Preference adjustable multi-objective NMPC: An unreachable prioritized point tracking method

This paper proposes a new preference adjustable multi-objective model predictive control (PA-MOMPC) law for constrained nonlinear systems. With this control law, a reasonable prioritized optimal solution can be directly derived without constructing the Pareto front by solving a minimal optimization problem, which is a novel development of recently proposed utopia tracking approaches by additionally considering objective preferences with more flexible terminal and stability constraints. The tracking point of the proposed PA-MOMPC law is represented by a parametric vector with the parameters adjustable on the basis of objective preferences. The main result of this paper is that the solution obtained through the proposed PA-MOMPC law is demonstrated to have two important properties. One is the inherent Pareto optimality, and the other is the priority consistency between the solution and the tuning parametric vector. This combination makes the objective priorities tuning process transparent and efficient. The proposed PA-MOMPC law is supported by feasibility analyses, proof of nominal stability, and a numerical case study.     

An experimental assessment of finite-state Predictive Torque Control for electrical drives by considering different online-optimization methods

Finite-State Predictive Torque Control (FS-PTC) is experimentally investigated based on different online-optimization methods by using a two-level voltage source inverter for an induction machine. The calculation time and the switching frequencies are important research points for FS-PTC industrial applications. Long-step FS-PTC methods are expected to improve the performance of the system. However, the calculation time will increase exponentially with the increase of the prediction horizon. A reduced switching frequency PTC (RSF-PTC) method by considering the reductions of the switching frequency and the calculation time is tested. Based on this algorithm, an extended prediction horizon is proposed and verified on a common test bench. A torque-band based PTC (TB-PTC) method is proposed and discussed in this paper. The TB-PTC method pre-calculates the torque error between the predicted torque and the torque reference. The optimization method focuses on the flux error and the switching frequency for switching states which constrain the torque error within the torque-band. The conventional FS-PTC method, the RSF-PTC method with one-step and two-step horizons and the TB-PTC method are developed and experimentally compared in this work. The results confirm that conventional FS-PTC, RSF-PTC and TB-PTC methods can work well in the full speed range. When the switching frequencies and the calculation effort are taken into consideration, the RSF-PTC algorithm shows the better performance. However, the conventional FS-PTC method and the TB-PTC method have better current performance.     

Actuator Fault Tolerant Control Based on a MIMO-MPC: Application in a Double-Pipe Heat Exchanger

In this work, an experimental fault tolerant control (FTC) implementation is presented. The FTC is based on a multi-input multi-output (MIMO) model predictive control (MPC). The aim of the FTC is to keep on operating a double-pipe counter-current heat exchanger even if the main actuator of the heat exchanger is stuck open. To develop the FTC, an adaptive observer was implemented in order to design a fault detection and isolation (FDI) system. In the FDI system, the cold and hot water flow rate estimations by the adaptive observer are compared to the control signals provided by the MPC. The results of the implementation of the FTC using a MIMO model predictive control were compared to the results obtained in a previous work which was developed using model-following control.     

Improved fuzzy PID controller design using predictive functional control structure

In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control.     

Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms

In pharmaceutical development, it is very useful to exploit the knowledge of the causal relationship between product quality and critical material attributes (CMA) in developing new formulations and products, and optimizing manufacturing processes. With the big data captured in the pharmaceutical industry, computational intelligence (CI) models could potentially be used to identify critical quality attributes (CQA), CMA and critical process parameters (CPP). The objective of this study was to develop computational intelligence models for pharmaceutical tabletting processes, for which bio-inspired feature selection algorithms were developed and implemented for optimisation while artificial neural network (ANN) was employed to predict the tablet characteristics such as porosity and tensile strength. Various pharmaceutical excipients (MCC PH 101, MCC PH 102, MCC DG, Mannitol Pearlitol 200SD, Lactose, and binary mixtures) were considered. Granules were also produced with dry granulation using roll compaction. The feed powders and granules were then compressed at various compression pressures to produce tablets with different porosities, and the corresponding tensile strengths were measured. For the CI modelling, the efficiency of seven bio-inspired optimization algorithms were explored: grey wolf optimization (GWO), bat optimization (BAT), cuckoo search (CS), flower pollination algorithm (FPA), genetic algorithm (GA), particle swarm optimization (PSO), and social spider optimization (SSO). Two-thirds of the experimental dataset was randomly chosen as the training set, and the remaining was used to validate the model prediction. The model efficiency was evaluated in terms of the average reduction (representing the fraction of selected input variables) and the mean square error (MSE). It was found that the CI models can well predict the tablet characteristics (i.e. porosity and tensile strength). It was also shown that the GWO algorithm was the most accurate in predicting porosity. While the most accurate prediction for the tensile strength was achieved using the SSO algorithm. In terms of the average reduction, the GA algorithm resulted in the highest reduction of inputs (i.e. 60%) for predicting both the porosity and the tensile strength.     

Exploiting the channel using uninterrupted collision‐free MAC adaptation over IEEE 802.11 WLANs

IEEE 802.11 wireless local area networks (WLANs) have reached an important stage and become a common technology for wireless access due to its low cost, ease of deployment, and mobility support. In parallel with the extensive growth of WLANs, the development of an efficient medium access control protocol that provides both high throughput performance for data traffic and quality of service support for real‐time applications has become a major focus in WLAN research. The IEEE 802.11 Distributed Coordination Functions (DCF/EDCA) provide contention‐based distributed channel access mechanisms for stations to share the wireless medium. However, performance of these mechanisms may drop dramatically because of high collision probabilities as the number of active stations increases. In this paper, we propose an adaptive collision‐free MAC adaptation. The proposed scheme prevents collisions and allows stations to enter the collision‐free state regardless of the traffic load (saturated or unsaturated) and the number of stations on the medium. Simulation results show that the proposed scheme dramatically enhances the overall throughput and supports quality of service for real‐time services over 802.11‐based WLANs. Copyright © 2013 John Wiley & Sons, Ltd.     

Asphaltene precipitation and deposition in oil reservoirs – Technical aspects,experimental and hybrid neural network predictive tools

Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO₂ injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation.     

二次调节扭矩加载系统解耦控制的研究

针对静液驱动二次调节双通道扭矩加载系统中两通道间转矩耦合的具体情况,首先将其分解为两个单变量控制系统,并采用了动态矩阵解耦控制算法。这种方式只需采用二次元件的转矩信号;同时,控制算法结构简单,对于模型要求低,可调参数少,计算量少,易于在线实施。试验结果表明,该方法具有较好的解耦效果。     

带有自校正的PID预测计算机控制算法研究

常规PID控制有许多不完善之处,其中最主要的问题就是PID控制器参数一旦调整好后。在整个控制过程中都是固定不变的,从而使系统很难达到最佳的控制效果。提出了一种带有自校正的PID预测计算机控制算法,包括控制结构、控制器设计以及变换为具有PID结构形式的过程。仿真结果表明带有白校正的PID预测控制算法调整时间短,平滑性较好,表明该带有自校正的PID预测计算机控制算法的有效性和较好的控制综合性能。     

Integration of scheduling and control for batch processes using multi‐parametric model predictive control

Integration of scheduling and control results in Mixed Integer Nonlinear Programming (MINLP) which is computationally expensive. The online implementation of integrated scheduling and control requires repetitively solving the resulting MINLP at each time interval. (Zhuge and Ierapetritou, Ind Eng Chem Res. 2012;51:8550–8565) To address the online computation burden, we incorporare multi‐parametric Model Predictive Control (mp‐MPC) in the integration of scheduling and control. The proposed methodology involves the development of an integrated model using continuous‐time event‐point formulation for the scheduling level and the derived constraints from explicit MPC for the control level. Results of case studies of batch processes prove that the proposed approach guarantees efficient computation and thus facilitates the online implementation. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3169–3183, 2014