A Systematic Min–Max Optimization Design of Constrained Model Predictive Tracking Control for Industrial Processes against Uncertainty

Zhang, Ridong; Wu, Shan; Cao, Zhixing; Lu, Jingyi; Gao, Furong

doi:10.1109/tcst.2017.2748059

Cited by 54 publications

(31 citation statements)

References 26 publications

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“…In view of the above-mentioned uncertainty, the ERM problem has become very difficult and complex. Recently, many metaheuristic algorithms have been widely used to solve real-world optimization problems with uncertainty [3].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Velocity Differential Evolutionary Particle Swarm Optimization for Optimal Scheduling of a Distributed Energy Resources With Uncertain Scenarios

Dabhi

Pandya

2020

IEEE Access

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In the MicroGrid environment, the high penetration of uncertain energy sources such as solar Photovoltaics (PVs), Energy Storage Systems (ESSs), Demand Response (DR) programs, Vehicles to Grid (V2G or G2V) and Electricity Markets make the Energy Resource Management (ERM) problem highly complex. All such complexities should be addressed while maximizing income and minimizing the total operating costs of aggregators that accumulate all types of available energy resources from the MicroGrid system. Due to the presence of mixed-integer, discrete variables and non-linear network constraints, it is sometimes very difficult to solve such problem using traditional optimization methods. This paper proposes a new metaheuristic optimization technique entitled the ''Enhanced Velocity Differential Evolutionary Particle Swarm Optimization'' (EVDEPSO) algorithm to tackle the ERM problem. Its key feature is the updation of the Velocity by the terms named as Enhanced Velocity, Cooperation and Stochastic Uni-Random Distribution and position by the term Deceleration Factor. The performance of the proposed method is measured by a case study comprises of 100 scenarios of a 25-bus MicroGrid with high penetration of aforementioned energy sources. IEEE Computational Intelligence Society organized the competition on the above mentioned problem, in which EVDEPSO secured a second rank. The results of EVDEPSO are compared with the competition participated optimization algorithms. It also compared with well-known optimization algorithms such as Variable Neighborhood Search and Differential Evolutionary Particle Swarm Optimization. The comparison results show that the performance of EVDEPSO is superior in terms of the Ranking Index (R.I) and Average Ranking Index (A.R.I) as compared to the aforementioned algorithms. For effective comparative analysis of algorithms, standard statistical test named as One-Way ANOVA and Tukey Test is used. The results of this test also prove the effectiveness of EVDEPSO algorithm as compared to all tested algorithms. INDEX TERMS Enhanced velocity differential evolutionary particle swarm optimization, distributed energy resource management, smart grid, electric vehicles, demand response, electricity market, energy storage. SYMBOLS SYMBOLS Description DiG Conventional DG units Sup External power suppliers Solar Solar Generation (PV) units ST Energy Storage Systems (ESSs) Ev Electric vehicles(Ev) Ms Electricity Markets LD Load SE Scenario The associate editor coordinating the review of this manuscript and approving it for publication was Sheng Huang.

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Section: Introductionmentioning

confidence: 99%

Enhanced Velocity Differential Evolutionary Particle Swarm Optimization for Optimal Scheduling of a Distributed Energy Resources With Uncertain Scenarios

Dabhi

Pandya

2020

IEEE Access

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“…After all, the actual model mismatch may be caused by actuator faults or system internal faults. Therefore, this kind of fault-tolerant control is also passive fault-tolerant control [11][12]. Zhang et al proposed a new constrained MPC control [11] and applied it to the injection molding machine.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this kind of fault-tolerant control is also passive fault-tolerant control [11][12]. Zhang et al proposed a new constrained MPC control [11] and applied it to the injection molding machine. Industrial processes are considered to be time and batch dependent, i.e.…”

Section: Introductionmentioning

confidence: 99%

Genetic-Algorithm-Optimization-Based Predictive Functional Control for Chemical Industry Processes Against Partial Actuator Faults

2020

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Actuator faults, which are common in industrial processes, can make the controller fail to achieve the desired control objectives, which may lead to the degradation of control performance. In order to solve this problem, this paper proposes a predictive functional control based on genetic algorithm optimization. Firstly, an extended dimension discrete switched model is constructed, which consists of a state difference variable, tracking error and a new state variable including tracking error. In this model, the performance index function based on a genetic optimization algorithm is selected, and its parameters are adjusted and the controller is designed. Then, under the obtained control law, the switching signal is designed and the range of uncertainty caused by the actuator fault is given to realize the robustness of the system. At the same time, the corresponding robustly sufficient conditions are presented. The advantage of this design is to avoid the disadvantages of manually adjusting the performance parameters, and the system has good tracking performance. Finally, taking the typical injection molding process of chemical production process as an example, the speed and pressure parameters are controlled, and compared with the traditional control method, the effectiveness and feasibility of the proposed method are verified. INDEX TERMS Chemical industry processes, partial actuator faults, genetic-algorithm-optimization design, predictive functional control

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“…Although it is difficult to design the Lyapunov function for the latter solution, its dimensionality and computing load are smaller than those of the former solution. If the control process involves uncertainties and external perturbations, then the stability of MPC will be negatively affected, while the H-infinity model predictive control strategy [31], [32] can analyze the stability of uncertain closed-loop systems, reduce the impact of perturbations on the system's control performance, and enable the controlled objects to remain robust to external perturbations. Therefore, such control methods are widely used.…”

Section: Introductionmentioning

confidence: 99%

Improved H-Infinity Hybrid Model Predictive Fault-Tolerant Control for Time-Delayed Batch Processes Against Disturbances

Chen

2020

IEEE Access

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An H-infinity model predictive fault-tolerant control strategy is proposed for multi-phase batch processes with interval delay and actuator failures. First, state variables, state errors and output tracking errors are introduced to establish an extended-state-space switched system model. Then, based on this model, a predictive fault-tolerant control law is designed for tracking the set point by the output that satisfies the requirements of the optimal performance index under input and output constraints. The feasibility conditions for the solvability of the control law are presented in the form of linear matrix inequalities. In addition, the designed switching law is constructed, and the gain of the control law is obtained via the optimization algorithm. This design has several advantages: the output tracking is faster, the tracking performance is superior, and the trace is smoother at the switching time. Finally, through a comparison with traditional methods, the effectiveness and feasibility of this method are demonstrated via injection molding simulation. INDEX TERMS Batch production systems, added delay, fault tolerant control, linear matrix inequalities, Lyapunov methods, predictive models, switching systems, predictive control.

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A Systematic Min–Max Optimization Design of Constrained Model Predictive Tracking Control for Industrial Processes against Uncertainty

Cited by 54 publications

References 26 publications

Enhanced Velocity Differential Evolutionary Particle Swarm Optimization for Optimal Scheduling of a Distributed Energy Resources With Uncertain Scenarios

Enhanced Velocity Differential Evolutionary Particle Swarm Optimization for Optimal Scheduling of a Distributed Energy Resources With Uncertain Scenarios

Genetic-Algorithm-Optimization-Based Predictive Functional Control for Chemical Industry Processes Against Partial Actuator Faults

Improved H-Infinity Hybrid Model Predictive Fault-Tolerant Control for Time-Delayed Batch Processes Against Disturbances

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