Networked Predictive Control Research Articles

Intelligent vehicle lateral tracking algorithm based on neural network predictive control

Intelligent vehicle lateral tracking algorithm based on neural network predictive control

Kinematic analysis and advanced control of a vectored thruster based on 3RRUR parallel manipulator for micro-size AUVs

Abstract Autonomous underwater vehicles (AUVs) have played a pivotal role in advancing ocean exploration and exploitation. However, traditional AUVs face limitations when executing missions at minimal or near-zero forward velocities due to the ineffectiveness of their control surfaces, considerably constraining their potential applications. To address this challenge, this paper introduces an innovative vectored thruster system based on a 3RRUR parallel manipulator tailored for micro-sized AUVs. The incorporation of a vectored thruster enhances the performance of micro-sized AUVs when operating at minimal and low forward speeds. A comprehensive exploration of the kinematics of the thrust-vectoring mechanism has been undertaken through theoretical analysis and experimental validation. The findings from theoretical analysis and experimental confirmation unequivocally affirm the feasibility of the devised thrust-vectoring mechanism. The precise control of the vector device is studied using Physics-informed Neural Network and Model Predictive Control (PINN-MPC). Through the adoption of this pioneering thrust-vectoring mechanism rooted in the 3RRUR parallel manipulator, AUVs can efficiently and effectively generate the requisite motion for thrust-vectoring propulsion, overcoming the limitations of traditional AUVs and expanding their potential applications across various domains.

Constrained model predictive control for networked jump systems under denial‐of‐service attacks and time delays

AbstractThis article addresses the problem of model predictive control of networked jump systems in the presence of DoS attacks and time delays. In the structural framework of the network predictive control system, we mathematically model the networked jump system by using Markov chains to describe the time delays and a polytope model to describe the jump system phenomenon, considering the properties of DoS attacks and time delays. Based on this, we propose a strategy to lessen the effect of network constraints on the control performance of the system. This strategy involves the corresponding control inputs from the control sequence for real‐time active compensation. It includes adjusting the control sequence application length variation based on the duration of the DoS attacks and time delays at each moment. In addition, we demonstrate the recursive feasibility of the control strategy and the global asymptotic stability of the control system from a theoretical perspective through the Lyapunov stability theory. Finally, the effectiveness of the proposed strategy is verified by simulation arithmetic.

Observer‐based networked predictive controller with bounded and unknown time‐varying delay

AbstractThis paper investigates a networked control system with bounded and time‐varying delays. Also, an observer‐based predictive controller is developed for the active compensation of the network communication delay since it may lead to poor performances or even unstable dynamics for the systems. The existence of the state observer can be established by choosing an appropriate Lyapunov function, and to do this, Luenberger observer gain is computed with Linear Matrix Inequality (LMI) based on the stability conditions of the system. The practical aspect of this research, different from previous works, is the accessibility of the states that are not always available for the system. Controller and observer gains and other essential variables are derived through LMI. In the closed‐loop system, which is modeled as a switching system, the switches are based on communication delays. Using the Lyapunov stability method for switching systems, sufficient LMI conditions are derived to guarantee the stability of the closed‐loop system. Finally, the results of the simulation have demonstrated the performance of the methodology presented.

Physics-Informed Neural Network Modeling and Predictive Control of District Heating Systems

Physics-Informed Neural Network Modeling and Predictive Control of District Heating Systems

Neural network predictive controller based on the improved TPA-LSTM model for ultra-supercritical units

To mitigate the impact of large-scale renewable energy power on the national grid in China, it is imperative to enhance the flexible peaking capability of coal-fired thermal power units. The coordinated control system, central to the load control of coal-fired units, faces challenges such as multivariable coupling, sluggish response, and uncertain coal quality parameters. This paper introduces a neural network predictive controller based on the improved TPA-LSTM model, aimed at addressing these issues. Initially, a data-driven control model is established to break through the limitations of traditional linear predictive control and effectively handle disturbance uncertainties. Then, a multivariable coordinated control strategy based on the neural network controller is designed, achieving effective decoupling of multiple parameters and ensuring high adaptability across all load conditions. Additionally, by integrating an automatic model updating mechanism, the system can recalibrate in real-time when model mismatches occur due to equipment aging, maintenance, or changes in coal quality, thereby enhancing overall control performance. Simulation results demonstrate that this strategy has excellent control effectiveness, meeting the flexible peaking demands of 1000 MW ultra-supercritical units. The calibration feature of the data-driven model significantly improves control performance following model mismatches.

Analysis of DC motor for process control application using neural network predictive controller

A DC motor is a critical actuator in process control systems. This study investigates the effectiveness of a deep learning (DL) based Neural Network Predictive Controller (NNPC) for precise DC motor speed control. The NNPC anticipates the motor’s future behaviour based on its current state and control inputs. The controller then optimally generates inputs to minimise tracking errors and enhance system performance. The NNPC demonstrated a remarkable reduction in Mean Squared Error (MSE), achieving a training MSE of 2.75 × 10−14 and the best validation MSE of 9.2023 × 10−14. These quantitative outcomes affirm the reliability and robustness of the proposed NNPC for speed control in DC motor systems across diverse applications.

Finite region stability and networked predictive control for 2-D nonlinear time-delayed systems with quantization, packet dropouts and random disturbances

In this paper, we focus on the finite region stability (FRS) and networked predictive control (NPC) problems for two-dimensional (2-D) networked control systems (NCS) with time delays, quantization errors, packet losses and random disturbances described by the Roesser model. The random packet dropouts and disturbances are modeled as Bernoulli process, and the sufficient conditions of FRS and finite region boundedness (FRB) for 2-D NCS are obtained by using the expectation to process the Lyapunov function difference, so as to better study the transient performance of the systems. According to the recursive formula, an active compensation scheme for time delays of NPC is proposed, which takes into account quantization errors, packet losses, nonlinearity and interferences. Introducing FRS and FRB into 2-D NCS and considering the above factors in NPC scheme are new attempts made in this paper. An example is used to demonstrate the validity of the conclusions in the end.

Neural network predictive control for fault detection and identification in DFIG with SMES for low voltage ride-through requirements

The increasing reliance on renewable energy sources brings to light the operational challenges of doubly fed induction generators (DFIGs), particularly under grid disturbances. This study introduces an innovative approach employing Neural Network Predictive Control (NNPC) for fault detection and identification (FDI) in DFIG systems, integrated with Superconducting Magnetic Energy Storage (SMES) to meet Low Voltage Ride-Through (LVRT) requirements. Our method uniquely combines NNPC for dynamic wind speed prediction and precise control of rotor-side and grid-side converters with the stability enhancement offered by SMES. Simulation results demonstrate a notable improvement in DFIG performance under variable wind conditions and grid failures. The system-maintained voltage stability at the Point of Common Coupling (PCC) with less than 5% deviation during transients and ensured consistent power output, marking a significant advancement in LVRT compliance. However, the model assumes steady-state grid conditions and does not account for extreme weather scenarios, indicating areas for future research. This study's findings contribute valuable insights into the robust operation of DFIGs within modern renewable energy grids.

Neural network predictive control of converter inlet temperature based on event‐triggered mechanism in flue gas acid production

AbstractThe process of smelting non‐ferrous metals results in significant emissions of flue gas that contains sulfur dioxide (SO), which is very harmful to the environment. Through precise control of converter inlet temperature, it is feasible to enhance the conversion ratio of SO and simultaneously mitigate environmental pollution by generating acid from flue gas. Because of the high degree of uncertainty in smelting process, converter inlet temperature is challenging to regulate and controller frequently needs updating. To improve control performance and decrease controller update times, an event‐triggered neural network model predictive control (ETNMPC) strategy is proposed. First, long short‐term memory (LSTM) prediction model and model predictive controller are developed. Second, it is decided whether to update the existing controller by designing an event‐triggered mechanism. Finally, using real data from a copper facility in Jiangxi Province, the temperature control experiment of converter inlet is carried out. Simulation results demonstrate that the proposed ETNMPC outperforms conventional time‐triggered method in terms of control performance, greatly lowers the times of controller updates, and significantly lowers computation costs and communication burden.

Neural network predictive control in renewable systems (HKT-PV) for delivered power smoothing

The reduction of power fluctuations from intermittent renewable sources is one of the most pressing challenges today. Recent research has shown that prediction and control mechanisms, when combined with energy storage systems, significantly contribute to improving these techniques. However, substantial research gaps still exist regarding the optimization of energy storage system operability. This article introduces an innovative power smoothing method based on neural network predictive control, in conjunction with the exponential moving average method. The proposed approach encompasses the ability to substantially reduce energy fluctuations, optimize battery state of charge, and mitigate ramp rates, thereby preventing deep discharges that shorten battery lifespan. Furthermore, the control system's primary objective is to optimize energy exchange with the grid, surpassing the performance offered by other conventional power smoothing methods. The control system excels in optimizing energy exchange within the network, surpassing conventional methods. Extensive testing on the University of Cuenca microgrid reveals a consistently more stable and higher battery charge compared to conventional methods. Numerical results for underscore the method's effectiveness with a fluctuation suppression rate of 30.78 % compared to 34.85 % (low pass filter) and 36.22 % (ramp rate) methods respectively. The enhanced voltage profiles at the common coupling point ensure the delivery of high-quality and stable power.

Networked explicit hybrid predictive control of an industrial benchmark process

Hybrid systems have acquired deep attention in research as many systems in industries and laboratories manifest hybrid dynamic behaviors. Optimal control design for hybrid systems exhibits challenges for controlling the interacting continuous and discrete dynamics. A double-tank hybrid system (DTHS) is proposed in this paper with a mixed-integer actuation mechanism. The proposed DTHS aims to control the liquid level of the two tanks simultaneously by a networked hybrid predictive controller. Hybrid frameworks are provided to investigate the system through reachability analysis. An explicit hybrid model predictive control (EHMPC) has been developed to control the levels of the DTHS. Hardware-In-the-Loop (HIL) simulation is performed for assessing the performance of the EHMPC with a hardware real-time networked controller. The networked real-time controller is constructed and implemented using the Node.js frameworks. The simulation and realtime results are presented and discussed to evaluate the hybrid controller performance.

Distributed Secondary Control for DC Microgrids With Time-Varying Communication Delays: A Networked Predictive Control Scheme

Distributed Secondary Control for DC Microgrids With Time-Varying Communication Delays: A Networked Predictive Control Scheme

Consensus-based secondary control for DC microgrids with communication delays via a networked predictive control strategy

In today’s cyber–physical microgrid systems, the consensus-based secondary control is generally utilized to settle the voltage deviation and rough current allocation issues at the primary control level. However, time delays follow inevitably the introduction of sparse communication networks, and most existing works adopt passive tolerance approaches. To actively alleviate the unavoidable delay effect in microgrids’ communication networks, a networked predictive control (NPC) strategy is proposed for an islanded DC microgrid subject to time delays in this paper. Firstly, the predictive approaches for both voltage and current are developed based on the cyber–physical microgrid model. Unlike the practice of passively tolerating time delays, the NPC strategy is proposed to actively compensate for the effect of communication delays by estimating real-time voltage and current values using the previously obtained prediction models. Moreover, to prove the generality of the developed method, the microgrid systems’ stability can be derived from the Schur stability of the closed-loop system, thus the DC microgrid can achieve voltage regulation and proportional current sharing simultaneously. Finally, the performance of our method against the time delay effect is validated by extensive experiments on an islanded 48-V DC microgrid system, in terms of its feasibility, delay tolerance ability, and robustness to load changes and communication faults. Experimental results demonstrate the effectiveness and superiority of the NPC strategy.

Predictive trajectory tracking control for the USV in networked environments with communication constraints

The trajectory tracking control problem of under-actuated unmanned surface vessels (USVs) suffering from communication constraints (network delay, packet loss, packet disorder) as well as external environment disturbance is investigated in this paper. To solve the aforementioned trajectory control problem, a nonlinear networked predictive control strategy based on the discrete sliding mode framework is proposed. Initially, using the forward Euler discretization method, a novel discrete virtual velocity control law is designed to transform trajectory tracking control into virtual velocity tracking control. Then, a sliding mode controller is suggested to suppress external disturbance of the USV. In addition, a networked sliding mode predictive control (NSMPC) scheme is proffered to guarantee active compensation of the communication constraints and stability of the trajectory tracking system simultaneously. Afterward, a theoretical analysis is given to show that the closed-loop system is uniformly bounded stable after the introduction of the digital network. Finally, extensive comparative simulation examples are provided to demonstrate the effectiveness of the proposed control strategy.

Model predictive coordinated cooperative control mechanism for multiagent systems based on priority negotiation

Aiming at the control requirements in complex industrial processes and the problem of poor decision-making flexibility of agents in state/output difference-based consensus strategies, a novel model predictive coordinated cooperative control mechanism is proposed using multiagent systems as the computational paradigm. The proposed mechanism considers the global state of the system, the interaction of control actions among agents, the constraints of the process, and energy consumption. According to the proposed coordinated cooperative control mechanism, each agent must accomplish three tasks at each time step. First, each agent proposes its own “winner-takes-all” control scheme and calculates its payoff. Then, adjacent agents determine their own priorities at the current time step according to the payoffs of the “winner-takes-all” scheme by using a designed pairwise game mechanism. Next, each agent, in order of priority, calculates its sequence of actual optimal control input increments by applying the dynamic predictive control approach based on the dynamic model of the system. Finally, the effectiveness of the proposed coordinated cooperative control mechanism is verified by comparing it with the networked coordination-based distributed model predictive control approach and the centralized model predictive control approach based on the temperature control process of heating an alumina ceramic block in a high-power microwave reactor with six microwave sources.

Event-based predictive control for interval type-2 Takagi–Sugeno fuzzy systems under Kalman filter

In this article, the event-based predictive control problem for a class of interval type-2 Takagi–Sugeno fuzzy system is investigated. To reduce the influence of Gaussian noise, an improved Kalman filter is proposed, in which the system state is filtered or not depends on an event-triggered mechanism. Then, by designing the network predictive control scheme via interval type-2 fuzzy model, some sufficient conditions are derived, which guarantee the Schur stabilization of the concerned system subject to network delays. Finally, the correctness and effectiveness are verified by two simulation examples.

Event-based networked predictive control of cyber-physical systems with delays and DoS attacks

Event-based networked predictive control of cyber-physical systems with delays and DoS attacks

Model-free distributed integral sliding mode predictive control for multi-agent systems with communication delay

This paper explores the problem of distributed cooperative tracking control for networked nonlinear multi-agent systems (MASs) with communication delays. The difficulty of distributed control of subsystems across several geographic areas is addressed by utilizing MASs in this study. To overcome modeling difficulties, a model-free control algorithm is employed. To handle networked delay, a network predictive control approach is utilized. To address these issues in a unified manner, a novel distributed model-free integral sliding mode predictive control strategy is proposed. This approach requires simple data to make predictions to achieve distributed tracking performance while also actively compensating for delays. The effectiveness of the proposed method is demonstrated through simulations and experimental results.

Event-Triggered Control for Networked Predictive Control Systems With Time Delay and External Disturbance

This paper develops a novel combination of the event-triggered disturbance estimation and rejection (DER) technique, and the networked predictive control (NPC) strategy for the networked control systems (NCSs) with external disturbance and time delay. Firstly, the Luenberger observers are constructed to estimate system state and disturbance, respectively. Next, with the aid of the observer-based NPC strategy, the effect of network-induced time delay is compensated effectively. Simultaneously, the DER technique is incorporated into the networked predictive controller design for the disturbance rejection. Furthermore, to cope with the networked bandwidth constraint, the event-triggered DER&NPC technique is embedded in the controller design of the considered NCSs to ensure the desired closed-loop system performance with a more efficient network resource utilization. Finally, two simulation examples are exploited to validate the proposed results.