrid electrical vehicles, which leads to multi-driving modes co-existing when driving the vehicle. However, the frequent driving mode switching (DMS) will probably need the engine to be started frequently, which can result in extra fuel consumption. So, avoiding unnecessary DMS should be fully considered when designing the control strategy. For solving this problem, a model predictive control (MPC) strategy integrating Markov chain driving intention identification is put forward. First, the component models of the powertrain system are established. Second, according to the real driving cycle data, a driving intention model based on the Markov chain is designed according to the real driving cycle data. Then the MPC-based control strategy aiming at reducing DMS times is proposed by integrating the cost of DMS. Finally, the proposed control strategy is contrasted with three other control strategies to verify its validity in reducing the mode switching times and improving the fuel economy.

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 study presents a novel sensorless model predictive control (MPC) strategy of a wind‐driven doubly fed induction generator (DFIG) connected to a dc microgrid. In this configuration, the stator is directly connected to the dc microgrid through a diode rectifier, and the rotor is fed by only a voltage source converter (VSC). This connection structure brings considerable benefits such as simple control scheme and reduced power converter cost. In order to obtain sensorless operation, a simple and effective sensorless position detection technique has been proposed. It is based on the detection of the stator frequency, and it is designed to operate without any machine parameters. Thus, the proposed sensorless method offers as a model‐free solution. In addition, the MPC strategy has been used as a current controller to overcome the weaknesses of the inner control loop and to consider the discrete‐time operation of the VSC. The proposed control system is robust against machine parameters, speed, and load variations. To verify the dynamic and steady‐state performances of the proposed sensorless MPC scheme under various speed and load conditions, experimental studies are performed with 6.4 kW DFIG. Experimental results aim to show that the proposed sensorless MPC strategy works properly at steady state and in transient.