Multivariable adaptive control: A survey

Adaptive control is a control methodology capable of dealing with uncertain systems to ensure desired control performance. This paper provides an overview of some fundamental theoretical aspects and technical issues of multivariable adaptive control, and a thorough presentation of various adaptive control schemes for multi-input–multi-output systems, literature reviews on adaptive control foundations and multivariable adaptive control methods, and related technical problems. It covers some basic concepts and issues such as certainty equivalence, stability, tracking, robustness, and parameter convergence. It discusses some of the most important topics of adaptive control: plant uncertainty parametrization, stable controller adaptation, and design conditions for different adaptive control schemes. The paper also presents a detailed study of well-developed multivariable model reference adaptive control theory and design techniques. It provides an introduction to multivariable adaptive pole placement and adaptive nonlinear control, and it concludes by identifying some open research problems.     

Investigation of kinetics of agglomerate growth in oil agglomeration process

In this study, the kinetics of agglomerate growth in a batch oil agglomeration process has been studied using bituminous coal. The effect of operating variables such as kerosene concentration, pulp density and speed of agitation on the agglomeration process was investigated. It has been found that the second-order kinetic equation describes the growth of agglomerates adequately. The growth of the agglomerates in the oil agglomeration process shows a self-preserving growth. Using this, a characteristic curve has been developed. For the prediction of the size distribution of the agglomerates, the d₅₀ values of the agglomerates must be known. Therefore, a model has been developed by using the kinetic equation for estimation of d₅₀ values of agglomerates for this coal. It was shown that the size distribution of the agglomerates for any levels of the process variables studied can be predicted using the equation of characteristic curve and d₅₀ values. Knowledge obtained from this study will be helpful for technological advancement of this kind of study.     

Experimental and theoretical study on H2/CO2 separation by a five-step one-column psa process

An experimental and theoretical study is performed for bulk separation of H₂/CO₂ mixture (70/30 volume %) by PSA process with zeolite 5A, a process widely used commercially in conjunction with the catalytic steam reforming of natural gas or naphtha. For the optimized adsorption conditions of PSA, the characteristics of adsorption/desorption characteristics have been studied through breakthrough and desorption experiments under various conditions. The purge-to-feed ratio is important to the H₂ product purity only at a long adsorption step time. H₂ could be concentrated from 70% in the feed to 99.99% at H₂ recovery of 67.5%. The results of all five steps in PSA are successfully predicted by the LDF model considering an energy balance and nonlinear isotherm. For the model, the effective diffusivities (D,) are obtained separately from the uptake curves of H₂ and CO₂. The Langmuir-Freundlich isotherm is used to correlate the experimental equilibrium data and is very well fitted to the results.     

Bootstrapping a robot’s kinematic model

We present a system that is able to autonomously build a 3D model of a robot’s hand, along with a kinematic model of the robot’s arm, beginning with very little information. The system starts by using exploratory motions to locate and centre the robot’s hand in the middle of its field of view, and then progressively builds the 3D and kinematic models. The system is flexible, and easy to integrate with different robots, because the model building process does not require any fiducial markers to be attached to the robot’s hand. To validate the models built by the system we perform a number of experiments. The results of the experiments demonstrate that the hand model built by the system can be tracked with a precision in the order of 1 mm, and that the kinematic model is accurate enough to reliably position the hand of the robot in camera space.     

Modeling solute diffusion in aqueous polymer solutions

There are numerous models describing solute diffusion in polymer solutions. An analysis of these models demonstrates that none can provide a consistent explanation of the phenomenon observed for solutes that can be modeled as essentially solid spheres. For this reason, a new model based on a physical obstruction approach is presented. In this model, solute movement in the presence of polymer chains is assumed to be governed by the probability of the solute molecule encountering a series of openings between the chains larger than its hydrodynamic radius. The model is tested against data taken from the literature for a series of polymer solutions and solute probes and found to provide good agreement to the effects of solute size, polymer concentration, and polymer flexibility.     

Steady-state target optimization designs for integrating real-time optimization and model predictive control

In industrial practice, the optimal steady-state operation of continuous-time processes is typically addressed by a control hierarchy involving various layers. Therein, the real-time optimization (RTO) layer computes the optimal operating point based on a nonlinear steady-state model of the plant. The optimal point is implemented by means of the model predictive control (MPC) layer, which typically uses a linear dynamical model of the plant. The MPC layer usually includes two stages: a steady-state target optimization (SSTO) followed by the MPC dynamic regulator. In this work, we consider the integration of RTO with MPC in the presence of plant-model mismatch and constraints, by focusing on the design of the SSTO problem. Three different quadratic program (QP) designs are considered: (i) the standard design that finds steady-state targets that are as close as possible to the RTO setpoints; (ii) a novel optimizing control design that tracks the active constraints and the optimal inputs for the remaining degrees of freedom; and (iii) an improved QP approximation design were the SSTO problem approximates the RTO problem. The main advantage of the strategies (ii) and (iii) is in the improved optimality of the stationary operating points reached by the SSTO-MPC control system. The performance of the different SSTO designs is illustrated in simulation for several case studies.     

L2 disturbance attenuation for highly dissipative nonlinear spatially distributed processes via HJI approach

For many practical industrial spatially distributed processes (SDPs), their dynamics are usually described by highly dissipative nonlinear partial differential equations (PDEs). In this paper, we address the L₂ disturbance attenuation problem of nonlinear SDPs using the Hamilton–Jacobi–Isaacs (HJI) approach. Firstly, by collecting an ensemble of PDE states, Karhunen–Loève decomposition (KLD) is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. Subsequently, these EEFs together with singular perturbation (SP) technique are used to obtain a finite-dimensional slow subsystem of ordinary differential equation (ODE) that accurately describes the dominant dynamics of the PDE system. Secondly, based on the slow subsystem, the L₂ disturbance attenuation problem is reformulated and a finite-dimensional H_∞ controller is synthesized in terms of the HJI equation. Moreover, the stability and L₂-gain performance of the closed-loop PDE system are analyzed. Thirdly, since the HJI equation is a nonlinear PDE that has proven to be impossible to solve analytically, we combine the method of weighted residuals (MWR) and simultaneous policy update algorithm (SPUA) to obtain its approximate solution. Finally, the simulation studies are conducted on a nonlinear diffusion-reaction process and a temperature cooling fin of high-speed aerospace vehicle, and the achieved results demonstrate the effectiveness of the developed control method.     

State space model predictive fault-tolerant control for batch processes with partial actuator failure

This paper presents a state space model predictive fault-tolerant control scheme for batch processes with unknown disturbances and partial actuator faults. To develop the model predictive fault-tolerant control, the batch process is first treated into a non-minimal representation using state space transformation. The relevant concepts of the corresponding model predictive fault-tolerant control is thus introduced through state space formulation, where improved closed-loop control performance is achieved even with unknown disturbances and actuator faults, because, unlike traditional model predictive fault-tolerant control, the proposed control method can directly regulate the process output/input changes in the design. For performance comparison, a traditional model predictive fault-tolerant control is also designed. Application to injection velocity control shows that the proposed scheme achieve the design objective well with performance improvement.     

Simulation and analysis of extractive distillation process in a valve tray column using the rate based model

Valve trays are becoming popular in the chemical process industries owing to their flexibility to handle a wide range of vapor throughputs. Using the rigorous rate based model, the importance of the non-equilibrium approach is demonstrated for a typical extractive distillation process in a Glitsch V-1 valve tray column. Simulation results based on an in-house developed code indicated that the rate based model predictions for a valve tray column operation showed significant differences relative to the equilibrium model. Even small errors in product purities translated into nonoptimal feed stage locations and inaccurate number of stages required. The counter-intuitive effect of high reflux ratio on separation is explained.     

基于海量搜索历史数据的用户兴趣模型

针对互联网搜索引擎环境中,基于海量搜索历史数据分析用户兴趣的问题,提出一种改进的用户兴趣模型。该模型根据用户搜索的历史数据,结合向量空间模型（ VSM ）和TF-IDF算法,递归地回溯出用户兴趣权重列表。为解决用户兴趣变化和时间性能的问题,该模型引入时间遗忘机制进行动态更新,并在Hadoop分布式系统架构下利用MapReduce分布式编程模型进行实现。实验结果表明,改进的用户兴趣模型的查准率和召回率都能达到50',具有较好的可行性和可用性。