Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization

Zou, Tao; Wu, Shan; Zhang, Ridong

doi:10.1016/j.isatra.2017.12.022

Cited by 35 publications

(21 citation statements)

References 36 publications

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“…Actuator faults are known as one of the main faults in control systems, especially in NCSs. In the literature of FTC, actuator faults are generally modeled with a diagonal fault matrix Φ = diag { φ 1 , φ 2 , … , φ m } , which is considered to be a known constant matrix (Liu et al, 2016; Wang et al, 2014; Zhihong et al, 2011; Zou et al, 2018), an unknown matrix with known bounds on each φ l (Chen et al, 2015; Khatibi and Haeri, 2017; Sakthivel et al, 2017), or stochastic with a known probability distribution (Peng et al, 2010; Tian et al, 2010; Zha et al, 2017). The possible actuator faults are then modeled by u F ( k ) = Φ u ( k ) , where Φ = diag { φ 1 , φ 2 , … , φ m } is the fault matrix.…”

Section: System Descriptionmentioning

confidence: 99%

“…where φ l ∈ R , ( l = 1 , 2 , … , m ) are m uncorrelated random variables with expectation μ l and variance δ l 2 . It is worth mentioning that the known fault matrix model u F ( k ) = Φ c u ( k ) presented in Liu et al (2016), Zou et al (2018), Zhihong et al (2011) and Wang et al (2014), can be included in the proposed model by defining E { Φ } = Φ ¯ = Φ c , δ l 2 = 0 …”

Section: System Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Robust finite–time stochastic stabilization and fault–tolerant control for uncertain networked control systems considering random delays and probabilistic actuator faults

Bahreini

Zarei

Razavi–Far

et al. 2019

Transactions of the Institute of Measurement and Control

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This paper focuses on the problem of reliable finite–time stochastic stability (FTSS) for uncertain networked control systems (NCSs). A Markovian jump system (MJS) model with partly unknown transition probabilities (TPs) for the NCSs with random delays, data packet dropouts (disorders as well) and stochastic actuator faults is established to describe the closed–loop system. A mode-dependent static output feedback controller is designed taking only the measured outputs into account. A new criterion is also derived in terms of linear matrix inequalities (LMIs) to ensure reliable FTSS of the closed–loop system, based on the stochastic stability theory. Simulation studies on a benchmark numerical example, as well as an unstable numerical example can verify the effectiveness of the proposed method.

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Section: System Descriptionmentioning

confidence: 99%

Section: System Descriptionmentioning

confidence: 99%

Robust finite–time stochastic stabilization and fault–tolerant control for uncertain networked control systems considering random delays and probabilistic actuator faults

Bahreini

Zarei

Razavi–Far

et al. 2019

Transactions of the Institute of Measurement and Control

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“…In this strategy, fault detection is implemented by utilizing a set-valued observer, fault isolation is performed by setting manipulations, and FTC is carried out based on the designed robust MPC law. Zou et al [18] proposed a model predictive FTC method together with genetic algorithm for batch processes in the presence of disturbances and partial actuator faults.…”

Section: Introductionmentioning

confidence: 99%

Probability-Based Robust Stochastic Predictive Fault-Tolerant Control for Industrial Processes With Actuator Failures and Interval Time-Varying Delays

et al. 2021

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A stochastic robust predictive fault-tolerant control (SRPFTC) method is proposed for industrial processes with interval time-varying delays and actuator failures occurring under a certain probability. Its main contribution is to propose a new control strategy for time-delay systems where actuator failures meet a certain probability, which improve the traditional fault-tolerant control (FTC) method. First of all, an extended state space model composing of the state and output error is established to describe a type of industrial processes with interval time-varying delays, uncertainties, unknown disturbances and actuator failures occurring under a certain probability. Secondly, based on stochastic Lyapunov function theory, robust model predictive control method, time delay upper and lower bounds and linear matrix inequality theory, the stochastic stability conditions of the above the equivalent model are given. Then the control law updated in real time according to the probability of actuator failure is also given. This not only ensures the tracking control under different conditions (normal or fault), but also reduces the energy consumption of traditional FTC methods. Lastly, the effectiveness and feasibility are verified by the case study of the TTS20 water tank.

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“…Ding et al [7] proposed a novel ILC scheme to control the uncertain multiple-input multiple-output discrete system under actuator faults. In [8], a model predictive control method was suggested for the injection molding batch process with partial actuator failures. Due to the uncertainties of actuator fault, the control system design is often mismatched [1,9].…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Dai

et al. 2020

Complexity

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As the actuator faults in an industrial process cause damage or performance deterioration, the design issue of an optimal controller against these failures is of great importance. In this paper, a fractional-order predictive functional control method based on population extremal optimization is proposed to maintain the control performance against partial actuator failures. The proposed control strategy consists of two key ideas. The first one is the application of fractional-order calculus into the cost function of predictive functional control. Since the knowledge of analytical parameters including the prediction horizon, fractional-order parameter, and smoothing factor in fractional-order predictive functional control is not known, population extremal optimization is employed as the second key technique to search for these parameters. The effectiveness of the proposed controller is examined on two industrial processes, e.g., injection modeling batch process and process flow of coke furnace under constant faults, time-varying faults, and nonrepetitive unknown disturbance. The comprehensive simulation results demonstrate the performance of the proposed control method by comparing with a recently developed predictive functional control, genetic algorithm, and particle swarm optimization-based versions in terms of four performance indices.

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Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization

Cited by 35 publications

References 36 publications

Robust finite–time stochastic stabilization and fault–tolerant control for uncertain networked control systems considering random delays and probabilistic actuator faults

Robust finite–time stochastic stabilization and fault–tolerant control for uncertain networked control systems considering random delays and probabilistic actuator faults

Probability-Based Robust Stochastic Predictive Fault-Tolerant Control for Industrial Processes With Actuator Failures and Interval Time-Varying Delays

Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

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