Fuel consumption, subsequent emissions and safe operation of class 8 vehicles are of prime importance in recent days. It is imperative that a vehicle operates in its true optimal operating region, given a variety of constraints such as road grade, lo...
Energy-optimal adaptive cruise control (EACC) is becoming increasingly popular due to its ability to save energy. Considering the negative impacts of system noise on the EACC, an improved modified model predictive control (MPC) is proposed, which com...
For the time being, renewable energy source (RES) penetration has significantly increased in power networks, particularly in microgrids. The overall system inertia is dramatically decreased by replacing traditional synchronous machines with RES. This...
A microgrid cluster is composed of multiple interconnected microgrids and operates in the form of cluster, which can realize energy complementation between microgrids and significantly improve their renewable energy consumption capacity and system op...
In order to reduce the lateral error of path-following control of unmanned vehicles under variable curvature paths, we propose a path-following control strategy for unmanned vehicles based on optimal preview time model predictive control (OP-MPC). Th...
The aim of this study is to develop a model that can accurately calculate building loads and demand for predictive control. Thus, the building energy model needs to be combined with weather prediction models operated by a model predictive controller...
This article presents a novel approach to address the critical issues of stable rotation and energy efficiency in electric vehicles (EVs). To achieve these objectives, we propose a comprehensive control system that leverages the power of optimization...
This paper addresses the implementation of economic-oriented model predictive controllers for the dynamic real-time optimization of the operation of wastewater treatment plants (WWTP). Both the economic-optimizing controller (pure-EMPC) and the econo...
The growing interest in saving building energy has increasingly motivated studies on model predictive control (MPC), where system operation proceeds according to a planned operation strategy. Data-driven models that perform learning using past operat...
Variable-speed induction motor drives spend most of their service life at partial load, where rated-flux field-oriented control (FOC) is inefficient and where loss-minimizing control (LMC) recovers a large part of the loss. LMC, however, is brittle i...
This paper presents a novel robust optimal predictive control approach for a class of nonlinear continuous-time systems perturbed by unknown disturbances. First, a new error state with a linear proportional-integral-differential (PID) structure consi...
This research study proposes an indoor temperature regulation predictive optimal control system that entails the use of both deep reinforcement learning and the Modbus TCP communication protocol. The designed architecture comprises distributed sub-pa...
In some applications of microgrids and distributed generation, it is necessary to feed islanded or stand-alone loads with high-quality voltage to provide low total harmonic distortion (THD). To fulfil these demands, an
Due to the randomness of the intermittent distributed energy output and load demand of a micro-energy-grid, micro-sources cannot fully follow the day-ahead micro-energy-grid optimal dispatching plan. Therefore, a micro-energy-grid is difficult to ope...
As the penetration of renewable energy increases, the distribution grid faces great challenges in integrating large amounts of distributed energy sources and dealing with their output uncertainty. To address this, a multi-time scale optimal dispatch...
This paper proposes a vector modulation-based model predictive current control strategy for a two-stage matrix converter. The switching frequency is kept constant by fixing the switching instantly. The control scheme controls the source reactive powe...
Finite control set model predictive control (FCS-MPC) is an attractive control method for electric drives. This is primarily due to the ease of implementation and robust responses. When applied to rotor current control of the Doubly Fed Induction Gen...
Trajectory planning for a redundant manipulator is a classic problem. However, because it is difficult to precisely evaluate its maximum performance, an optimization method has been typically used. In this study, a novel time-optimal trajectory plann...
High penetration of wind power in the modern power system renders traditional automatic generation control (AGC) methods more challenging, due to the uncertainty of the external environment, less reserve power, and small inertia constant of the power...
An optimization procedure for type 1 Takagi–Sugeno Fuzzy Logic Controller (FLC) parameter tuning is shown in this paper. Ant colony optimization is used to obtain the optimal controller parameters, and only a small amount of post-optimization p...
Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered....
Model predictive control, without strict constraints on the control model, effectively overcomes problems, such as poor system dynamic quality caused by time delay, can improve control accuracy to a certain extent, and can directly process input and...
Robust control methodology for two-area load frequency control model is proposed in this paper. The paper presents a comparative study between the performance of model predictive controller (MPC) and optimized proportional–integral–deriva...
With the rapid development of urban rail transit, train operation control is required to meet increasingly stringent demands in terms of energy consumption, comfort, punctuality, and precise stopping. The optimization and tracking control of speed pr...
Existing control strategies, such as Real-time Optimization (RTO), Dynamic Real-time Optimization (DRTO), and Economic Model Predictive Control (EMPC) cannot enable optimal operation and control behavior in an optimal fashion. This work proposes a no...
The high penetration of photovoltaic (PV) systems and fast communications networks increase the potential for PV inverters to support the stability and performance of microgrids. PV inverters in the distribution network can work cooperatively and fol...
This paper considers a class of large-scale systems which is composed of many interacting subsystems, and each of them is controlled by an individual controller. For this type of system, to improve the optimization performance of the entire closed-lo...
This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regress...
In the heating, ventilation, and air conditioning (HVAC) systems of mushroom growing control rooms, traditional rule-based control methods are commonly adopted. However, these methods are characterized by response delays, leading to underutilization...
Setting sights on the problem of input-output constraints in most industrial systems, an implicit generalized predictive control algorithm based on an improved particle swarm optimization algorithm (PSO) is presented in this paper. PSO has the advant...
Controlling legged robots to run at high speeds or to traverse complex terrains remains challenging due to the difficulty of handling the interaction between the robot and the ground. Impedance control and model predictive control are widely used to...
To improve the effectiveness of model predictive control (MPC) in dynamic traffic signal control strategies, it has been combined with graph convolutional networks (GCNs) and deep reinforcement learning (DRL) technologies. In this study, a neural-net...
With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for int...
Residential water heaters use a substantial amount of electrical energy and contribute to 25% of the energy usage in the residential sector. This raises concern for users in countries with flat rate electricity fees and where fossil fuels are used fo...
The application and performance of Nonlinear Model Predictive Control (NMPC) are critically limited by the computational efficiency of solving nonlinear combinatorial optimization problems. To address this challenge, this study proposes an efficient...
Urban separate sewer systems face significant challenges from rainfall-derived infiltration and inflow (RDII) during the wet season. To achieve the integrated optimization of operational safety, energy consumption, and carbon emissions, this study pr...
To address the stochasticity of real-world driving conditions and the optimality of energy allocation in a hybrid electric vehicle (HEV), this paper proposes a model predictive control (MPC) energy management strategy based on the Stacked–CNN&n...
Optimizing building equipment control is crucial for enhancing energy efficiency. This article presents a predictive control applied to a commercial building heated by a hydronic system, comparing its performance to a traditional heating curve-based...
l-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be direct...
Model predictive control (MPC) is a prominent research focus in motor drives, offering advantages in dynamic response, steady-state accuracy, robustness, and multi-objective handling. However, increasing performance demands in modern systems, coupled...
This paper proposes an optimized predictive control strategy to mitigate the potential leakage current of grid-tied photovoltaic (PV) systems to improve the lifespans of PV modules. In this work, the PV system is controlled with an optimized predicti...
Recent developments in control theory coupled with the growing availability of real-time data have paved the way for improved data-driven control methodologies. This study explores the application of a data-enabled predictive control (DeePC) algorith...
In order to deal with control constraints and the performance optimization requirements in aircraft engines, a new nonlinear model predictive control method based on an elastic BP neural network with a hybrid grey wolf optimizer is proposed in this p...
This paper predicts the network security posture of an ICS, focusing on the reliability of Industrial Control Systems (ICSs). Evidence reasoning (ER) and belief rule base (BRB) techniques are employed to establish an ICS network security posture pred...
For long-term missions in complex seas, the onboard energy resources of autonomous underwater vehicles (AUVs) are limited. Thus, energy consumption reduction is an important aspect of the study of AUVs. This paper addresses energy consumption reducti...
A Model-based predictive current control (MBPCC) has recently become a powerful advanced control technology in industrial drives. However, MBPCC relies on the knowledge of the system model and parameters, being, therefore, very sensitive to parameter...
The distributed model predictive control (DMPC) strategy with particle swarm optimization (PSO) is applied to solve the collision-free trajectory tracking problem for the mecanum-wheeled mobile robot (MWMR) formation. Under the leader–follower...
The centrifugal compressor is the heart that drives the operation of natural gas pipeline systems. Under low-throughput conditions, natural gas often returns back to the compressor through the surge control valve to increase the flow rate and avoid s...
Model predictive control (MPC) approaches are widely used in robotics, because they guarantee feasibility and allow the computation of updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms...
Traditional robust model predictive control (RMPC) strategies often face challenges of excessive conservatism and limited dynamic performance, which can hinder their effectiveness in trajectory tracking applications. To overcome these issues, this pa...
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