Energy intense nature of cement kiln demands optimal operation to minimize the energy requirement. Optimal control of cement kiln is achieved by proper tuning of the model predictive controller (MPC), which is addressed in this work. Genetic algorithm (GA) is used to determine the MPC weights that minimize the overall energy utilization with reduced tracking error. Single objective function has been formulated using importance weighted performance metrics like energy utilization and integral absolute error in tracking the desired response. Importance weights are determined in specific to the control scenarios using an interactive decision tree (IDT). It interacts with the operator to detect the weaker metrics and raises the importance level for further improvement. The algorithm terminates after attending all the metrics with the consent from the operator. Five control scenarios that predominantly occur in industrial cement kiln have been considered in this study. It includes tracking, measured, and unmeasured disturbance rejection of pulse and Gaussian type noises. The results illustrate the minimized energy operation with the use of the proposed single objective function as compared with the multi-objective function-based GA tuning procedure.

Optimal Tuning of Model Predictive Controller Weights Using Genetic Algorithm with Interactive Decision Tree for Industrial Cement Kiln Process

Due to the need to control simultaneously torque and flux in a permanent magnet synchronous machine (PMSM), their predictive control is a multiobjective optimization problem (MOOP). To solve this problem, traditional predictive control uses the weighting factors to convert the MOOP into a single-object optimization problem. Based on the lexicographic method, a simple strategy without weighting factors called tolerant sequential model predictive control (TSMPC) is proposed. In the proposed method, the cost functions of torque and flux are, respectively, placed in two layers of the sequential structure. The voltage vectors satisfying the torque tolerance are selected as the candidates for the next layer. Then, the candidate that can minimize the flux cost function will be executed by the inverter. The advantages of the proposed TSMPC method are that it can avoid cumbersome weighting factor adjustment and directly set the tolerance value based on performance requirements. The feasibility and stability of the TSMPC strategy are validated by theoretical analysis, and the experimental results show that it has good performance.

Tolerant Sequential Model Predictive Direct Torque Control of Permanent Magnet Synchronous Machine Drives

This research paper presents an offline and online user defined priority driven multi-objective optimal control study of a bioprocess in a fed-batch reactor. Productivity and the amount of substrates used in the process are considered as the two control objectives in that order of priority for this purpose. The priorities in the objective functions are realized using the lexicographic approach by sequentially solving multiple objectives to arrive at a Pareto solution point. This approach is not sensitive to the tuning of weighting parameters as compared to the scalarized objective, practiced conventionally. The weighting factors tuning issue is demonstrated with an offline optimal control. The lexicographic optimization approach is then implemented to overcome this thing issue. Subsequently, the online optimal control problem is solved using economic model predictive control (EMPC) owing to the economic nature of the control objectives. Often, the Pareto curve is such that marginally relaxing one objective results into a significant improvement in the other objective. This can easily be implemented with the lexicographic approach and is demonstrated using EMPC. Moreover, unlike the continuous processes, the batch processes operate for a specific batch time. Hence, the shrinking horizon approach along with the EMPC framework is employed in the fed-batch bioreactor for online control with extended Kalman filter (EKF).

Multi-criterion control of a bioprocess in fed-batch reactor using EKF based economic model predictive control

Many control problems in process systems feature multi-objective optimization problems that involve several and often conflicting objective functions, such as economic profit and environmental concerns. In this paper, we consider a class of multi-objective model predictive control (MO-MPC) problems where nonlinear systems are subject to state and control constraints and multiple economic criteria are conflicting. Using the lexicographic optimization, we propose a prioritized MO-MPC scheme with guaranteed stability for economic optimization. At each sampling time, the MPC action is computed by solving a set of sequentially ordered single objective optimized control problems. Some sufficient conditions are established to ensure recursive feasibility and asymptotic stability of the MO-MPC in the context of economic criteria optimization. Two examples of multi-objective control of a coupled-tank system and a free-radical polymerization process are exploited to illustrate the effectiveness of the proposed MPC scheme and to evaluate the performance by some comparison experiments.

Lexicographic MPC with multiple economic criteria for constrained nonlinear systems

Lexicographic optimization‐based clustering search metaheuristic for the multiobjective flexible job shop scheduling problem

Lexicographic optimization based MPC: Simulation and experimental study

Classical Proportional Integral Derivative(PID) controller
remains the most popular approach for industrial process
control. Poor tuning of PID controller can lead to mechanical
wear associated with excessive control activity, poor control
performance and even poor quality products. In this paper, we
design procedure for the internal model control(IMC) approach
for tuning of conventional PID controller with proper tuning
rules. Furthermore, with help of analytical rule of step test
obtaining the effective first order time delay model of the process.
A simulation example of continuous stirred tank reactor is used
in which the IMC based PID tuning method implemented and
the step response of the closed loop system is compared with
classical tuning methods like Ziegler-Nichols and Cohen-Coon.

Performance analysis of IMC based PID controllertuning on approximated process model

The optimization problem associated with model predictive control (MPC) is often multi-objective in nature. Conventionally, these objectives are augmented with proper weighting parameters and solved using a single objective optimization. The weighting parameters in the augmented objective function represent their priorities. However, selection of these weighting parameters requires proper tuning for required performance. Hence, an explicit multi-objective optimization approach in MPC is quite valuable. Furthermore, solving multi-criterion control problem poses additional challenges for batch or semi-batch processes because of their inherent non-steady state operation. We present a case study of semi-batch process for its multi-criterion MPC with user defined priority in this work. The results are further compared with single augmented objective approach.

Multi-objective prioritized control of a semi-batch process with multiple feed and multiple products using economic MPC

Modern day power system is highly inter-connected and is expected to operate under the constraints of maintaining reliability, stability, security and economic operation. The Flexible AC Transmission System (FACTS) controllers provide an aid in operating under stability margins as well as provide flexibility to the power transmission system. STATCOM is one of the most widely-used FACTS controller. With these developments, there is a need to include the FACTS controllers into the process of power system state estimation; typically the dynamic state estimation for efficient and reliable power system operation. This paper initially discusses the dynamic mathematical modeling of STATCOM connected to a Single Machine Infinite Bus system (SMIB). Dynamic state estimation is performed using the Extended Kalman Filter (EKF). Simulation results show effectiveness of EKF in estimating all states of the considered electrical power system process under noisy measurements and plant-model mismatch.

EKF based dynamic state estimation of SMIB system integrated with STATCOM

This paper analyses a discrete-time (s, S) queueing inventory model with service time and back-order in inventory. The arrival of customers is assumed to be the Bernoulli process. Service time follows a geometric distribution. As soon as the inventory level reaches a pre-assigned level due to demands, an order for replenishment is placed. Replenishment time also follows a geometric distribution. When the inventory level reduces to zero due to the service of customers or non-replenishment of items, a maximum of k customers are allowed in the system and the remaining customers are assumed to be completely lost till the replenishment. Matrix-Analytic Method (MAM) is used to analyze the model. Stability conditions, various performance measures of the system, waiting-time distribution and reorder-time distribution are obtained. Numerical experiments are also incorporated.

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Markana Anilkumar

Papers

Lexicographic optimization based MPC: Simulation and experimental study

Performance analysis of IMC based PID controllertuning on approximated process model

Multi-objective prioritized control of a semi-batch process with multiple feed and multiple products using economic MPC

EKF based dynamic state estimation of SMIB system integrated with STATCOM

Analysis is a discrete time queueing-inventory model with back-order of items