Robust attitude tracking control for a rigid spacecraft under input delays and actuator errors

Attitude control of a rigid spacecraft under input delays, disturbances, parameter uncertainties, actuator errors, and constraints is a challenging problem. In this paper, these problems are considered simultaneously, and a robust control approach to attitude tracking of a rigid spacecraft is exploited. The design methodology is based on three steps: (1) compensating input delays by using the backstepping technique, (2) design of a disturbance observer for the delayed system by using the super-twisting algorithm to estimate unknown internal and external disturbances, then adding a feedforward compensation law based on the estimated signal to the backstepping controller to attenuate the effects of disturbances, (3) employing a robust least-square scheme to map the specified control command on the redundant actuators in the presence of actuator error, including actuator magnitude deviation and misalignment, with regard to actuator amplitude and rate constraints. The effectiveness of the proposed algorithm is shown by various numerical simulations.     

Predicting discharge coefficient of triangular labyrinth weir using extreme learning machine,artificial neural network and genetic programming

Weirs are a type of hydraulic structure used to direct and transfer water flows in the canals and overflows in the dams. The important index in computing flow discharge over the weir is discharge coefficient (C _d). The aim of this study is accurate determination of the C _d in triangular labyrinth side weirs by applying three intelligence models [i.e., artificial neural network (ANN), genetic programming (GP) and extreme learning machine (ELM)]. The calculated discharge coefficients were then compared with some experimental results. In order to examine the accuracy of C _d predictions by ANN, GP and ELM methods, five statistical indices including coefficient of determination (R ²), root-mean-square error (RMSE), mean absolute percentage error (MAPE), SI and δ have been used. Results showed that R ² values in the ELM, ANN and GP methods were 0.993, 0.886 and 0.884, respectively, at training stage and 0.971, 0.965 and 0.963, respectively, at test stage. The ELM method, having MAPE, RMSE, SI and δ values of 0.81, 0.0059, 0.0082 and 0.81, respectively, at the training stage and 0.89, 0.0063, 0.0089 and 0.88, respectively, at the test stage, was superior to ANN and GP methods. The ANN model ranked next to the ELM model.

     

Corrugated all-composite sandwich structures. Part 1: Modeling

An analytical model for the compressive and shear response of monolithic and hierarchical corrugated composite cores has been developed. The stiffness model considers the contribution in stiffness from the bending- and the shear deformations of the core members in addition to the stretching deformation. The strength model is based on the normal stress and shear stress distribution over each core member when subjected to a shear or compressive load condition. The strength model also accounts for initial imperfections. In part 1 of this series, the analytical model is described and the results are compared to finite element predictions. In part 2, the analytical model is compared to experimental results and the behaviour of the corrugated structures is investigated more thoroughly using failure mechanism maps.     

Extended kalman filter‐based nonlinear model predictive control of a continuous KCl‐NaCl crystallizer

An extended Kalman filter (EKF)‐based nonlinear quadratic dynamic matrix control (EQDMC) for an evaporative cooling draft‐tube baffled (DTB) KCl crystallizer is developed. The controller is used to maintain the productivity, crystal mean size and impurity of crystals. Since these controlled variables are not directly measurable, the EKF is used to estimate them. The nonlinear controller is a combination of an extended linear dynamic matrix control (EDMC) and the quadratic dynamic matrix control (QDMC). This combination provided good control of the system despite the process nonlinearity, constraints, and inadequate reliable online measurement of the controlled variables. The performance of the controller in the presence of plant/model mismatch, disturbance, wrong estimation and simultaneous step changes in the controller setpoints is discussed.     

MODELING AND NONLINEAR CONTROL OF A CONTINUOUS KCI-NaCI CRYSTALLIZER

A controller is developed by combining the extended linear quadratic matrix control (EQDMC) and neural network algorithms. The dynamic neural network scheme is used to identify the process and generate a nonlinear model. The control algorithm is applied to a multi-input multi-output (MIMO) evaporative cooling KCl-NaCl-H₂O crystallizer. Closed loop responses of the system using the proposed algorithm and those of PID controllers are compared. It is shown that in all cases, the response of the proposed controller to step changes in setpoints is faster than the PID controllers.     

Applications of the crystallization process in the pharmaceutical industry

The applications of the crystallization technique in the pharmaceutical industry as a purification and separation process for the isolation and synthesis of pure active pharmaceutical ingredients (API), co-crystals, controlled release pulmonary drug delivery, and separation of chiral isomers are briefly discussed using a few case studies. The effect of process variables and solvent on the polymorphism and morphology of stavudine is discussed. The implementation of external control in the form of feedback and real-time optimal control using cooling and antisolvent crystallization of paracetamol in water-isopropyl alcohol is introduced. Two methods to prepare micron-sized drug particles, namely, micro-crystallization and polymer-coated API-loaded magnetic nanoparticles for pulmonary drug delivery, are discussed. The significance of co-crystals in drug administration is highlighted using the theophylline-nicotinamide co-crystal system. Resolution of chloromandelic acid derivatives, a racemic compound, is achieved using direct crystallization and diastereomeric salts crystallization. The crystal structures of diastereomeric salts of chloromandelic acid and phenylethylamine are determined. The structure comparison between the less soluble and more soluble salts shows that weak interactions such as CH/π interactions and van der Waals forces contribute to chiral recognition when the hydrogen bonding patterns are similar.     

A mixing model for diagnosing reacting tracers and tracing reactants in chemical and biochemical processes

It has previously been shown (Tanner et al., 1985) that biochemical and chemical reaction processes of the Type A → B → C can lead not only to kinetic hysteresis between the rate of formation of C and the concentration of species B in batch processes, but also hysteresis between those variables in a closed, imperfectly mixed (two zone) batch reactor. Furthermore, crossplotting the intermediate reactant, B, in one region of the poorly mixed reactor against B from the other major region leads to a clockwise hysteresis curve which is defined by both its area and the coordinate phase angle. This paper shows that the earlier analysis (Tanner et al., 1985) can be extended to the more general system A → B, where A in one region of the vessel is crossplotted against A measured in the other region. With an initial concentration of A* in one zone equal to its highest concentration, the inscribed area double-valued crossplot of A, uniquely defines the system in terms of the inter-vessel flowrate to reaction rate constant ratio, D/k.     

Directionally sensitive MCUSUM mean charts

In the context of a disease outbreak detection, a prime interest is to only detect increases in the process mean. It is thus desirable to have a directionally sensitive multivariate chart that can effectively detect either increases or decreases in the process mean vector. In this paper, with a suitable transformation that truncates multivariate observations either above or below the process mean vector, we propose one-sided and two one-sided MCUSUM charts for monitoring the mean of a multivariate normal process. Among the proposed charts, the one-sided MCUSUM charts are directionally sensitive, while the two one-sided MCUSUM charts are directionally insensitive. In addition, the fast initial response feature is also incorporated into the proposed charts to enhance their sensitivities against initial process shifts. The run length characteristics of these control charts are computed with the Monte Carlo simulation. Based on the run length comparisons, it is found that the proposed charts are more sensitive than the existing charts when detecting moderate-to-large shifts in the process mean. The proposed charts are also applied on real datasets to support the theory.