Delay‐Range‐Dependent Robust Constrained Model Predictive Control for Industrial Processes with Uncertainties and Unknown Disturbances

Shi, Huiyuan; Li, Ping; Wang, Limin; Su, Chengli; Yu, Jinping; Cao, Jiangtao

doi:10.1155/2019/2152014

Cited by 14 publications

(13 citation statements)

References 54 publications

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“…Denote D(r) {(t, k) : t + k =r, t ≥ 0, k ≥ 0}. For any integerr ≥ max {r 1 , r 2 }, it follows from (35) and 13that…”

Section: Imentioning

confidence: 99%

“…In addition, studies on phase running time multi-phase characteristics are few [31]- [34]. In practice, delay is a widespread phenomenon in industrial process [35], [36], which has rather complicated effect on system stability and it is the key factor for system instability and makes system analysis stability and controller design even harder. Batch processes are influenced by time delay as well.…”

Section: Introductionmentioning

confidence: 99%

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Design of Optimal Hybrid Controller for Multi-Phase Batch Processes With Interval Time Varying Delay

Zhang

Wang

2019

IEEE Access

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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 this model, hybrid iterative learning control is combined with feedback control and a 2D optimal hybrid controller is designed. Secondly, the average dwell time method is adopted to give sufficient conditions for system robust stability in terms of linear matrix inequalities and the upper bound for system's optimal performance index is provided. Besides, the issue of optimal controller design is presented to draw the minimum upper limit of the closed-loop system performance index. The presented control law not only can make the closed-loop system preserving good tracking performance but also ensures its H ∞ performance. Last but not least, the validity of the above strategies is proved by the simulation on an injection molding process. INDEX TERMS Multi-phase batch processes, optimal hybrid iterative learning control, interval time varying delay, average dwell time.

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“…Denote D(r) {(t, k) : t + k =r, t ≥ 0, k ≥ 0}. For any integerr ≥ max {r 1 , r 2 }, it follows from (35) and 13that…”

Section: Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of Optimal Hybrid Controller for Multi-Phase Batch Processes With Interval Time Varying Delay

Zhang

Wang

2019

IEEE Access

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“…The MPC, also named receding horizon control, is recognized as the most efficient and application potential method, considering the advanced process control methodology [17]. In MPC, a process model is used to predict and optimize the future behavior of the system [18].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control With Integral Compensation for Motion Control of Robot Manipulator in Joint and Task Spaces

Chen¹,

Luo²,

Han³

et al. 2020

IEEE Access

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The motion control of robot manipulators is a crucial problem concerning automatically controlled robots. In this work, the model predictive control method with an integral compensation (MPC-I), which compensates for the matched uncertainties due to unmodeled dynamics, is proposed to solve the trajectory tracking problem of robot manipulators in joint and task spaces. First, this paper decouples the joint variables of the robot manipulator using a computed torque control method. The MPC-I method is, thereafter, derived to realize the motion control of the robot manipulators in joint space. To realize the motion control of the robot manipulator in task space, the task space is, thereafter, converted into the joint space, in which the MPC-I method is executed, afterward, to control the robot. Furthermore, an MPC-I variation, in which the inverse kinematics is calculated indirectly, is proposed to achieve the motion control in task space. The novelty of this paper is to propose the MPC-I method and the method of converting task space to joint space with indirect inverse kinematics calculation. The former is suitable for the dynamic control of the robot manipulators in the joint space, and the latter can extend the MPC-I method to dynamic control in task space. To evaluate the performance of the proposed control method, motion control simulations are performed in the task and joint spaces, respectively. Simulation results and comparisons verify the effectiveness of the proposed control approach for the dynamic control of the UR5 robot manipulator. INDEX TERMS Model predictive control (MPC), motion control, robot manipulators, trajectory tracking.

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“…Some extra constraints inherent to some systems, like solution positivity in the case of biological systems or human migrations or the needed behavior robustness against parametrical changes of disturbance actions add additional complexity to the related investigations and need the use of additional mathematical or engineering tools for the research development, [5][6][7]. A large variety of modeling and design tools have to be invoked and developed in the analysis depending on the concrete systems under study and their potential applications as, for instance, the presence of internal and external delays, discretization, dynamics modeling based on fractional calculus, the existence of complex systems with interconnected subsystems, [8][9][10][11][12][13], hybrid coupled continuous/digital tandems, nonlinear systems and optimization and estimation techniques [14][15][16][17][18][19] as well as robotic and fuzzy-logic based systems, [20,21]. In particular, decentralized control is a useful tool for controlling dynamic systems by cutting some links between the dynamics coupling a set of subsystems integrated in the whole system at hand.…”

Section: Introductionmentioning

confidence: 99%

“…It is a common designer´s basic idea in mind for complex control designs to try to minimize the modeling designs and computational loads without significantly losing the system´s performance and its essential properties. For instance, in [8], the dynamic characteristic of a discrete-time system is given as an extended state space description in which state variables and output tracking error are integrated while they are regulated independently. The proposed robust model predictive control is much simpler than the traditional versions since the information of the upper and lower bounds of the time-varying delay are used for design purposes.…”

Section: Introductionmentioning

confidence: 99%

Parametrical Non-Complex Tests to Evaluate Partial Decentralized Linear-Output Feedback Control Stabilization Conditions from Their Centralized Stabilization Counterparts

Sen

Ibeas

2019

Applied Sciences

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. This paper formulates sufficiency-type linear-output feedback decentralized closed-loop stabilization conditions if the continuous-time linear dynamic system can be stabilized under linear output-feedback centralized stabilization. The provided tests are simple to evaluate, while they are based on the quantification of the sufficiently smallness of the parametrical error norms between the control, output, interconnection and open-loop system dynamics matrices and the corresponding control gains in the decentralized case related to the centralized counterpart. The tolerance amounts of the various parametrical matrix errors are described by the maximum allowed tolerance upper-bound of a small positive real parameter that upper-bounds the various parametrical error norms. Such a tolerance is quantified by considering the first or second powers of such a small parameter. The results are seen to be directly extendable to quantify the allowed parametrical errors that guarantee the closed-loop linear output-feedback stabilization of a current system related to its nominal counterpart. Furthermore, several simulated examples are also discussed.

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Delay‐Range‐Dependent Robust Constrained Model Predictive Control for Industrial Processes with Uncertainties and Unknown Disturbances

Cited by 14 publications

References 54 publications

Design of Optimal Hybrid Controller for Multi-Phase Batch Processes With Interval Time Varying Delay

Design of Optimal Hybrid Controller for Multi-Phase Batch Processes With Interval Time Varying Delay

Model Predictive Control With Integral Compensation for Motion Control of Robot Manipulator in Joint and Task Spaces

Parametrical Non-Complex Tests to Evaluate Partial Decentralized Linear-Output Feedback Control Stabilization Conditions from Their Centralized Stabilization Counterparts

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