This paper proposes a precise structure solution for the ratio control of nonlinear processes blend stations. The main feature of the proposed structure is to give a good ratio performance for both set-point changes and load disturbances that might occur in any one of the loops. For each loops (two or more) a nonlinear structure based on a multiple model and nonlinearity compensator is proposed. The main advantages consist in a classic tuning procedure for each of the controllers and a very simple procedure for nonlinearity determination, suitable for industrial applications. All components of control structure ware implemented on real time control application.

 In the past fifty years, several attempts have been made to model the characteristics of Pneumatic Artificial Muscles (PAMs). PAM models based on their geometrical properties are the most commonly found ones in the scientific literature. In the process of deriving those models a lot of assumptions and simplifications are made due to the fact that PAM is a highly non-linear form of actuation. The purpose of this study is to propose additional considerations for future model improvements that will augment the overall model accuracy, and will best describe the relationship between force, displacement and non-linear thermal properties of PAM actuators through extensive observation and analysis of its thermodynamic characteristics during long-run operation experiments. In this article multiple experimental results will be presented that prove the relation between the thermodynamic properties of the PAMs, especially in iterative operations, and the accuracy on the muscle's force-prolongation relationship.

 Automated guided vehicles (AGVs) are widely used in warehouse and manufacturing facilities for point to point transportation of cargo. A typical objective in such facilities is space efficiency (store as many items as possible in a limited space) and throughput maximization. The two objectives are conflicting. Storing more items limits the available space for paths that can be used by the AGVs to transport goods within the facility, forcing the AGVs to take longer, more congested paths limiting the facilitys throughput. Therefore, to maintain acceptable throughput, an efficient task allocation and vehicle coordination is required. In this paper we study the automation of such vehicles in a warehouse with a specific topology. In the topology considered, vehicle movement is extremely constrained making the overall system prone to deadlocks. Delays caused by deadlocks are significant and seriously affect operational performance of the warehouse. Our main objective is to derive an efficient task assignment and agent coordination policy such that the overall system throughput is maximized. We present a problem transformation derived by the constraints and we propose a specific policy which is scalable and requires minimal computational power therefore it can be applied in real time.

 The paper proposes a Fault Detection and Isolation (FDI) procedure based on a model-free approach and the use of pattern recognition techniques. In particular this paper aims to improve the isolation performance of a Fuzzy Faults Classifier (FFC) previously proposed by the author by the adjustment of the membership function used in an inner fuzzification module and by the use of the Mahalanobis distance as metric for identifying the most probable fault. The proposed approach is applied to an industrial multishaft centrifugal compressor located in an Air Separation Unit (ASU) of a refinement plant. In particular faults due to the wear and tear of the thrust bearing and to fouling of the compressor stage are considered. The case study confirms the goodness of the overall procedure in the detections of both single as well as multiple faults and shows the improvements of the proposed approach in terms of the isolation system promptness.

 In this work the model identification of a visbreaking column for theestimation of the recovered volume at 360 C of Gasoil is considered and a filtering procedure for the selection of the identification dataset is presented. A high valuable product for the visbreaking process is the light gasoil; its purity can be measured by the recovered volume at 360 C and, for control purposes, an on-line estimation of this property is very important. In this paper a new procedure for predicting the light gasoil recovered volume is presented; the approach is based on the use of a clustering Fuzzy C-Means algorithm for the selection of the input data used in the identification process. Results are presented which prove the goodness of the proposed procedure and the reliability of the estimated model in the prediction of the gasoil recovered volume.