This research deals with the modeling and control of a plasticating twin screw extruder (TSE) that will be used to obtain consistent product quality. The TSE is a widely used process technology for compounding raw polymers. Compounding creates a polymer with improved properties that satisfy the demand of modern plastic applications. Modeling and control of a TSE is challenging because of its high nonlinearity, inherent time delay, and multiple interactive dynamic behavior. A complete methodology is proposed in this thesis to design an advanced control scheme for a TSE. This methodology was used to develop a model predictive control scheme for a laboratory scale plasticating TSE and to implement the control scheme in real-time. The TSE has a processing length of 925 mm and a length to screw diameter ratio (L/D) of 37. High density polyethylenes with different melt indices were used as processing materials. Manipulated variables and disturbance variables were selected based on knowledge of the process. Controlled variables were selected using a selection method that includes a steady state correlation between process output variables and product quality variables, and dynamic considerations. Two process output variables, melt temperature (Tm) at the die and melt pressure (Pm) at the die, were selected as controlled variables. A new modeling approach was proposed to develop grey box models based on excitation in the extruder screw speed (N), one of the manipulated variables. The extruder was excited using a predesigned random binary sequence (RBS) type excitation in N and nonlinear models relating Tm and Pm to N were developed using this approach. System identification techniques were used to obtain model parameters. The obtained models have an autoregressive moving average with exogenous (ARMAX) input structure and the models explain the physics of the extrusion process successfully. The TSE was also excited using a predesigned RBS in the feed rate (F) as a manipulated variable. Models relating Tm and Pm to F were developed using a classical system identification technique; both models have ARMAX structures. The model between Pm and F was found to give excellent prediction for data obtained from a stair type excitation, indicating that the obtained models provide a good representation of the dynamics of the twin screw extruder. Analysis of the TSE open loop process indicated two manipulated variables, N and F, and two controlled variables, Tm and Pm. Thus, a model predictive controller (MPC) was designed using the developed models for this 2X2 system and implemented in real-time. The performance of the MPC was studied by checking its set-point tracking ability. The robustness of the MPC was also examined by imposing external disturbances. Finally, a multimodel operating regime was used to model Tm and N. The operating regime was divided based on the screw speed, N. Local models were developed using system identification techniques. The global model was developed by combining local models using fuzzy logic methodology. Simulated results showed excellent response of Tm for a wide operating range. A similar approach was used to design a global nonlinear proportional-integral controller (n-PI) and a nonlinear MPC (n-MPC). Both the controllers showed good set-points tracking ability over the operating range. The multiple model-based MPC showed smooth transitions from one operating regime to another operating regime. / Process Control

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In this thesis a quadrotor is studied and a linear model is derived using grey-box estimation, a discipline in system identification where a model structure based on physical relations is used and the parameters are estimated using input-output measurements. From IMU measurements and measured PWM signals to the four motors, a direct approach using the prediction-error method is applied. To investigate the impact of the unknown controller the two-stage method, a closed-loop approach in system identification,  is applied as well. The direct approach was enough for estimating the model parameters. The resulting model manages to simulate the major dynamics for the vertical acceleration and the angular rates well enough  for future control design.

Today’s power systems are based on a centralised system and distribution networks that are considered as passive terminations of transmission networks. The high penetration of Distributed Generation (DG) at the distribution network level has created many challenges for this structure. New tools and simulation approaches are required to address the subject and to quantify the dynamic characteristics of the system. A distribution network or part of it with DG, Active Distribution Network Cell (ADNC), can no longer be considered as passive. An equivalent dynamic model of ADNC is therefore extremely important, as it enables power system operators to quickly estimate the impact of disturbances on the power system’s dynamic behaviour. A dynamic equivalent model works by reducing both the complexity of the distribution network and the computation time required to run a full dynamic simulation. It offers a simple and low-order representation of the system without compromising distribution network dynamic characteristics and behaviour as seen by the external grid. This research aims to develop a dynamic equivalent model for ADNC. It focuses on the development of an equivalent model by exploiting system identification theory, i.e. the grey-box approach. The first part of the thesis gives a comprehensive overview and background of the dynamic equivalent techniques for power systems. The research was inspired by previous work on system identification theory. It further demonstrates the theoretical concept of system identification, system load modelling and the modelling of major types of DG. An equivalent model is developed, guided by the assumed structure of the system. The problem of equivalent model development is then formulated under a system identification framework, and the parameter estimation methodology is proposed. The validation results of the effectiveness and accuracy of the developed model are presented. This includes the estimation of the parameter model using a clustering algorithm to improve the computational performance and the analysis of transformer impedance effects on the ADNC responses. The evaluation of probability density function, eigenvalue analysis and parameter sensitivity analysis for the model parameters are also presented. Typical model parameters for different network topologies and configurations are identified. Finally, the developed equivalent model is used for a large power system application. The accuracy and robustness of the developed equivalent model are demonstrated under small and large disturbance studies for various types of fault and different fault locations.

Buildings are currently controlled in a sub optimal way, using a WC controller that is dependent only on the external temperature. A rich amount of real-time data from installed sensors is available within the buildings and the network and can be used to counteract this. To better control the indoor temperature and the heat supply this degree-project develops a model and optimizer for control of the indoor temperature, where industry standard data streams are used as inputs. The model and optimizer can be implemented in a MPC which takes the future external temperature into consideration and enhances the ability to control the heat supply. There are two main reasons why enhanced control is interesting to look at, the economic aspects and the comfort of the occupancies. This degree project is focused on developing a general building model for the purpose of utilizing the building as an energy storage for peak-shaving.  The finalized model is a dynamic grey-box model developed using data from a multifamily building, Building A, located in Västerås Sweden. The training period is set to 408 hours, and the prediction horizon is set to 48 hours as a result of the verification. To demonstrate the utilization possibilities of using the building as a heat storage, an optimizer is constructed to evaluate a peak shaving control strategy. The control objective (Qsupply) is controlled by manipulating the indoor temperature (Tin) within a set interval. By setting a fixed interval for the indoor temperature within the comfort interval, the comfort is still maintained. For the peak shaving different flexibilities within the indoor temperature have been examined with a range from 22 +/- 0.25 degrees Celcius to 22 +/- 2.00 degrees Celcius.  The model is verified in 4 steps: prediction ability on the historic data, parametric verification on the time constant, simulation of heat supply separately from the historic data and model generality by implementing the model on a second multifamily building, Building B. The model has a RRMSE of 8% for Building A and 9% for Building B which is considered excellent.  Due to the lack of access to the real building, the developed model is not validated. Based on peak shaving and energy consumption, the preferred solution is 22+/- 1.25 degrees Celcius. But based on surveys about occupancies attitude toward flexibility in the indoor temperature and economical aspects, an indoor temperature of 22 +/- 0.50 degrees Celcius is considered the best choice with the maximum peak in the heat supplied decreased by 35% and the energy consumption is decreased by 10% compared to the historical case. We suggest allowing the customers to choose their preferred flexibility to ensure comfort.