Intelligent Optimal Control Research Articles

Islanded Microgrid Frequency Regulations Concerning the Integration of Tidal Power Units: Real-Time Implementation

This brief proposes a new optimal intelligent controller for the secondary load frequency control (SLFC) in islanded microgrids (MGs). Particularly, this brief is applied a precise model of the tidal power generation system for the SLFC in the stand-alone MG. Moreover, in order to reduce the cost of energy storage (ES) devices in the MG, vehicle-to-grid (V2G) technology has utilized as a new idea to overcome this difficulty. Hence, a fractional type-2 fuzzy P+ID controller is applied to have a robust balance between generation and demand. Furthermore, it is quite clear that optimal tuning of the controller’s parameters can cause a significant effect on the performance of the whole of the system. Consequently, a new heuristic optimization algorithm, entitled sine–cosine, is used to find the best value of the proposed controller’s parameters. Finally, by using real-data, the performance and robustness of the suggested controller over other controller techniques have examined by the dSPACE real-time simulator.

Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm

Despite the vast benefits of integrating renewable energy sources (RES) with the utility grid, they pose stability and power quality problems when interconnected with the existing power system. This is due to the production of high voltages and current overshoots/undershoots during their injection or disconnection into/from the power system. In addition, the high harmonic distortion in the output voltage and current waveforms may also be observed due to the excessive inverter switching frequencies used for controlling distributed generator’s (DG) power output. Hence, the development of a robust and intelligent controller for the grid-connected microgrid (MG) is the need of the hour. As such, this paper aims to develop a robust and intelligent optimal power flow controller using a grasshopper optimization algorithm (GOA) to optimize the dynamic response and power quality of the grid-connected MG while sharing the desired amount of power with the grid. To validate the effectiveness of proposed GOA-based controller, its performance in achieving the desired power sharing ratio with optimal dynamic response and power quality is compared with that of its precedent particle swarm optimization (PSO)-based controller under MG injection and abrupt load change conditions. The proposed controller provides tremendous system’s dynamic response with minimum current harmonic distortion even at higher DG penetration levels.

Development and Application of Dynamic Timing Optimization Platform for Big Data Intelligent Traffic Signals

As the number of car ownership increases, road traffic flow continues to increase. At the same time, traffic pressure at intersections is increasing as well. At present, most of the traffic lights in China are fixed cycle control. This timing control algorithm obviously cannot make timely adjustments according to changes in traffic flow. In this case, a large number of transportation resources would be wasted. It is very necessary to establish a dynamic timing system for Big data intelligent traffic signals. In this research, the video recognition method was used to acquire the number of vehicles at the intersection in real time, and the obtained data was processed by the optimization algorithm to make a reasonable dynamic timing of the traffic signals. The test results show that after using the big data intelligent traffic signal dynamic timing optimization control platform, in the experimental area, the overall total delay time was reduced by 23%, and the travel time was reduced by 15%. During the off-peak period, the overall total delay time in the experimental region was reduced by 17% and travel time was reduced by 10%. The big data intelligent traffic signal dynamic timing optimization platform would improve the operational efficiency and traffic supply capacity of the existing transportation infrastructure, and could provide real convenience for citizens.

Soft-sensing modeling and intelligent optimal control strategy for distillation yield rate of atmospheric distillation oil refining process

Abstract It is a challenge to conserve energy for the large-scale petrochemical enterprises due to complex production process and energy diversification. As critical energy consumption equipment of atmospheric distillation oil refining process, the atmospheric distillation column is paid more attention to save energy. In this paper, the optimal problem of energy utilization efficiency of the atmospheric distillation column is solved by defining a new energy efficiency indicator — the distillation yield rate of unit energy consumption from the perspective of material flow and energy flow, and a soft-sensing model for this new energy efficiency indicator with respect to the multiple working conditions and intelligent optimizing control strategy are suggested for both increasing distillation yield and decreasing energy consumption in oil refining process. It is found that the energy utilization efficiency level of the atmospheric distillation column depends closely on the typical working conditions of the oil refining process, which result by changing the outlet temperature, the overhead temperature, and the bottom liquid level of the atmospheric pressure tower. The fuzzy C-means algorithm is used to classify the typical operation conditions of atmospheric distillation in oil refining process. Furthermore, the LSSVM method optimized with the improved particle swarm optimization is used to model the distillation rate of unit energy consumption. Then online optimization of oil refining process is realized by optimizing the outlet temperature, the overhead temperature with IPSO again. Simulation comparative analyses are made by empirical data to verify the effectiveness of the proposed solution.

Optimal Load-Shedding Control of a Microgrid Power System

In this paper, an intelligent optimal control method is applied to solve the load-shedding problem which has an effect of reduction in fuel cost for the operating period of a diesel generator system unit integrated into a microgrid (MG) system. The optimal control has been implemented firstly to reduce the operating cost of the diesel generator and secondly to secure the proper operation of the distribution system by keeping the power balance of the MG system within a positive range of its acceptable value with the objective to meet the maximum demand side capacity. This paper has implemented a new intelligent method to solve the load shedding and saving cost of the diesel generator fuel problems in MG power system. The application of additional renewable energy systems in an MG power system has an impact on the reduction of the fuel cost of the diesel generator and implies at the same time adjustments in the fluctuations in relative excess energy generation, which has been maintained within a range of approximately thirty percent of the normal value of supply side of the MG power system.

Intelligent Energy Management Strategy Based on Artificial Neural Fuzzy for Hybrid Vehicle

This paper proposes an intelligent energy management strategy for a hybrid hydraulic–electric vehicle in order to minimize its total energy consumption. It proposes first to model the vehicle total energy consumption and investigates the minimization of an expended energy function, formulated as the sum of electrical energy provided by the on-board batteries and consumed fuel. More precisely, it is proposed in this paper an intelligent controller that shows its capabilities of increasing the overall vehicle energy efficiency and, therefore, minimizing total energy consumption. The proposed strategy consists of an advanced supervisory controller at the highest level (third), which corresponds to a fuzzy system deciding the most appropriate operating mode of the system. In the second level, an intelligent optimal control strategy is developed based on neuro-fuzzy logic. Then, in the first level, there are local fuzzy controllers to regulate vehicle subsystems set points to reach the best operational performance. The advantage of the proposed strategy could be summarized as follows: first, it can be implemented online and second, reduces total energy consumption compared with several traditional methods. The proposed strategy validations are performed using a mix of automotive TruckMaker and MATLAB/Simulink developed software on several (standard or not) driving cycles.

Intelligent optimal control for the crawler vehicle with semi-active suspension using modified staged continuous tabu search algorithm

This paper proposes a novel intelligent optimal control strategy for crawler vehicles with semi-active suspension. The proposed control strategy aims at improving vehicle ride comfort by addressing contradictory suspension properties requirements of ride comfort and handling stability simultaneously. After establishing seven degrees of freedom dynamic model of the crawler vehicle, a comprehensive evaluation index is developed to trade off among various vehicle performances, which include ride comfort, damper thermal reliability, elastic element fatigue and handling stability. Then, using modified staged continuous tabu search (MSCTS) algorithm, the optimal control efforts of semi-active suspension, damping ratios, are determined by minimizing the cost function defined by the comprehensive evaluation index. Demonstrated by simulations with triangle convex block and random ground roughness excitations, MSCTS control strategy can successfully improve ride comfort performance and achieve the optimal comprehensive performance as well.

Optimal control for wastewater treatment process based on an adaptive multi-objective differential evolution algorithm

In this paper, for the operations of wastewater treatment processes (WWTPs), an intelligent multi-objective optimization control (IMOOC), based on an adaptive multi-objective differential evolution (AMODE) algorithm, is proposed to search for the suitable set-points to balance the treatment performance and the operational costs. In this IMOOC, the combination of an AMODE algorithm and the multi-objective critical issues helps us to fulfill all the control objectives simultaneously. To improve the optimization efficiency and achieve fast convergence, the AMODE algorithm is designed to improve the local search and the global exploration abilities: The adaptive adjustment strategies are developed to select the suitable scaling factor and crossover rate in the process of searching. Meanwhile, the multi-objective critical issues, according to the state of the processes, are given as a nonlinear multi-objective optimization problem to evaluate the operational performance of WWTPs. Therefore, once the nonlinear multi-objective optimization problem is solved at each sampling time, the most appropriate set of Pareto is selected as suitable set-points to achieve the process performance. To demonstrate the merits of our proposed method, the proposed IMOOC is applied to the Benchmark Simulation Model No. 1 of WWTPs. The results show that the proposed IMOOC effectively provides process control. The performance comparison with other algorithms also indicates that the proposed optimal strategy yields better effluent qualities and lower average operation consumption.

Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems

This paper addresses the trajectory-tracking control problem of mobile robot systems with nonholonomic constraints, in the presence of time-varying parametric uncertainties and external disturbances. This necessitates an accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence control technique to design a robust controller to meet the control objectives. The design of the intelligent controller is based on the optimal control theory, the adaptive neural network system, and the robust control technique. The trajectory-tracking problem is solved using the optimal control methodology. Since the nonholonomic wheeled mobile robot is strongly nonlinear, the neural network system is applied to approximate the nonlinear function in the optimal control law. The robust controller, for his part, is then applied to adaptively estimate an unknown upper bound of the time-varying parametric uncertainties, external disturbances and approximation error of the neural network system. The stability of the closed-loop robot system is proven using the optimal control theory and Lyapunov stability analysis. The results of the simulation studies on three typical nonholonomic mobile robots are provided to demonstrate the effectiveness of the proposed controller. In addition, a comparative study with a recent robust adaptive controller shows that our proposed intelligent controller gives better results, in the sense that the output trajectory converges to the steady state faster with smaller tracking error.

Intelligent adaptive observer-based optimal control of overhead transmission line de-icing robot manipulator

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Optimal intelligent control for a variable-speed wind turbine using general regression neural network and adaptive PSO algorithm

In this paper, a robust optimal general regression neural network sliding mode (GRNNSM) controller is designed for a variable speed wind turbine (VSWT). The main objective of the controller is to optimise the energy captured from the wind. Sliding mode control (SMC) approach emerges as an especially suitable option to deal with a VSWT. However, for large uncertain systems, the SMC produces chattering problems due to the higher needed switching gain. In order to guarantee the wind power capture optimisation without any chattering problems, this study propose to combine the SMC with the general regression neural network (GRNN) based on adaptive particle swarm optimisation (APSO) algorithm. The GRNN is used for the prediction of uncertain model part and hence enable a lower switching gain to be used for compensating only the prediction errors. The APSO algorithm with efficient global search is used to train the weights of GRNN in order to improve the network performance in terms of the speed of convergence and error level. The stability is shown by the Lyapunov theory and the control action used did not exhibit any chattering behaviour. The effectiveness of the designed method is illustrated in simulations by the comparison with traditional SMC.

Optimal intelligent control for a variable-speed wind turbine using general regression neural network and adaptive PSO algorithm

In this paper, a robust optimal general regression neural network sliding mode GRNNSM controller is designed for a variable speed wind turbine VSWT. The main objective of the controller is to optimise the energy captured from the wind. Sliding mode control SMC approach emerges as an especially suitable option to deal with a VSWT. However, for large uncertain systems, the SMC produces chattering problems due to the higher needed switching gain. In order to guarantee the wind power capture optimisation without any chattering problems, this study propose to combine the SMC with the general regression neural network GRNN based on adaptive particle swarm optimisation APSO algorithm. The GRNN is used for the prediction of uncertain model part and hence enable a lower switching gain to be used for compensating only the prediction errors. The APSO algorithm with efficient global search is used to train the weights of GRNN in order to improve the network performance in terms of the speed of convergence and error level. The stability is shown by the Lyapunov theory and the control action used did not exhibit any chattering behaviour. The effectiveness of the designed method is illustrated in simulations by the comparison with traditional SMC.

Strategy of Intelligent Energy-Saving Optimization Control and Energy Consumption Analysis on Regulator Control System of Closed Greenhouse

Adopting the integrated TOPSIS intelligent energy optimization control strategy, and compared to conventional single control strategy on energy consumption of greenhouse equipment under closed condition, this paper arrives at the best energy saving optimization control strategy with comprehensive benefits. The result shows that, integrated intelligent optimizing control was obviously more energy saving compared to those did not take optimization control. Specific results as follows: TOPSIS integration strategy with energy saving of 725.39kwh, energy-saving rate of 44.19%.This shows that the proposed integrated intelligent energy optimization control strategy and energy saving effect is remarkable.

Analysis of potential energy conservation and CO2 emissions reduction in China's non-ferrous metals industry from a technology perspective

As the world's largest producer and consumer of ten kinds of non-ferrous metals, China's non-ferrous metals industry accounts for 4.39% of total national energy consumption. It is an energy-intensive industry that faces great challenges related to energy consumption and global climate change. Applying energy-efficient technologies may be a useful strategy for the development of this industry. This paper establishes a technology system within the LEAP model to estimate energy conservation and CO2 emissions abatement potentials for China's non-ferrous metals industry in 2010–2020. Five smelting processes are considered for aluminum, copper, lead, zinc and magnesium. Three scenarios (BAU, LT and HT) are set to simulate different technological policies and calculate abatement potentials. The results indicate that energy consumption and CO2 emissions will continue to grow rapidly as the sector develops. Energy consumption under the BAU scenario will reach 88.15million tce in 2020, 61% higher than in 2010. A maximum of 7.73million tce of energy can be saved and 36.86million tons of CO2 can be reduced under the strongest technology policy scenario (HT) compared to BAU. Energy conservation in aluminum, magnesium, zinc, copper and lead smelting processes account for 72.9%, 13.2%, 10.2%, 2.6% and 1.1% respectively of the total energy savings potential, while the CO2 abatement potential mostly comes from aluminum, which accounts for 86% of the total. Targeted technology policies should be made for different metals: in the aluminum sector for instance, large scale aluminum electrolytic cells above 300kA, new cathode structures and intelligent optimization control technologies could be employed; in the zinc industry, direct leaching and long period electric-deposition could be utilized; in the magnesium industry, new types of kilns could be introduced.

Intelligent optimal setting control of a cobalt removal process

Cobalt removal process is an important step in zinc hydrometallurgy. Because of its complex reaction mechanism and dynamic characteristics, human supervision with low level control is not sufficient to keep the stable and optimal operation of cobalt removal process. This paper presents an intelligent optimal setting control strategy of cobalt removal process. The control strategy consists of process monitoring unit, zinc dust utilization factor (ZDUF) estimation unit, cobalt removal ratio (CRR) optimal setting unit, oxidation reduction potential (ORP) setting unit and case based reasoning (CBR) controller. Process monitoring unit judges the state of current process. When process is at steady state, economical optimization is conducted by allocating suitable CRR to reactors according to their ZDUF. In order to realize automatic control, CRR is transformed into the setting value of ORP through an integrated model which is also able to estimate outlet cobalt ion concentration. When a process is at an abnormal state, case based reasoning controller is triggered to handle the undesired situation by providing rational solution of control variables. An industrial experiment shows that by using the proposed control strategy, zinc dust consumption can be reduced while the required cobalt removal performance is always achieved. Stability of cobalt removal can also be improved by limiting CRR of each reactor in predefined ranges.

A MULTISWARM COMPETITIVE PARTICLE SWARM ALGORITHM FOR OPTIMIZATION CONTROL OF AN ETHYLENE CRACKING FURNACE

A fuzzy C-means (FCM) multiswarm competitive particle swarm optimization (FCMCPSO) algorithm is proposed, in which FCM clustering is used to divide swarms adaptively into different clusters. The large-scale swarms are according to the standard particle swarm optimization (PSO) algorithm, whereas the small-scale swarms search randomly in the neighborhood of the optimal solution to increase the probability of jumping out of the local optimization point. Within every cluster, the adaptive value gained by competitive learning is respectively found and arranged in order. Swarms of small adaptive value were integrated with the neighboring swarms of large adaptive value to search the optimal solution competitively by the swarms. The algorithm's validity was tested by benchmark functions and compared with other PSO algorithms. Furthermore, an integrated FCMCPSO-radial basis function neural network was studied for nonlinear system modeling and intelligent optimization control of cracking depth of an ethylene cracking furnace application in a chemical process.

Optimal operational control for complex industrial processes

Process control should ensure not only controlled variables to follow their setpoint values, but also the whole process plant to meet operational requirements optimally (e.g., quality, efficiency and consumptions). Process control should also enable that operational indices for quality and efficiency be improved continuously, while keeping the indices related to consumptions at the lowest possible level. This paper starts with a survey on the existing operational optimization and control methodologies and then presents a data-driven hybrid intelligent optimal operational control for complex industrial processes where process operational models are difficult to obtain. Applications via a hybrid simulation system and an industrial roasting process for hematite ore mineral processing are presented to demonstrate the effectiveness of the proposed operational control method. Issues for future research on the optimal operational control for complex industrial processes are outlined before concluding the paper.

Research on Intelligent Optimization Control Method for Oil Pumping

In the oil production process, some oil pumps exist in the light load running and empty pumping problems, which result in the waste of electric energy. In order to realize the oil pumping energy saving optimal control by adopting oil pumping start-stop intermittent control scheme, an intelligent energy saving optimal control system based on genetic algorithm and wavelet neural network is proposed in this paper. The Morlet wavelet is adopted as the activation function of neural network and builds the wavelet neural networkandthe structure and parameters of the wavelet neural network which are chromosome encoded, and the genetic algorithm is used to optimize the connection weights and the scale parameters in order to improve the generalization ability and the approximation ability of neural networks. The parameters of effective oil pumping energy saving optimal control are measured using multisensors. The system is used in the oil production plant; the test data show that the oil pumping energy conservation effect is obvious.

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.