LSTM-Based Predefined-Time Model Predictive Tracking Control for Unmanned Surface Vehicles with Disturbance and Actuator Faults

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.

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

A habituating blood glucose control strategy for the critically ill

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.

— 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.

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 proposes an improved constrained networked model predictive tracking control design for the chamber pressure of a coke furnace under uncertainty and packet losses. Unlike conventional constrained model predictive control (MPC) strategies that have a limitation in the consideration of both set-point tracking and the dynamic process responses, the system state variables and output tracking errors are combined and thus can be regulated simultaneously in the new MPC scheme. Based on such advantages, there are more degrees of freedom for the subsequent controller design and improved system performance can then be obtained. Case studies on the regulation of chamber pressure of a coke furnace under process uncertainties and packet losses are investigated to verify the proposed approach in comparison with typical traditional constrained MPC schemes.

This study proposes a model predictive stator current control (MPSCC) strategy of doubly fed induction generator (DFIG) under unbalanced grid voltage conditions. Sinusoidal and balanced stator currents injected into the power grid can be ensured due to the direct control of stator currents rather than rotor currents. Model predictive control instead of traditional vector control is adopted, which can increase the current loop bandwidth and obtain faster dynamic responses. Conventional resonant regulators such as second‐order generalised integrators or second‐order vector integrators that are usually used to eliminate unbalanced components in stator currents can also be avoided. Moreover, both extractions of negative sequence components in rotor or stator currents and calculation of commanded rotor current are avoided, which simplifies the control scheme. The proposed MPSCC strategy can also implement the grid connection by introducing the virtual stator currents without any changes in the control scheme. Finally, experimental results based on a 1 kW lab DFIG system are provided to validate the effectiveness of the proposed control strategy.

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 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.

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 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.

Optimal operation and control of (variable-speed) pumped-storage power plants (PSPPs) is essential to meet the growing demands on the dynamics for the stabilization of power distribution grids with an increasing amount of renewable energy sources. Existing work on the control of PSPPs is typically based on rather simplified system models, in particular of the (long) pipeline system. In this article, a nonlinear model predictive control (NMPC) strategy is proposed, which enables fast closed-loop dynamics while keeping all system constraints, including the pressure constraints along the pipeline system. The control strategy is based on a physics-based model, which enables easy parameterization and application to other plant sizes or topologies. To ensure real-time capability of the model predictive control (MPC) strategy, a number of measures are outlined in this article. Finally, the feasibility of the proposed control strategy is demonstrated by detailed simulation studies. An accurate tracking for fast changing desired grid powers as well as a high robustness with respect to parameter uncertainties is demonstrated.