Jingxian Yu's research while affiliated with Liaoning University and other places

Publications (21)

Article
In view that the previous control methods usually rely too much on the models of batch process and have difficulty in a practical batch process with unknown dynamics, a novel data-driven two-dimensional (2D) off-policy Q-learning approach for optimal tracking control (OTC) is proposed to make the batch process obtain a model-free control law. First...
Article
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A comprehensive optimization design method of new constrained robust model predictive fault-tolerant control is proposed for industrial processes with partial actuator failures and unknown disturbances. A new state space model which is composed of differential state variables, output tracking errors, and new state variables related to output tracki...
Article
The work deals with composite iterative learning model predictive control (CILMPC) for uncertain batch processes via a two dimensional Fornasini–Marchesini (2D-FM) model. A novel equivalent error system is first presented which is composed of state error and tracking error. Then an iterative learning predictive updating law is constructed by 2D sta...
Article
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Concerning two dimensional (2D) multi-phase batch processes, a delay-range-dependent optimal hybrid iterative learning control (2D-OILC) scheme is presented. Firstly, the process is converted into a 2D-FM time delay switched system in different dimensions by introduction of state errors and system output tracking errors among batches. According to...
Article
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In this paper, a 2D terminal constrained model predictive iterative learning control method of batch processes with time delay is proposed to deal with time delay, input and output constraints, and disturbances in batch processes. Firstly, an iterative learning control law is designed for the given batch process; then the state error and output tra...
Article
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A fuzzy predictive fault-tolerant control (FPFTC) scheme is proposed for a wide class of discrete-time nonlinear systems with uncertainties, interval time-varying delays, and partial actuator failures as well as unknown disturbances, in which the main opinions focus on the relevant theory of FPFTC based on Takagi-Sugeno (T-S) fuzzy model descriptio...
Article
A new design method of two-dimensional (2D) controller for multi-phase batch processes with time delay and disturbances is proposed to ensure the stability of the control system and realize efficient production in industry. The batch process is first converted to an equivalent but different dimensional 2D-FM switched system. Based on the 2D system...
Article
Asynchronous switching between the controller and the active subsystems in multiphase batch processes may cause the systems to be unstable around the switching instants. In view of this, an average dwell-time method-based iterative learning control (ILC) scheme is proposed in this paper. First, the multiphase process is represented as an equivalent...
Article
In this paper, a limited rolling time domain-based hybrid tracking control method for injection and packing-holding phase in injection molding process is proposed. A more flexible controller is designed by adding the adjustable weighting coefficient. Firstly, the input and output models in different phases are established based on the collected inp...
Article
This paper studies a robust constrained model predictive fault-tolerant control (RCMPFTC) problem for a class of industrial processes with uncertainties, interval time-varying delay, unknown disturbances and partial actuator failures, in which the main idea is in the relevant theory of RCMPFTC based on a novel extended state space model description...
Article
Full-text available
A delay-range-dependent robust constrained model predictive control is proposed for discrete-time system with uncertainties and unknown disturbances. The dynamic characteristic of the discrete-time system is established as a new extended state space model in which state variables and output tracking error are integrated and regulated independently....
Article
A robust fuzzy predictive control (RFPC) based on Takagi-Sugeno (T-S) fuzzy model is proposed for systems with uncertainties, time-varying delays, unknown disturbances, as well as strong nonlinearity. First, the T-S fuzzy model is built by a number of linear submodels and nonlinear membership functions. Then, by introducing the output tracking erro...
Article
For nonlinear batch processes with actuator faults and external disturbances, a fault-tolerant guaranteed cost controller is proposed based on fuzzy iterative learning control. The linear process model is treated by a sector nonlinear method into a T-S fuzzy faulty model, then the obtained model is transformed into the 2D Roesser equivalent model u...
Article
Considering inevitable time delays, actuator faults and other issues in practical industrial batch processes, a two dimensional (2D) robust iterative learning fault-tolerant controller design is presented. The design consists of two parts. First, combining the state error and output error, the iterative learning control is incorporated into the est...
Article
In this paper, a new two‐dimensional (2D) fuzzy composite iterative learning fault‐tolerant control strategy using a 2D Takagi‐Sugeno fuzzy model is proposed for batch processes with time delay and actuator faults. Firstly, based on the local‐sector nonlinearity method, a 2D Takagi‐Sugeno fuzzy model representing the nonlinear batch process with ac...
Article
Concerning multi-phase batch processes with delay, disturbance, and actuator faults, the design of 2D robust hybrid composite iterative learning fault-tolerant guaranteed cost controller is put forward. First of all, hybrid iterative learning control law is designed and the multi-phase batch process with interval time-varying delay is converted to...
Article
In order to cope with system disturbances in multi-phase batch processes with different dimensions, a hybrid robust control scheme of iterative learning control combined with feedback control is proposed in this paper. First, with a hybrid iterative learning control law designed by introducing the state error, the tracking error and the extended in...
Article
A robust design of a hybrid iterative learning fault-tolerant guaranteed cost control scheme is proposed for a class of multi-phase batch processes under faults and disturbances. Firstly, based on an equivalent two-dimensional Fornasini-Marchsini (2D-FM) switched system with actuator faults varying within an allowable range, a 2D robustly hybrid co...
Article
The injection molding process is a typical multi-phase batch process. As the filling and packing-holding phases share the same actuator, faults occurring in the actuators may cause serious impact on the performance and running time. Because these two phases are of crucial importance in relation to the final quality of the product, to solve this pro...
Article
In this paper, the iterative learning fault-tolerant control problem for multi-phase batch processes with uncertainty and actuator faults was studied. Firstly, making full use of the characteristics of two-time dimension (2D) feature and repetitiveness in batch processes and introducing the state error and output error between the adjacent batches,...
Article
In this paper, a T-S model-based fuzzy delay-range-dependent iterative learning control (ILC) scheme is developed for highly nonlinear batch processes with interval time-varying delays. The two-dimensional (2D) T-S time-delay model is constructed to remedy the disadvantage that the overall linear model cannot sufficiently describe the nonlinear bat...

Citations

... In the first PFTC case, the controller is constant and system can accommodate only a confined number of known faults. On the other hand, AFTC can accommodate faults by restructuring or reconfiguration the controller structure using the fault information [4]- [6]. ...
... Research on ILC system-design methods and convergence performance on the basis of 2D system theory has become an important direction for the development of ILC technology [10,11]. Based on the theory of 2D systems, the work in [12,13] discussed a comprehensive predictive ILC strategy to ensure the fast convergence performance of the learning process under model error and uncertain disturbance. In [14,15], a high-order ILC controller was proposed according to the stability theory of linear repetitive processes, which optimized the monotonic convergence and robust performance of the system. ...
... As a consequence, the investigation of singular systems is both theoretically and practically important [1,4,39]. It is worth noting that time delays are common in many physical plants, and they can have a substantial negative impact on performance and even the stability of practical systems [7,27,32,42,43,44]. Singular models and time-delay phenomena are general enough to enable some fundamental results from the theory of state-space systems to be extended to this class of systems (see for instance [3,5,10,15,17,45]). ...
... If there are non-repetitive disturbances in the system or batch length is inconsistent, the control effect of iterative learning control method will be greatly worsened. Yu et al. [24] proposed a robust predictive fault-tolerant control method for MPBP with time-varying delays, uncertainty and disturbances. This control method can effectively solve the problem of inconsistent batch length in the iterative learning control method. ...
... Research on ILC system-design methods and convergence performance on the basis of 2D system theory has become an important direction for the development of ILC technology [10,11]. Based on the theory of 2D systems, the work in [12,13] discussed a comprehensive predictive ILC strategy to ensure the fast convergence performance of the learning process under model error and uncertain disturbance. In [14,15], a high-order ILC controller was proposed according to the stability theory of linear repetitive processes, which optimized the monotonic convergence and robust performance of the system. ...
... After the nonlinear system is linearized, the linear model obtained cannot completely match the nonlinear system. References [3][4][5][6] treat the part of the linearized model that does not match the nonlinear system as uncertainties and combine with robust control to guarantee the system is stable. But as the system runs for a long time, the aging of equipment and other factors, it will bring more uncertainties, which will bring about the continuous decrease of the control performance of the controller. ...
... e iterative learning controller can be designed without precise model information, which has the advantages of simple structure, batch processes, etc. [4,5]. ...
... Iterative learning control (ILC) is an effective control methodology that can enhance the tracking accuracy of dynamical systems by learning from previous iterations data [1,2], and has been applied to many applications like biomedical engineering [3,4], batch processes [5][6][7], robotic systems [8,9], urban traffic control [10,11], and so forth [12][13][14][15][16]. A more detailed discussion about various ILC designs in the continuous-or discrete-time domain can be found in [1,17]. ...
... Wang et al [30] presented the optimal designed algorithm of 2D hybrid robust iterative learning fault-tolerant guaranteed control. Obviously, the optimal control of batch processes has attracted considerable attention [53][54][55]. The non-optimal method guarantees the stability of the system and achieves tracking control. ...
... Recently, fault tolerance techniques for the robust model predictive control design problem have gained more attraction in dealing with faulty nonlinear systems [17][18][19][20]. In [21], a new robust constrained model predictive fault-tolerant control method was proposed for uncertain time-delay systems with unknown disturbances and partial actuator failures. ...