Junfei Qiao

Beijing University of Technology | Bei Gong Da · Faculty of Information Technology

A continuous stirred tank reactor (CSTR) system is widely applied in wastewater treatment processes. Its control is a challenging industrial-process-control problem owing to great difficulty to achieve accurate system identification. This work proposes a deep learning-based model predictive control (DeepMPC) to model and control the CSTR system. Th...

Deep belief network (DBN) is an efficient learning model for unknown data representation, especially nonlinear systems. However, it is extremely hard to design a satisfactory DBN with a robust structure because of traditional dense representation. In addition, backpropagation algorithm-based fine-tuning tends to yield poor performance since its eas...

Deep belief network (DBN) is one of the most feasible ways to realize deep learning (DL) technique, and it has been attracting more and more attentions in nonlinear system modeling. However, DBN cannot provide satisfactory results in learning speed, modeling accuracy and robustness, which is mainly caused by dense representation and gradient diffus...

To comply with the effluent standards and growing demands for safety and reliability, the operation of WWTPs has been considered as a multiobjective control problem. In this paper, a data-driven multiobjective predictive control (MOPC) method was developed to deal with the conflicting control objectives to improve the operation performance of WWTPs...

Multiobjective optimal control (MOC) optimize multiple performance indices of nonlinear systems to obtain setpoints, and design the controller to track the setpoints. However, if the feasibility of the controller is not considered, untraceable setpoints may be obtained. Furthermore, the performance of data-driven MOC may be degraded due to insuffic...

The real-time detection technique and comprehensive characterization of dioxin (DXN) emission concentration during the municipal solid waste incineration process persist as unresolved challenges. Prevailing research predominantly relies on data-driven models, often overlooking the potential benefits derived from fusing combustion mechanism knowledg...

In this article, an evolution-guided adaptive dynamic programming (EGADP) algorithm is developed to address the optimal regulation problems for the nonlinear systems. In the traditional adaptive dynamic programming algorithms, policy improvement is typically reliant on the gradient information, according to the first order necessity condition. Howe...

Surrogate-assisted evolutionary algorithms have been widely employed to solve data-driven optimization problems. However, for offline data-driven optimization, it is very challenging to perform evolutionary search efficiently as well as accurately since no new data is available during the optimization process. To mitigate this issue, a multifidelit...

In this paper, the model‐free robust control problem is investigated for nonlinear systems with a relaxed condition of initial admissible control. An advanced integral reinforcement learning method is developed, which merges the adaptive network‐based fuzzy inference system (ANFIS) and pre‐training of the initial weights. To loose the condition for...

The increasing complexity and scale of the wastewater treatment process (WWTP) demand more and more safety and stability. However, due to the unavoidable existence of external disturbance, sludge bulking is commonly encountered, which can result in risks for the efficient and stable operation of WWTP. To address this problem, a knowledge-data-drive...

Fuzzy neural network (FNN) is regarded as a prominent approach in application of time series modeling. With the capability of fuzzy reasoning, FNN can capture temporal patterns from the time-series samples. However, the existing FNNs may suffer from the temporal pattern distortion because possibly multi-scale features can not be explored sufficient...

Optimal control is developed to guarantee nonlinear systems run in an optimum operating state. However, since the operation demands of systems are dynamically changeable, it is difficult for optimal control to obtain reliable optimal solutions to achieve satisfying operation performance. To overcome this problem, a knowledge-data driven optimal con...

This paper designs two novel event-triggered control (ETC) schemes based on the critic learning technique for constrained discrete-time nonlinear systems. First, starting from the stability of the constrained system, a static ETC method is developed to reduce the computational burden. Then, a nonnegative dynamic variable is introduced into the stat...

Evolutionary multitasking optimization (EMTO), owing to its advantage of knowledge sharing, is capable of resolving multiple optimization tasks concurrently. Considering the evolutionary progresses between tasks may be inconsistent, it is necessary for EMTO to regulate the knowledge transfer strategy (KTS), which can alleviate the negative transfer...

Water quality prediction is an indispensable task in water environment and source management. The existing predictive models are mainly designed by data-driven artificial neural networks (ANNs), especially deep learning models for large-scale water quality prediction. However, the state of water environment is a dynamic process where the stationari...

Establishing an accurate model of dynamic systems poses a challenge for complex industrial processes. Due to the ability to handle complex tasks, modular neural networks (MNN) have been widely applied to industrial process modeling. However, the phenomenon of domain drift caused by operating conditions may lead to a cold start of the model, which a...

For the data-driven multimodal multiobjective optimization problems (MMOPs), the inevitable uncertainties will lead to distortion of multiple peak landscapes, thus causing slow convergence in complex landscapes. To solve this problem, a robust multimodal multiobjective particle swarm optimization (RMMPSO) is designed to alleviate slow convergence....

Set-point optimization of wastewater treatment process (WWTP) is critical for energy savings but is challenging due to complex nonlinear mechanisms and measurement noises. To address this optimization problem, a mechanism-data-driven multiobjective optimization method is developed to alleviate deficiencies in mechanisms and process data. First, a m...

Neural network control has been developed into an efficient strategy to guarantee the safe and steady operation of wastewater treatment process (WWTP). However, due to the complex mechanism and serious damage of sludge bulking in WWTP, it is significant for neural network control to achieve the timely self-helaing of operation. Therefore, the goal...

In this paper, a new stabilizing value iteration Q-learning (SVIQL) algorithm is presented to achieve the online evolving control for unknown nonlinear systems. To achieve this, we aim to establish the data-driven evolving control framework, ensure the stability of iterative policies derived from SVIQL, and reduce the computational cost in online l...

The wastewater treatment process (WWTP) is beneficial for maintaining sufficient water resources and recycling wastewater. A crucial link of WWTP is to ensure that the dissolved oxygen (DO) concentration is continuously maintained at the predetermined value, which can actually be considered as a tracking problem. In this article, an experience repl...

Deep belief network (DBN) is an effective deep learning model, which can learn the complex data by extracting features hierarchically. However, the successful application of DBN depends on the suitable size of the structure (the number of hidden neurons), which is still an open problem. Currently, the network structure size is basically determined...

To approach the brain-like neural network and further improve the performance of the modular neural network (MNN), an adaptive evolutionary modular neural network with intermodule connections (EA-ICMNN) is proposed in this study. The EA-ICMNN is composed of a group of multilayer neural networks. Unlike traditional MNNs, in addition to the intramodu...

This article aims to design a model‐free adaptive tracking controller for discrete‐time nonlinear systems with unknown dynamics and asymmetric control constraints. First, a new Q‐function structure is designed by introducing the control input into the tracking error of the next moment, in order to eliminate the final tracking error, avoid the stead...

Carbon monoxide (CO) is a toxic gas emitted during municipal solid waste incineration (MSWI). Its emission prediction is conducive to pollutant reduction and optimized control of MSWI. The variables of MSWI exhibit redundant and interdependent correlations with CO emissions. Furthermore, the mapping relationship is difficult to characterize. Theref...

In this article, an adaptive critic scheme with a novel performance index function is developed to solve the tracking control problem, which eliminates the tracking error and possesses the adjustable convergence rate in the offline learning process. Under some conditions, the convergence and monotonicity of the accelerated value function sequence c...

Task similarity is a major requisite to trigger knowledge sharing in evolutionary multitasking optimization (EMTO). Unfortunately, most of the existing EMTO algorithms only focus on the similarity between population distributions of tasks, but ignore the search behavior of populations, which may degrade the performance of cross-task knowledge shari...

In many fields, spatiotemporal prediction is gaining more and more attention, e.g. , air pollution, weather forecasting, and traffic forecasting. Water quality prediction is a spatiotemporal prediction task. However, there are several challenges in water quality prediction: 1) Water quality time series has a complex nonlinear relationship, making...

Coastal water-quality prediction is the indispensable work to prevent the red tide and marine pollution accidents, which also provides the effective assistance to study ocean carbon sink. Due to the multiple inducing-factors and their spatio-temporal coupling effects, the water-quality prediction not only needs to be supported by big data, but also...

Dynamic periodic event-triggered control (DPETC) is a type of event-triggered control (ETC) that inherits the excellent properties of ETC to avoid unnecessary communication and further extends the inter-event intervals by introducing a dynamic variable. Despite their beneficiary effects, matters like communication scheduling have been proposed due...

Depending on high quality images, industrial vision technologies can basically oversee all the industrial production processes, such as workpiece processing and assembly automation, which play a highly significant role in promoting detection automation and production capacity in assembly lines. Unlike the natural scene images which consist of riche...

The furnace temperature (FT) control is the key for ensuring the stable operation and effective pollution reduction in municipal solid waste incineration (MSWI) processes. However, conventional control strategies encounter challenges in effectively managing FT due to uncertainties associated with material composition, feeding modes, and equipment m...

In this paper, based on the adaptive critic control method, an improved event-based trajectory tracking mechanism of continuous-time (CT) nonlinear multiplayer zero-sum games (MZSGs) is established. It is worthy of note that previous papers studying the trajectory tracking issue of nonlinear CT MZSGs only apply to the case where the reference traje...

This article presents two new event-triggered control (ETC) schemes based on the online critic learning technique, which aims at tackling the optimal regulation problem of discrete-time constrained nonlinear systems with the disturbance input. First, a novel stability criterion condition is designed to obtain an initial admissible policy pair by us...

Dioxins (DXN) is a persistent environmental pollutant that poses risks such as weakened immune system, teratogenic and carcinogenic effects. Municipal solid waste incineration (MSWI) plant is one of the major DXN generation sources. It is imperative to implement the monitoring and control. However, the harsh environment prevents the use of conventi...

In wastewater treatment processes (WWTPs), data and knowledge are employed to build an effective model for monitoring its operation. Unfortunately, they are difficult to be fused due to their heterogeneity, which struggles to provide a united and reliable solution. To solve this issue, a data-knowledge-driven inductive learning (DKIL) method is int...

The safety performance function (SPF) is an extensively employed tool in road safety assessment. However, traditional modeling methods often fall short of effectively capturing the intricate interdependencies among diverse traffic variables. To address this limitation, a feature importance analyzable resilient deep neural network (RDNN) is proposed...

Municipal solid waste incineration (MSWI) is a dynamic industrial process involving complex physical and chemical reactions. Due to the uncertain municipal solid waste (MSW) composition and dynamic operation conditions, it is difficult to guarantee optimal operation for the MSWI process. To solve this problem, a data-driven optimal control scheme i...

Fuzzy neural network (FNN) is a structured learning technique that has been successfully adopted in nonlinear system modeling. However, since there exist uncertain external disturbances arising from mismatched model errors, sensor noises, or unknown environments, FNN generally fails to achieve the desirable performance of modeling results. To overc...

The accurate and timely prediction of nitrogen oxides (NOx) emissions ensures eco-friendly and efficient operations for municipal solid waste incineration (MSWI) plants. Due to the high nonlinearity and uncertainty in MSWI processes, constructing an efficient prediction model remains challenging. This work proposes a comprehensively improved interv...

In this thesis, we construct improved value iteration (VI) and online VI structures, in a bid to tackle the optimal tracking control problem for discrete-time nonlinear systems. Note that asymmetric control restraints and the discount factor are considered. First, related properties are discussed for novel VI, involving the monotonicity of the iter...

In this article, we propose a dual‐mode event‐triggered predictive control method for nonlinear systems with bounded disturbances. The proposed method contains two triggering mechanisms, namely, the hybrid threshold‐based event‐triggered model predictive control (HETMPC) mechanism and the event‐triggered linear quadratic regulator mechanism. The fo...

In recent years, the application of function approximators, such as neural networks and polynomials, has ushered in a new stage of development in solving optimal control problems. However, considering the existence of approximation errors, the stability of the controlled system cannot be guaranteed. Therefore, in view of the prevalence of approxima...

This article investigates the secure consensus problem of general linear multiagent systems with denial-of-service (DoS) attacks. Owning to the existence of DoS attacks, it is challenging to investigate the event-triggered control of multiagent systems in a fully distributed manner. This article presents a novel dynamic event-triggered mechanism to...

A novel dynamic event-triggered control strategy is proposed by utilizing the adaptive critic learning (ACL) technique for nonlinear continuous-time systems subject to disturbances in this paper. To address the transformation of the robust-optimal control problem, a modified cost function containing the disturbance term is introduced. The dynamic e...

This paper is concerned with a novel integrated multi-step heuristic dynamic programming (MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the zero cost function, MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. Then, the stability of the system is analyzed using contro...

In this article, the dissolved oxygen (DO) concentration control problem in wastewater treatment process (WWTP) is studied. Unlike existing control strategies that control DO concentration at a fixed value, here we develop a different control framework. Under the proposed control framework, an intelligent control method of DO concentration based on...

Multitask optimization (MTO) mainly utilizes knowledge transfer among tasks to address multiple optimization problems in parallel. However, the decision space dimensions of different tasks often differ, which leads to the failure of knowledge transfer. Therefore, it is a challenging problem to transfer knowledge among tasks with different dimension...

Optimal control problems are ubiquitous in practical engineering applications and social life with the idea of cost or resource conservation. Based on the critic learning scheme, adaptive dynamic programming (ADP) is regarded as a significant avenue to address the optimal control problems by combining the advanced design ideas such as adaptive cont...

In this paper, a novel optimal control scheme is established to solve the multi-player zero-sum game (ZSG) issue of continuous-time nonlinear systems with control constraints and unknown dynamics based on the adaptive critic technology. To relax the requirement of system dynamics, a neural network-based identifier is applied to reconstruct the unkn...

Due to the large uncertainty in the municipal solid waste incineration (MSWI) process, the furnace temperature of the MSWI process is difficult to control and the controller is updated frequently. To improve the accuracy and reduce the number of controller updates, a novel event-triggered control method based correntropy self-organizing TS fuzzy ne...

Many real−world applications are dynamic multi−objective optimization problems (DMOPs). The transfer of knowledge in the evolutionary process is believed to have advantages in solving DMOPs. However, most existing works can hardly be focused on the effectiveness of knowledge, which may lead to the negative transfer to degrade searching performance...

Due to high complexity and time-variant operation, as well as increasingly requirements for water quality, stability and reliability, wastewater treatment process (WWTP) is regarded as an adaptive control problem. In this study, a data-driven adaptive control with deep learning (DRAC-DL) is developed to improve the operational performance of WWTP....

In this article, the generalized $N$ -step value gradient learning (GNSVGL) algorithm, which takes a long-term prediction parameter $\lambda$ into account, is developed for infinite horizon discounted near-optimal control of discrete-time nonlinear systems. The proposed GNSVGL algorithm can accelerate the learning process of adaptive dynamic pr...

In this paper, by making use of recurrent neural networks (RNNs), a novel event‐based robust optimal controller is designed for a category of continuous‐time (CT) nonlinear nonaffine systems with disturbances. For tackling the optimal‐robust problem transformation and the performance guarantee, a system identifier is developed to reconstruct the sy...

Wastewater treatment process (WWTP), consisting of a class of physical, chemical, and biological phenomena, is an important means to reduce environmental pollution and improve recycling efficiency of water resources. Considering characteristics of the complexities, uncertainties, nonlinearities, and multitime delays in WWTPs, an adaptive neural con...

In this paper, the decentralised tracking control (DTC) problem is investigated for a class of continuous-time large-scale systems with external disturbance by utilising adaptive dynamic programming (ADP). Firstly, the DTC problem is solved by designing corresponding optimal controllers of the isolated subsystems, which are formulated with N augmen...

Evolutionary multitasking optimization (EMTO) has capability of performing a population of individuals together by sharing their intrinsic knowledge. However, the existed methods of EMTO mainly focus on improving its convergence using parallelism knowledge belonging to different tasks. This fact may lead to the problem of local optimization in EMTO...

Multi-objective optimal control is widely applied in wastewater treatment processes (WWTPs) to ensure the security and stability of the operation processes. However, for the existing stepwise multi-objective optimal control (SMOC) algorithms, the unknown disturbances will further influence the obtain of set-points and the design of control laws, wh...

The main goal of multitask optimization (MTO) is the parallel optimization of multiple different tasks. However, since different tasks in the MTO problem usually have heterogeneous characteristics, it is difficult to realize the positive knowledge transfer among tasks, resulting in poor convergence. To cope with this problem, a multi-task particle...

Dioxin (DXN) is a persistent organic pollutant produced from municipal solid waste incineration (MSWI) processes. It is a crucial environmental indicator to minimize emission concentration by using optimization control, but it is difficult to monitor in real time. Aiming at online soft-sensing of DXN emission, a novel fuzzy tree broad learning syst...