Alternative solutions to multi-variate control performance assessment problems

Huang, Biao; Ding, Steven X.; Thornhill, Nina F.

doi:10.1016/j.jprocont.2005.09.003

Cited by 42 publications

(34 citation statements)

References 23 publications

(54 reference statements)

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“…Next, we give the switched system measurement data tensor space representation, which is a fundamental step for switched control system performance benchmark estimation. Then, we can extend the data-driven subspace approach for multivariate linear systems in [35] to tensor space modeling Figure 5. Unfolding for tensor space modeling F of switched system operator with additive disturbance including three subsystems, and each subsystem has three inputs and three outputs.…”

Section: Performance Benchmark Estimationmentioning

confidence: 99%

“…The representation shows that this tensor space modeling is quite general, it can capture logical switching feature, for example, the interaction nature of control and protection systems for safety-important process. Consequently, the data-driven performance benchmark estimation algorithm based on higher-order singular value decomposition that extends subspace approach [8,35] can be derived for the calculation of control performance benchmark for performance measure. Moreover, by using orthogonal projection in tensor space, the prediction error method formulation of a performance benchmark estimation procedure is then presented for the switched control system performance assessment, which is solvable using Tensor Toolbox for MATLAB.…”

Section: Introductionmentioning

confidence: 99%

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Performance assessment of switched control systems based on tensor space approach

Jiang

Shi

2015

Adaptive Control & Signal

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Based on tensor space, a new performance assessment approach is proposed for switched control systems in this paper. Switched control system is widely existing in industrial processes, especially in safety-important processes where the systems controlled may be switched by protection logic during accident. Clearly, bad performance of control and protection strategies will eventually drive the system to a dangerous condition. To treat this problem, one may set up a performance assessment procedure for the switched control systems using multiple model method, where performance is evaluated through assessment of every individual submodel of the switched system. Obviously, this approach ignores interaction nature between control algorithm and protection strategy. The proposed method can capture the interaction nature of control and protection systems. In the tensor space, the interacting relation of control and protection systems can be synthetically represented by adding the logical switching space. Specifically, the tensor space modeling representation for switched systems has been developed, which can actually model the high coupling interaction of control and protection systems. The data-driven tensor space algorithm based on higher-order singular value decomposition has also been developed to assess the performance of switched control systems. By using orthogonal projection in tensor space extended from the matrix space, prediction error approach has been employed to obtain the optimal prediction error variance being as the control performance benchmark for performance assessment. Finally, numerical simulation examples are presented to illustrate the rationality and effectiveness of tensor space approach by comparing with the multiple model approach.

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Section: Performance Benchmark Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance assessment of switched control systems based on tensor space approach

Jiang

Shi

2015

Adaptive Control & Signal

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“…This result demonstrates the multiobjective and often conflicting objective of maximizing both the set-point tracking and disturbance rejection ability of the control strategy and tuning parameters. Studies have shown that while the ITAE criterion is useful as an objective function for set-point tracking, an objective function that incorporates both the ITAE and minimum variance control (MVC) should be employed to take into account disturbance rejection as well [53]. MVC is a useful benchmark to maximize the disturbance rejection ability.…”

Section: Case 4: Effect Of Stochastic Disturbancesmentioning

confidence: 99%

Model-Based Control-Loop Performance of a Continuous Direct Compaction Process

et al. 2011

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This study is concerned with enhanced modelbased control of a continuous direct compression pharmaceutical process. The control-loop performance is assessed in silico and results obtained will be incorporated into the pilot plant facility of the continuous direct compaction process at the NSF Engineering Research Center of Rutgers University. The models used in the study are obtained via system identification from a combination of first principlesbased dynamic models, experimental data, and/or literature data. The main objective of the paper is to formulate an effective control strategy at the basic/regulatory level, for the integrated continuous operation of the direct compaction process, and to maintain the process at the desired set-points, taking into account the multivariable process interactions and disturbances. Simulations show that that at very mild interactions, the proposed regulatory control strategy is able to maintain set-points at desired values. However, at moderate to high process interactions, oscillatory behavior of controlled variables is seen. The presence of disturbances also resulted in poor control-loop performance. Results also lend credence to the development of advanced control strategies in such scenarios and will be addressed in future work. Optimal control tuning parameters are obtained from a derivative-free optimization algorithm.

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“…Among these methods, prediction error approach is one of the most promising ones which does not need the interactor matrix. Because the concepts of prediction error approach and closed-loop potentials as defined in [28] play an important role in determining the performance of nitrate cascade controllers, we describe these concepts in detail in the following part.…”

Section: Control Performance Assessmentmentioning

confidence: 99%

Performance assessment of cascade controllers for nitrate control in a wastewater treatment process

Liu

2011

Korean J. Chem. Eng.

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A cascade control strategy is proposed to the benchmark simulation model 1 (BSM1) to enhance the treatment performance of nitrogen removal in a biological wastewater treatment plant. The proposed control approach consists of two control loops, a primary outer loop and a secondary inner loop. The method has two controllers of which the primary loop has a model predictive control (MPC) controller and the secondary loop has a proportional-integralderivative (PID) controller, which is a cascade MPC-PID controller. The primary MPC controller is to control the nitrate concentration in the effluent, and the secondary PID controller is to control the nitrate concentration in the final anoxic compartment. The proposed method controls the nitrate concentrations in the effluent as well as in the final anoxic reactor simultaneously to strictly satisfy the quality of the effluent as well as to remove the effects of disturbances more quickly by manipulating the external carbon dosage rate. Because the control performance assessment (CPA) technique has the features of determining the capability of the current controller and locating the best achievable performance, the other novelty of this paper is to suggest a relative closed-loop potential index (RCPI) which updates the CPA technology into a closed-loop cascade controller. The proposed method is compared with a cascade PID-PID control strategy and the original PID controller in BSM1 and an improved performance of the suggested cascade MPC-PID controller is obtained by using the CPA approach.

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Alternative solutions to multi-variate control performance assessment problems

Cited by 42 publications

References 23 publications

Performance assessment of switched control systems based on tensor space approach

Performance assessment of switched control systems based on tensor space approach

Model-Based Control-Loop Performance of a Continuous Direct Compaction Process

Performance assessment of cascade controllers for nitrate control in a wastewater treatment process

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