Improved Renyi Entropy Benchmark for Performance Assessment of Common Cascade Control System

Zhang, Qian; Wang, Yagang; Lee, Feifei; Chen, Qiu; Sun, Zhen

doi:10.1109/access.2019.2891074

Cited by 13 publications

(11 citation statements)

References 14 publications

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“…• integral time measures, e.g., Mean Square Error (MSE), Integral Absolute Error (IAE) [86], Integral Time Absolute Value (ITAE) [87], Integral of Square Time derivative of the Control input (ISTC) [88], Total Squared Variation (TSV) [89], and Amplitude Index (AMP) [71]; • correlation measures, such as oscillation detection index [90] or relative damping index [91]; • statistical factors utilizing different probabilistic distribution function (standard deviation, variance, skewness, kurtosis, scale, shape, etc.) [92], variance band index [93], or the factors of other probabilistic distributions [94][95][96]; • benchmarking methods [97]; and • alternative indexes using wavelets [98], orthogonal Laguerre [99] and other functions [65], Hurst exponent [100], persistence measures [101,102], entropy [103][104][105], multifractal approaches [106], or fractional-order [107,108].…”

Section: Data-driven Methodsmentioning

confidence: 99%

Performance Assessment of Predictive Control—A Survey

Domański

2020

Algorithms

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Model Predictive Control constitutes an important element of any modern control system. There is growing interest in this technology. More and more advanced predictive structures have been implemented. The first applications were in chemical engineering, and now Model Predictive Control can be found in almost all kinds of applications, from the process industry to embedded control systems or for autonomous objects. Currently, each implementation of a control system requires strict financial justification. Application engineers need tools to measure and quantify the quality of the control and the potential for improvement that may be achieved by retrofitting control systems. Furthermore, a successful implementation of predictive control must conform to prior estimations not only during commissioning, but also during regular daily operations. The system must sustain the quality of control performance. The assessment of Model Predictive Control requires a suitable, often specific, methodology and comparative indicators. These demands establish the rationale of this survey. Therefore, the paper collects and summarizes control performance assessment methods specifically designed for and utilized in predictive control. These observations present the picture of the assessment technology. Further generalization leads to the formulation of a control assessment procedure to support control application engineers.

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Section: Data-driven Methodsmentioning

confidence: 99%

Performance Assessment of Predictive Control—A Survey

Domański

2020

Algorithms

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“…Applying Parseval's theorem to Eq. (23), (24) enables the variances of the process output and differenced input to be computed as [47] :  , then take the optimal input variance as the horizontal axis and the optimal output variance as the vertical axis, By drawing the performance limit curve , the variance performance indexes of the linear controller can be obtained, such as Figure. 1. And the performance indexes for assessment are defined in Figure. 1.…”

Section: ( )mentioning

confidence: 99%

“…Moreover, they developed a novel datadriven control performance assessment method under two dimensions. In 2019, an improved entropy benchmark for performance assessment of common cascade control system was proposed by Zhang et al [24] , which combined entropy with output mean value and deal with the inconsistency of the minimum variance benchmark in evaluating non-Gaussian disturbance systems. PID control or model predictive control optimization based PID are the most widely used technology in the control system.…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of FO-PID Temperature Control System Using a Fractional Order LQG Benchmark

Zou

2020

IEEE Access

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In this paper, a fractional order LQG benchmark is proposed for the control performance assessment of fractional order control systems. Similar to the conventional LQG benchmark, the fractional order LQG performance benchmark curve is determined by the numerical calculation method, which avoids the calculation of the complex interaction matrix. The fractional order process model is discretized via fractional order calculus. Meanwhile, the fractional order integral is introduced into the conventional LQG cost function. Then solving the linear quadratic Gaussian problem under the fractional order model and fractional control, the optimal input and output variances are determined for different weighting factors and the performance curves can be achieved. The comparison between fractional order LQG and the conventional LQG shows the improvement of the proposed benchmark under the same condition. The proposed benchmark can provide a more direct and superior reference standard to evaluate the performance of fractional order control system. Finally, a case study of fractional order PID(FO-PID) controller in industrial heating furnace temperature control experiment with model matching and model mismatch conditions is used to verify the effectiveness of the proposed benchmark.

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“…For disposing the inconsistency of the minimum variance criterion when evaluating non-Gaussian perturbation systems, Zhang et al [21] proposed an improved Renyi entropy benchmark for the performance evaluation of common cascade control systems in 2019. By the performance index combined entropy with the output mean, the running state of the control system was evaluated.…”

Section: Introductionmentioning

confidence: 99%

A New Control Performance Evaluation Based on LQG Benchmark for the Heating Furnace Temperature Control System

2020

Processes

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Temperature control systems are a series of processes with large time-delay and non-linear characteristics. Research shows that using fractional-order modeling and corresponding control strategies can better control these processes. At the same time, the existing studies for control performance assessment are almost committed to the integer order control systems, and the methods used in few literatures on performance assessment of fractional order systems are also one-sided. This paper applies the linear quadratic Gaussian (LQG) evaluation benchmark to the performance evaluation of fractional-order control systems for the first time, starting with the LQG evaluation benchmark considering the input and output performance. The LQG benchmark can be obtained by the analytical algorithm, which simplifies the complexity of LQG solution. Finally, taking the application of the fractional predictive function control (FO-PFC) controller in the experiment of industrial heating furnace temperature control as an example, the effectiveness of the LQG benchmark is verified.

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Improved Renyi Entropy Benchmark for Performance Assessment of Common Cascade Control System

Cited by 13 publications

References 14 publications

Performance Assessment of Predictive Control—A Survey

Performance Assessment of Predictive Control—A Survey

Performance Assessment of FO-PID Temperature Control System Using a Fractional Order LQG Benchmark

A New Control Performance Evaluation Based on LQG Benchmark for the Heating Furnace Temperature Control System

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