Intelligent Optimal Control System Research Articles

The digitization work of cement plant in China

This article is to introduce the digitization work of smart cement plant in China. To save power and promote the product quality, delicacy management in variety ways of digital transformation is the best tool for the new built, or existing cement plants. New technologies, such as special integrated robots, online equipment, information plant form, cement process digital applicant (SAAS), Artificial Intelligence (AI) and Advanced Process Control (APC) are implemented to approve the traditional ways of operation.This paper introduces the application of intelligent factory technology in China, and focuses on the intelligent optimization control system. The system has self-learning and global optimization functions, with strong adaptability and robustness. In combination with quality prediction, kiln condition identification and quantitative identification of fire watching TV video images, the kiln system is feed-forward controlled to solve the control problem of long time delay, large inertia and strong coupling of the burning system. The system gives the global optimal recommended value of the control target through calculation, with which to ensure the best control and operation of the system.With the application of comprehensive intelligent construction, the cement plant will usually save 2–5 % of energy and 20 % staff simplification, and make the management mode more concise and efficient. Meanwhile, the cement groups are also developing the production information plat form, by merging the Enterprise Resource Planning (ERP) data, sales business to lower down the total cost. This paper tries to introduce the digital efforts that are carried out in the smart cement plants and shows the methodology of implementation.

Intelligent Optimal Control System With Flexible Objective Functions and Its Applications in Wastewater Treatment Process

Optimal control system has been considered as a valuable technique to enhance the reliability and security of wastewater treatment process (WWTP). However, due to the complex environments, it is difficult to realize the optimal control with changeable operational conditions in WWTP. To overcome this problem, an intelligent optimal control system with flexible objective functions (IOCS-FOF) was proposed in this paper. The main merits of this proposed IOCS-FOF were shown as follows. First, an optimal control system scheme was developed, where different time-scale operational objectives were detailed to describe the operational features. Second, a flexible objective strategy was designed to determine suitable objective functions, which could adapt to complex environments of WWTP. Third, a novel dynamic multiobjective optimization algorithm for flexible objectives (FO-DMOA) was developed to obtain the optimal solutions of IOCS-FOF. Finally, the proposed IOCS-FOF was applied in the benchmark simulation model No. 1 (BSM1) to confirm its technique capability. The results demonstrate that this proposed method can improve the operation performance of WWTP.

An intelligent optimization control method for the electrode current of submerged arc furnaces based on case reasoning

Due to the nonlinearity, strong coupling, and time-varying characteristics of three-phase electrode lift system of submerged arc furnace, the existing manual operation has the problems of electrode control hysteresis, poor balance of three-phase electrode current, and blindness of electrode current target setting. An intelligent optimization control method for the electrode current of submerged arc furnace based on case reasoning is proposed in this article, which is used to realize the automatic control of the electrode control system of the submerged arc furnace. First, the optimization model of electrode current setting value of the submerged arc furnace is established by the case-based reasoning method, and the corresponding electrode current value is calculated to maximize the yield in the safe power range of the furnace. Next, a three-phase electrode current decoupling controller is designed based on fuzzy rules. Finally, an intelligent optimization control system of three-phase electrode current of submerged arc furnace is designed and its superiority is verified by comparison with the proportional–integral–derivative controller. The designed control system has been applied to the smelting production of submerged arc furnace in a domestic smelter. The simulation and industrial operation results show that the system realizes automatic balance adjustment of electrode current of submerged arc furnace under normal working conditions, which greatly reduces the labor intensity of the operator, increases the smelting yield, reduces the unit energy consumption, and brings significant economic and social benefits to the enterprise.

Research on Intelligent Control System of A/A/O Process for Wastewater Treatment Pilot Based on ASM2D and Fuzzy Model

The paper focuses on the design and implementation of intelligent control system for actual wastewater treatment. The objective of this project is to develop an intelligent optimization control system for biological process based on on-line sensing parameters. The control logic of system based on the simplified ASM2d model and fuzzy logic. The system integrates data input and output modules, simulates the biological reaction, and optimizes the control parameters of biological treatment process, such as controlling aeration, dosing and internal reflux ratio. Real-time intelligent optimization control system optimizes the operation effect of wastewater treatment, ensures the water quality to reach the standard and achieves the goal of energy saving and consumption reduction.

Intelligent Optimal Recurrent Wavelet Elman Neural Network Control System for Permanent-Magnet Synchronous Motor Servo Drive

In this paper, an intelligent control system using recurrent wavelet-based Elman neural network (RWENN) for position control of permanent-magnet synchronous motor (PMSM) servo drive is proposed to achieve high precision tracking performance and to deal with the existence of uncertainties. The proposed intelligent optimal RWENN control system (IORWENNCS) incorporating an optimal controller, a RWENN controller and a robust controller. Based on the principle of optimal control, a position tracking controller is designed to minimize a quadratic performance index. In addition, a RWENN controller with accurate approximation capability is used to approximate a nonlinear function in the optimal control law. Moreover, a robust controller with adaptive bound estimation algorithm is proposed to confront the approximation error. The online adaptive control laws are derived based on the optimal control technique and Lyapunov stability analysis, so that the stability of the IORWENNCS can be guaranteed. Using the proposed control scheme, the position tracking performance is substantially improved and the robustness to uncertainties can be obtained as well. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the proposed IORWENNCS grants robust performance and precise tracking response regardless of load disturbances and PMSM uncertainties.

Intelligent optimal control system for ball mill grinding process

Operation aim of ball mill grinding process is to control grinding particle size and circulation load to ball mill into their objective limits respectively, while guaranteeing producing safely and stably. The grinding process is essentially a multi-input multi-output system (MIMO) with large inertia, strong coupling and uncertainty characteristics. Furthermore, being unable to monitor the particle size online in most of concentrator plants, it is difficult to realize the optimal control by adopting traditional control methods based on mathematical models. In this paper, an intelligent optimal control method with two-layer hierarchical construction is presented. Based on fuzzy and rule-based reasoning (RBR) algorithms, the intelligent optimal setting layer generates the loops setpoints of the basic control layer, and the latter can track their setpoints with decentralized PID algorithms. With the distributed control system (DCS) platform, the proposed control method has been built and implemented in a concentration plant in Gansu province, China. The industrial application indicates the validation and effectiveness of the proposed method.

Design and Development of an Embedded Intelligent Optimal Control Platform

An embedded intelligent optimal control platform is designed and developed in this paper. On the hardware side, the embedded industrial controller uses ARM9 as the core processor to meet the requirements of low-power consumption, low-cost and high computation performance. On the software side, Xenomai Linux OS and ProConOS are adopted to realise hard real-time control performance. Besides, MultiProg is used to support block programming of the control software. To validate the effectiveness of embedded control platform, the grinding circuit mineral processing plants is used as an example to design an intelligent optimal control system and validates its performance through Hardware-in-the-Loop experiment. The experiment results show that this platform can fully satisfy the requirements of industrial optimal control.

Perspectives of Computational Intelligence in Robotics and Automation

Perspectives of Computational Intelligence in Robotics and Automation

Intelligent Optimal Control of Single-Link Flexible Robot Arm

This paper addresses the design and properties of an intelligent optimal control for a nonlinear flexible robot arm that is driven by a permanent-magnet synchronous servo motor. First, the dynamic model of a flexible robot arm system with a tip mass is introduced. When the tip mass of the flexible robot arm is a rigid body, not only bending vibration but also torsional vibration are occurred. In this paper, the vibration states of the nonlinear system are assumed to he unmeasurable, i.e., only the actuator position can be acquired to feed into a suitable control system for stabilizing the vibration states indirectly. Then, an intelligent optimal control system is proposed to control the motor-mechanism coupling system for periodic motion. In the intelligent optimal control system a fuzzy neural network controller is used to learn a nonlinear function in the optimal control law, and a robust controller is designed to compensate the approximation error. Moreover, a simple adaptive algorithm is proposed to adjust the uncertain bound in the robust controller avoiding the chattering phenomena. The control laws of the intelligent optimal control system are derived in the sense of optimal control technique and Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. In addition, numerical simulation and experimental results are given to verify the effectiveness of the proposed control scheme.

Intelligent optimal control with dynamic neural networks

The application of neural networks technology to dynamic system control has been constrained by the non-dynamic nature of popular network architectures. Many of difficulties are-large network sizes (i.e. curse of dimensionality), long training times, etc. These problems can be overcome with dynamic neural networks (DNN). In this study, intelligent optimal control problem is considered as a nonlinear optimization with dynamic equality constraints, and DNN as a control trajectory priming system. The resulting algorithm operates as an auto-trainer for DNN (a self-learning structure) and generates optimal feed-forward control trajectories in a significantly smaller number of iterations. In this way, optimal control trajectories are encapsulated and generalized by DNN. The time varying optimal feedback gains are also generated along the trajectory as byproducts. Speeding up trajectory calculations opens up avenues for real-time intelligent optimal control with virtual global feedback. We used direct-descent-curvature algorithm with some modifications (we called modified-descend-controller-MDC algorithm) for the optimal control computations. The algorithm has generated numerically very robust solutions with respect to conjugate points. The adjoint theory has been used in the training of DNN which is considered as a quasi-linear dynamic system. The updating of weights (identification of parameters) are based on Broyden–Fletcher–Goldfarb–Shanno BFGS method. Simulation results are given for an intelligent optimal control system controlling a difficult nonlinear second-order system using fully connected three-neuron DNN.

Development of intelligent position control system using optimal design technique

This paper addresses the application of an intelligent optimal control system (IOCS) to control an indirect field-oriented induction servo motor drive for tracking periodic commands via a wavelet neural network. With the field orientation mechanism, the dynamic behavior of an induction motor is rather similar to a linear system. However, the uncertainties, such as mechanical parametric variation, external load disturbance and unmodeled dynamics in practical applications, influence the designed control performance seriously. Therefore, an IOCS is proposed to confront these uncertainties existing in the control of the induction servo motor drive. The control laws for the IOCS are derived in the sense of the optimal control technique and Lyapunov stability theorem, so that system-tracking stability can be guaranteed in the closed-loop system. With the proposed IOCS, the controlled induction servo motor drive possesses the advantages of good tracking control performance and robustness to uncertainties under wide operating ranges. The effectiveness of the proposed control scheme is verified by both simulated and experimental results. Moreover, the advantages of the proposed control system are indicated in comparison with the sliding-mode control system.