Control Performance Assessment Research Articles

Control performance assessment based on sensor fusion techniques

Control performance assessment techniques are widely studied and many performance assessment indices have been proposed. In this paper, a control performance assessment technique for multi-loop control systems is presented based on the decision fusion strategy. Since decisions based on individual indices can lead to erroneous results, decision fusion of different indices can improve the assessment accuracy, especially in multi-loop control systems in the presence of loop interactions. Performance assessment indices are individually evaluated and decisions based on these indices are fused. The results of simulation and practical implementation on series cascade control structures illustrate the effectiveness of the proposed algorithm.

Slow feature analysis for monitoring and diagnosis of control performance

Recently, slow feature analysis (SFA), a novel dimensionality reduction technique, has been adopted for integrated monitoring of operating condition and process dynamics. By isolating temporal behaviors from steady-state information, the SFA-based monitoring scheme enables improved discrimination of nominal operating point changes from real faults. In this study, we demonstrate that the temporal dynamics is an additional indicator of control performance changes, and further exploit its unique efficacy in control performance monitoring. Because of its data-driven nature and ease from first-principle knowledge, the SFA-based monitoring scheme allows an overall assessment of the plant-wide control performance and is compatible with different control strategies. An attractive feature of the SFA-based approach compared to existing ones is that generic process monitoring indices are used, which renders contribution plots naturally applicable to real-time diagnosis of control performance. As a result, potential fault variables as root causes of control performance changes can be identified, including not only controlled variables (CV) but also manipulated variables (MV) and disturbance variables (DV). Simulated and experimental studies demonstrate the effectiveness of the proposed method.

Two practical performance indexes for monitoring the Rhine–Meuse Delta water network via wavelet-based probability density function

Large scale systems (LSS) have a large size with several control loops. It is always demanding that all the local controllers of the LSS work in an optimal situation to raise the efficiency of the system. Therefore the performance monitoring of the distributed systems is common to detect any roots of deficiency in the subsystems. This paper presents control performance assessment (CPA) for the Rhine–Meuse Delta water network. The Delta water system consists of many rivers and sea outlets with barriers and sluices. The water network is in the low-lying area surrounded by rivers and the North Sea, and because of its location, the system is at high risk of floods. For the monitoring of the system, two new data driven performance indexes based on wavelet-based probability density function (PDF) are designed for the system to assess the performance of the subsystems and also the whole system. Finally, a comparison with Model predictive control benchmark is made to show the capability of the new benchmark. Simulation results show the effectiveness of the suggested methods in detecting the floods.

Cascade control of effluent nitrate and ammonium in an activated sludge process

We propose a new cascade control structure using a systematic tuning rule to enhance the treatment performance of nitrogen and ammonium removal in a predenitrifying activated sludge process. The primary outer control loop has a model predictive control (MPC) controller and the secondary inner loop utilizes two proportional–integral (PI) controllers, which form a cascade MPC–PI controller. The control objective is to simultaneously control the nitrate and ammonium concentrations in the effluent, which can decrease the effects of influent disturbances existing in a wastewater treatment process while maintaining better effluent quality. The prediction error method is employed to identity an accurate process model for the MPC controller design. Moreover, the control performance assessment (CPA) technique is proposed to tune the parameters of the outer MPC controller. Three tuning scenarios with different output weights in the MPC controllers are considered and the best tuning parameter is obtained using a closed-loop potential approach in the field of CPA. The results of the plant performances with respect to the three tuning cases demonstrate the effectiveness of the proposed controller tuning method based on CPA.

Research on Jing-Jin-Tang Power Grid Coordinated Control Strategy

The disadvantage of Beijing-Tianjin-Tangshan power grid operation control mode is that the control effect is poorer and the spare capacity demand is big, this paper puts forward a new coordinated control strategy. First of all, this paper describes the relationship between the load, frequency, the output of generating unit, the tie line power and the area control error relationship of the whole power grid, according to the regional automatic generation control performance assessment criteria, concludes the characteristics of regional unit adjustment. This paper analyzes the contradiction between them, and then proposes a coordinated control strategy between the Beijing-Tangshan power grid and Tianjin power grid. The case simulation results prove the superiority and feasibility of the new coordinated control strategy. Compared with the original, the new strategy reduces the volatility of the unit, shortens the recovery time of frequency, and has a certain practical significance.

Closed-loop PID controller design and performance assessment in the presence of measurement noise

A model, obtained by recently proposed closed-loop process fast identification, is used to design and evaluate performance of parallel PID controller, with a higher-order noise filter, by applying constrained optimization and simulation with estimated band-limited white noise added to controlled variable. On a laboratory thermal plant, with noisy measurements, it is demonstrated that the proposed closed-loop analysis performed by using the model is confirmable on the plant.

A New Measure of Uncertainty and the Control Loop Performance Assessment for Output Stochastic Distribution Systems

Minimization of output uncertainty is an important control target for stochastic systems subject to bounded random inputs. Firstly, causes that prevent realization of the minimum Shannon entropy (SE) control are examined based on the analysis of the SE definition in the continuous random variable (CRV) and on this basis, a new measure of uncertainty, which is called rational entropy (RE), is proposed, and the key properties of the RE are proved. Next, results are extended to the output stochastic distribution control (SDC) systems whose output probability density functions (PDFs) are approximated by a linear B-spline basis functions model. Then, two types of minimum RE controller with the mean constraint are given and several controller performance assessment (CPA) benchmarks for output SDC systems are presented. Finally, simulations are included to discuss the feasibility and effectiveness of the proposed measure of uncertainty and performance assessment methods.

Performance assessment of cascade control loops with non-Gaussian disturbances using entropy information

Cascade control is one of the routinely used control strategies in industrial processes because it can dramatically improve the performance of single-loop control, reducing both the maximum deviation and the integral error of the disturbance response. Currently, many control performance assessment methods of cascade control loops are developed based on the assumption that all the disturbances are subject to Gaussian distribution. However, in the practical condition, several disturbance sources occur in the manipulated variable or the upstream exhibits nonlinear behaviors. In this paper, a general and effective index of the performance assessment of the cascade control system subjected to the unknown disturbance distribution is proposed. Like the minimum variance control (MVC) design, the output variances of the primary and the secondary loops are decomposed into a cascade-invariant and a cascade-dependent term, but the estimated ARMA model for the cascade control loop based on the minimum entropy, instead of the minimum mean squares error, is developed for non-Gaussian disturbances. Unlike the MVC index, an innovative control performance index is given based on the information theory and the minimum entropy criterion. The index is informative and in agreement with the expected control knowledge. To elucidate wide applicability and effectiveness of the minimum entropy cascade control index, a simulation problem and a cascade control case of an oil refinery are applied. The comparison with MVC based cascade control is also included.

Multivariate Control Performance Assessment and Control System Monitoring via Hypothesis Test on Output Covariance Matrices

Control loops widely exist in industrial processes, whose performance directly influences the efficiency, safety, and product quality of production plants. Therefore, control performance assessment (CPA) and control system monitoring (CSM) are critically important for industrial processing. In consideration of multivariate control systems, the covariance matrix of closed-loop outputs plays an important role in both CPA and CSM. Existing methods mainly focus on comparing traces or determinants of the output covariance matrices, which only utilize partial information contained in the matrices. As a result, the assessment and monitoring results may be misleading. In this paper, a multiobjective scheme is proposed for both CPA and CSM of multivariate control systems, which takes the entire covariance matrices into account by conducting a hypothesis test on the equality of the matrices. To fulfill the presupposition of such test, autoregressive moving-average (ARMA) filters are established to remove the autocorrelation contained in the closed-loop output data. The developed scheme can be divided into three aspects: CPA using a minimum variance (MV) benchmark, CPA using a user-specified benchmark, and CSM based on historical data. Case studies show that, compared with the conventional approaches, the proposed method provides more abundant information and achieves better results.

Control performance assessment for a class of nonlinear systems using second-order Volterra series models based on nonlinear generalised minimum variance control

In this paper, control performance assessment for a class of nonlinear systems modelled by autoregressive second-order Volterra series with a general linear additive disturbance is presented. The proposed approach employs the nonlinear generalised minimum variance (NGMV) controller concept. The Volterra series models provide a natural extension of a linear convolution model with the nonlinearity considered in an additive term. The polynomial operator form is used throughout this paper for the description of the system input–output model. The closed form formulation of NGMV controller for autoregressive second-order Volterra series is presented in a polynomial form then a control assessment criterion based on the NGMV control is given. Simulation results and comparison studies are used to show the effectiveness of the proposed approach for a class of nonlinear systems.

Deterministic Performance Assessment and Retuning of Industrial Controllers Based on Routine Operating Data: Applications

Performance assessment and retuning techniques for proportional-integral-derivative (PID) controllers are reviewed in this paper. In particular, we focus on techniques that consider deterministic performance and that use routine operating data (that is, set-point and load disturbance step signals). Simulation and experimental results show that the use of integrals of predefined signals can be effectively employed for the estimation of the process parameters and, therefore, for the comparison of the current controller with a selected benchmark.

Data-Driven Two-Dimensional LQG Benchmark Based Performance Assessment for Batch Processes under ILC

A novel data-driven control performance assessment (CPA) method is proposed for batch processes controlled by iterative learning control (ILC) based on two-dimensional linear quadratic Gaussian (LQG) benchmark. Previous studies on CPA for ILC are based on an assumption that the model of the controlled batch process is known, whereas this study proposes a model-free CPA method. Based on the two-dimensional system theory, the closed-loop batch process under ILC can be converted into a two-dimensional Roesser model. This study proposes a novel closed-loop two-dimensional subspace identification method for the converted parameters unknown two-dimensional Roesser model. Using the identified model, the two-dimensional LQG tradeoff performance assessment surface can be obtained. The proposed method is verified by performing some simulations.

Control Performance Assessment and Design of Optimal Control to Harvest Ocean Energy

<?Pub Dtl=""?> Different approaches exist to design the optimal control strategy to harvest ocean energy with a point absorber. However, the control paradigm changes if the efficiency of the power takeoff (PTO) is taken into account. The motivation of this paper is twofold. The first objective is to develop a framework that includes the PTO efficiency to determine the optimal control strategy. The second objective is to assess the performance of any control strategy given the PTO efficiency. The performance assessment is based on the upper bound of the deliverable electrical energy of the optimal control strategy. As an example, different PTO efficiencies were considered for a given point absorber model, and extensive simulation results show the annual electrical energy delivered to the grid by the optimal control strategy. The performance of a nonoptimal control strategy is assessed with respect to the optimal control strategy.

Minimum entropy-based performance assessment of feedback control loops subjected to non-Gaussian disturbances

Control performance assessment (CPA) is a useful tool to establish the quality of industrial feedback control loops. While many current CPA techniques are developed solely for the controlled systems under the assumption of the Gaussian disturbance, the conventional minimum variance control (MVC) approach would not be applied when the disturbance distribution is non-Gaussian. In this paper, based on the information theory and the minimum entropy criterion, a more general CPA index for the feedback control loop subjected to unknown disturbance distribution is investigated. The fundamentals of MVC are first reexamined, and then an innovative performance index is given by incorporating the entropy. The feedback control algorithm based on minimum entropy, called MEC (minimum entropy control), is derived. MEC based CPA for the controlled system requires effective and systematic identification of the associated system models based on the closed-loop data. In this work, a new methodology based on the entropy criterion instead of the mean square error criterion is presented to estimate disturbances for the purpose of evaluating the performance of the control systems. To demonstrate the effective MEC based CPA method, both numerical and industrial examples are applied and compared with the MVC based CPA method.

Evaluation of an optimal ventilation IAQ control strategy using control performance assessment and energy demand

The purpose of this study is to suggest an optimal indoor air quality (IAQ) strategy for ventilation systems based on the control performance assessment (CPA) technique, whereby the performance of a control system can be quantified by a performance index. Four control structures of the IAQ ventilation system are proposed using a combination of a proportional-integral-derivative (PID) controller which is employed to control the concentration of particulate matter (PM), and two feed-forward controllers which are used to reject the influence of the subway train schedule and the concentration of outdoor PM. The control structure with the highest control performance is selected as an optimal control structure and re-tuned by a line search algorithm. The control structure consisting of a PID controller and a feed-forward controller for rejecting the effect of the train schedule exhibited the best control performance as well as low energy demand and low healthy risk of IAQ. When compared to a conventional ventilation system, the optimized structure exhibited a 70% improvement in control performance and reduced almost healthy risk of IAQ while reducing energy demand by 36%.

Performance Assessment of LQR and PI Controllers in Pitch Attitude System at Nonlinear Flight Region Including Uncertainty and Handling Quality Evaluation

This paper presents a comparative assessment based on time response specification performance between two controllers for pitch attitude system of a high fidelity F-16 aircraft at nonlinear flight control region which satisfies the handling qualities requirements across the entire flight envelope including aerodynamic uncertainties. A Linear- Quadratic-Regulator (LQR) and a Proportional- Integrated (PI) method are proposed and compared for controlling the pitch attitude system. Simulation Results show that both the controllers are capable of controlling the time domain response of pitch attitude system successfully. According to the results, LQR method gives the better performance, such as settling time, steady state error and max overshoot, compared to PID controller.

Performance Assessment of Industrial Linear Controllers in Univariate Control Loops for Both Set Point Tracking and Load Disturbance Rejection

This paper studies the performance assessment of linear controllers in univariate feedback control loops, where the processes to be controlled can be approximated by first-order plus dead time models, and the set points are subject to ramp or step changes. The lower bound of the total variation (TV) of control signals is established. Taking the lower bound of TV and that of integrated absolute error (IAE) as benchmarks, an IAE-TV-based performance index is proposed. Four industrial linear control schemes are investigated to find out the conditions simultaneously achieving satisfactory performances in terms of input load disturbance rejection and set point tracking subject to a constraint on the robustness. A novel performance assessment method is presented to calculate the proposed performance index from measurements. Numerical and experimental examples are provided to validate the performance benchmark and the effectiveness of the proposed performance assessment method.

The Multi-Attributes Assessment Method of Control Performance Based on Data Driven

The performance of a controller is usually characterized by several attribute indexes. In this context, this paper applies multi-attribute decision-making techniques to the assessment of control performance, which takes into account both stochastic and deterministic performance indexes. Case studies demonstrate the validity of the proposed method.

制御性能評価によるセルフチューニングPID制御系( 性能を診断し,達成する制御)

制御性能評価によるセルフチューニングPID制御系( 性能を診断し,達成する制御)

Practical Strategies to Reduce Pediatric CT Radiation Dose

The objective of this article is to provide a brief review of CT scanning radiation sensitivity in children and explain CT scan parameters that affect radiation dose. We discuss key factors influencing radiation dose and study quality and how these factors can be used to optimize scan protocols with the goal of reducing pediatric CT radiation dose without compromising diagnostic quality. Finally, we provide some practical tips for reducing radiation doses to children.