Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

open4siSustainable control has begun to stimulate research and development in a wide range of industrial communities particularly for systems that demand a high degree of reliability and availability (sustainability) and at the same time characterised by expensive and/or safety critical maintenance work. For heating systems such as HVAC plants, clear conflict exists between ensuring a high degree of availability and reducing costly maintenance times. HVAC systems have highly non-linear dynamics and a stochastic and uncontrollable driving force as input in the form of intake air speed, presenting an interesting challenge for modern control methods. Suitable control methods can provide sustainable maximisation of energy conversion efficiency over wider than normally expected air speeds and temperatures, whilst also giving a degree of “tolerance” to certain faults, providing an important impact on maintenance scheduling, e.g. by capturing the effects of some system faults before they become serious.This paper presents the design of different control strategies applied to a heating element nonlinear model. The description of this heating element was obtained exploiting a data driven and physically meaningful nonlinear continuous time model, which represents a test bed used in passive air conditioning for sustainable housing applications. This model has low complexity while achieving high simulation performance. The physical meaningfulness of the model provides an enhanced insight into the performance and functionality of the system. In return, this information can be used during the system simulation and improved model based and data driven control designs for tight temperature regulation. The main purpose of this study is thus to give several examples of viable and practical designs of control schemes with application to this heating element model. Moreover, extensive simulations and Monte Carlo analysis are the tools for assessing experimentally the main features of the proposed control schemes, in the presence of modelling and measurement errors. These developed control methods are also compared in order to evaluate advantages and drawbacks of the considered solutions. Finally, the exploited simulation tools can serve to highlight the potential application of the proposed control strategies to real air conditioning systems.openTurhan, T.; Simani, S.; Zajic, I.; Gokcen Akkurt, G.Turhan, T.; Simani, Silvio; Zajic, I.; Gokcen Akkurt, G

An interoperable workflow-based framework for the automation of building intelligent process control systems

One of the major problems to design and implement a control/supervision system for a process lies in the need to establish an ad-hoc system for each process installation. On the other side, an open challenge related to the deployment of Intelligent Decision Support Systems (IDSSs) is the interoperability of the different methods used, in order to allow interaction and reuse of different data mining methods and the use of methods based on a model or an expert. Thus, this paper proposes the use of visual workflows, to enable the automation of the design task and the implementation of Intelligent Process Control Systems (IPCSs). The framework will allow the user to specify the design and control of a concrete process as well as the required data-driven and expert models using a graphical workflow environment. The framework is based on a three-layer architecture: first, a comprehensive data science flow description layer (dataflow layer) to produce/discover data-driven models from process data; second, a flowchart of the different components of the process (process-design flow layer) to obtain a simulation model from the design. Finally, the on-line IPCS (process control workflow layer), where the different data-driven models, expert-based models and intelligent reasoning methods interoperate to control and supervise the process. Thus, the resulting system can automatically generate both simulation models of the process and programming code to control and supervise the process, using workflows designed for each particular installation. The case study is focused on the supervision of a Wastewater Treatment Plant (WWTP) located in the Barcelona region.The authors acknowledge the partial support of this work by the Industrial Doctorate Programme (2017-DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.Peer ReviewedPostprint (author's final draft

Flatness‑Based Control in Successive Loops of an H‑Type Gantry Crane with Dual PMLSM

Purpose In this article, the feedback control and stabilization problem of dual PMLSM-driven H-type gantry cranes is treated with the use of a fatness-based control method which is implemented in successive loops. Dual-drive gantry cranes can achieve high torque and high precision in the tasks’ execution. Such a type of crane can be used in several industrial applications. The solution to the associated nonlinear control problem is a particularly challenging research objective. Methods The integrated system that comprises the H-type gantry crane and two PMLSMs is shown to be diferentially fat. The control problem for this robotic system is solved with the use of a fatness-based control approach which is implemented in successive loops. To apply the multi-loop fatness-based control scheme, the state-space model of the H-type gantry crane with dual PMLSM is separated into subsystems, which are connected in cascading loops. Results For each subsystem, control can be performed with inversion of its dynamics as in the case of input–output linearized fat systems. The state variables of the preceding (ith) subsystem become virtual control inputs for the subsequent (i+1)th subsystem. In turn, exogenous control inputs are applied to the last subsystem. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. Conclusion A novel nonlinear optimal control method has been developed for the dynamic model of a dual PMLSM-driven gantry crane. The proposed method achieves stabilization of the H-type gantry crane with dual PMLSM without the need for difeomorphisms and complicated state-space model transformations. Using the local diferential fatness properties of each one of the subsystems that constitute the gantry crane's model, the design of a stabilizing feedback controller is enabled.This research work has been partially supported by Grant Ref. 301022 ’Nonlinear optimal and fatness-based control methods for complex dynamical systems’ of the Unit of Industrial Automation of the Industrial Systems Institute. Besides, the authors, Pierluigi Siano and Mohammed Al-Numay acknowledge fnancial support from the Researchers Supporting Project Number (RSP2023R150), King Saud University, Riyadh, Saudi Arabia

A realization model to develop the autopilot system of ships by specializing MDA

This paper presents a method which is based on the Model-Driven Architecture (MDA) and functional blocks to realize effectively the autopilot systems of ships. It brings out an executable MDA process to cover completely the requirement analysis, design and deployment phases of these systems. This process also allows the determined design elements to be customizable and re-usable in the new applications of controlled ship steering systems. The paper indicates straightforwardly the ship dynamic model-to-be used, the Computation Independent Model (CIM) of a ship autopilot system, the Platform Independent Model (PIM) of this system by using the Real-Time Unified Modeling Language (UML), and its Platform Specific Model (PSM) implemented by the functional blocks. Furthermore, the important transformation rules are also brought out and applied to convert the identified PIM into PSM for implementing quickly this system with different industrial frameworks such as the IEC61499 in a programmable controller. Then, its deployment model completely is tested on a model ship with the predetermined program and control performance

Tracking control for directional drilling systems using robust feedback model predictive control

A rotary steerable system (RSS) is a drilling technology which has been extensively studied and used for over the last 20 years in hydrocarbon exploration and it is expected to drill complex curved borehole trajectories. RSSs are commonly treated as dynamic robotic actuator systems, driven by a reference signal and typically controlled by using a feedback loop control law. However, due to spatial delays, parametric uncertainties and the presence of disturbances in such an unpredictable working environment, designing such control laws is not a straightforward process. Furthermore, due to their inherent delayed feedback, described by delay differential equations (DDE), directional drilling systems have the potential to become unstable given the requisite conditions. This paper proposes a Robust Model Predictive Control (RMPC) scheme for industrial directional drilling, which incorporates a simplified model described by ordinary differential equations (ODE), taking into account disturbances and system uncertainties which arise from design approximations within the formulation of RMPC. The stability and computational efficiency of the scheme are improved by a state feedback strategy computed offline using Robust Positive Invariant (RPI) sets control approach and model reduction techniques. A crucial advantage of the proposed control scheme is that it computes an optimal control input considering physical and designer constraints. The control strategy is applied in an industrial directional drilling configuration represented by a DDE model and its performance is illustrated by simulations

Optimal selection of control structure using a steady-state inversely controlled process model

The profitability of chemical processes strongly depends on their control systems. The design of a control system involves selection of controlled and manipulated variables, known as control structure selection. Systematic generation and screening alternative control structures requires optimization. However, the size of such an optimization problem is much larger when candidate controllers and their parameters are included and it rapidly becomes intractable. This paper presents a novel optimization framework using the notion of perfect control, which disentangles the complexities of the controllers. This framework reduces the complexity of the problem while ensuring controllability. In addition, the optimization framework has a goal-driven multi-objective function and requires only a steady-state inverse process model. Since dynamic degrees of freedom do not appear in a steady-state analysis, engineering insights are employed for developing the inventory control systems. The proposed optimization framework was demonstrated in a case study of an industrial distillation train

A model-based approach to the development of distributed control systems

Distributed Control Systems (DCS) are a class of application with specific characteristics. This type of system is used in industrial environments to control manufacturing processes. Usually they comprise a controller, a fieldbus network, and a set of Of-The-Shelf (OTS) components, interfacing process signals with real-time QoS requirements. In this paper we present a Model Driven Development (MDD) method that targets this category of systems. This method focuses on the critical stages of DCS development. Namely, the specification of system requirements, the choice of OTS modules and fieldbus system, and the validation of the design using real-time analysis tools. This MDD method uses the Unified modelling Language (UML) as support notation, including the extensions defined in the UML Profile for Schedulability, Performance and Time Specification.Fundação para a Ciência e Tecnologia; FEDER – Project METHODES (POSI/37334/CHS/2001)

An Adaptive Controller Design for Flexible-joint Electrically-driven Robots With Consideration of Time-Varying Uncertainties

Almost all present control strategies for electrically-driven robots are under the rigid robot assumption. Few results can be found for the control of electrically driven robots with joint flexibility. This is because the presence of the joint flexibility greatly increases the complexity of the system dynamics. What is worse is when some system dynamics are not available and a good performance controller is required. In this paper, an adaptive design is proposed to this challenging problem. A backstepping-like procedure incorporating the model reference adaptive control is employed to circumvent the difficulty introduced by its cascade structure and various uncertainties. A Lyapunov-like analysis is used to justify the closed-loop stability and boundedness of internal signals. Moreover, the upper bounds of tracking errors in the transient state are also derived. Computer simulation results are presented to demonstrate the usefulness of the proposed scheme. Keywords: Adaptive control; Flexible-joint electrically-driven robot; FAT 2. Introduction Control of rigid robots has been well understood in recent years, but most of the schemes ignore the dynamics coming from electric motors and harmonic drivers that are widely implemented in the industrial robots. However, actuator dynamics constitute an important part of the complete robot dynamics, especially in the cases of high-velocity movement and highly varying loads[1],[2]. The main reason for using a reduced model is to simplify complexity of controller design. For each joint, consideration of the flexibility from the M. C. Chien was with the Department of Mechanical Engineering, National Taiwan University of Science and Technology. He is now with the Mechanical and Systems Research Laboratories, Industrial Technology Research Institute, No. 195, Sec. 4, Chung-Hsing Rd., Chutung, Hsinchu, 310, Taiwan, R.O.C. (e-mail: [email protected]). 2 A. C. Huang is with the Department of Mechanical Engineering, National Taiwan University of Science and Technology. No. 43, Keelung Rd., Sec. 4, Taipei, Taiwan, ROC. (Tel:+886-2-27376490, Fax: +886-2-37376460, E-mail: [email protected]). (A. C. Huang provides phone number because he is the corresponding author.

Finite element modeling and simulation of a robotic finger actuated by Ni-Ti shape memory alloy wires

In this paper, a dynamic model for an artificial finger driven by Shape Memory Alloy (SMA) wires is presented. Due to their high energy density, these alloys permit the realization of highly compact actuation solutions with potential applications in many areas of robotics, ranging from industrial to biomedical ones. Despite many advantages, SMAs exhibit a highly nonlinear and hysteretic behavior which complicates system design, modeling, and control. In case SMA wires are used to activate complex robotic systems, the further kinematic nonlinearities and contact problems make the modeling significantly more challenging. In this paper, we present a finite element model for a finger prototype actuated by a bundle of SMA wires. The commercially available software COMSOL is used to couple the finger structure with the SMA material, described via the Muller-Achenbach-Seelecke model. By means of several experiments, it is demonstrated how the model reproduces the finger response for different control inputs and actuator geometries

Multicontroller: an object programming approach to introduce advanced control algorithms for the GCS large scale project

The GCS (Gas Control System) project team at CERN uses a Model Driven Approach with a Framework - UNICOS (UNified Industrial COntrol System) - based on PLC (Programming Language Controller) and SCADA (Supervisory Control And Data Acquisition) technologies. The first' UNICOS versions were able to provide a PID (Proportional Integrative Derivative) controller whereas the Gas Systems required more advanced control strategies. The MultiController is a new UNICOS object which provides the following advanced control algorithms: Smith Predictor, PFC (Predictive Function Control), RST* and GPC (Global Predictive Control). Its design is based on a monolithic entity with a global structure definition which is able to capture the desired set of parameters of any specific control algorithm supported by the object. The SCADA system -- PVSS - supervises the MultiController operation. The PVSS interface provides users with supervision faceplate, in particular it links any MultiController with recipes: the GCS experts are able to capture sets of relevant advanced control algorithm parameters to reuse them later. Starting by exposing the MultiController object design and implementation for a PVSS and Schneider PLC solution, this paper finishes by highlighting the benefits of the MultiController with the GCS applications