A comparative study of three validities computation methods for multimodel approach

The multimodel approach offers a very satisfactory results in modelling, diagnose and control of complex systems. In the modelling case, this approach passes by three steps: the determination of the model’s library, the validities computation and the establishment of the final model. In this context, this paper focuses on the elaboration of a comparative study between three recent methods of validities computation. Thus, it highlight the method that offers the best performances in term of precision. To achieve this goal, we apply, these three methods on two simulation examples in order to compare their performances

Sliding Mode Control

The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area

Singular Perturbations and Time-Scale Methods in Control Theory: Survey 1976-1982

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryJoint Services Electronics Program / N00014-79-C-0424U.S. Air Force / AFOSR 78-363

Identification of Multimodel LPV Models with Asymmetric Gaussian Weighting Function

This paper is concerned with the identification of linear parameter varying (LPV) systems by utilizing a multimodel structure. To improve the approximation capability of the LPV model, asymmetric Gaussian weighting functions are introduced and compared with commonly used symmetric Gaussian functions. By this mean, locations of operating points can be selected freely. It has been demonstrated through simulations with a high purity distillation column that the identified models provide more satisfactory approximation. Moreover, an experiment is performed on real HVAC (heating, ventilation, and air-conditioning) to further validate the effectiveness of the proposed approach

Optimal Control of Unknown Nonlinear System From Inputoutput Data

Optimal control designers usually require a plant model to design a controller. The problem is the controller\u27s performance heavily depends on the accuracy of the plant model. However, in many situations, it is very time-consuming to implement the system identification procedure and an accurate structure of a plant model is very difficult to obtain. On the other hand, neuro-fuzzy models with product inference engine, singleton fuzzifier, center average defuzzifier, and Gaussian membership functions can be easily trained by many well-established learning algorithms based on given input-output data pairs. Therefore, this kind of model is used in the current optimal controller design. Two approaches of designing optimal controllers of unknown nonlinear systems based on neuro-fuzzy models are presented in the thesis. The first approach first utilizes neuro-fuzzy models to approximate the unknown nonlinear systems, and then the feasible-direction algorithm is used to achieve the numerical solution of the Euler-Lagrange equations of the formulated optimal control problem. This algorithm uses the steepest descent to find the search direction and then apply a one-dimensional search routine to find the best step length. Finally several nonlinear optimal control problems are simulated and the results show that the performance of the proposed approach is quite similar to that of optimal control to the system represented by an explicit mathematical model. However, due to the limitation of the feasible-direction algorithm, this method cannot be applied to highly nonlinear and dimensional plants. Therefore, another approach that can overcome these drawbacks is proposed. This method utilizes Takagi-Sugeno (TS) fuzzy models to design the optimal controller. TS fuzzy models are first derived from the direct linearization of the neuro-fuzzy models, which is close to the local linearization of the nonlinear dynamic systems. The operating points are chosen so that the TS fuzzy model is a good approximation of the neuro-fuzzy model. Based on the TS fuzzy model, the optimal control is implemented for a nonlinear two-link flexible robot and a rigid asymmetric spacecraft, thus providing the possibility of implementing the well-established optimal control method on unknown nonlinear dynamic systems

Core power control analysis and design for triga nuclear reactor

An efficient nuclear core power control is essential in providing a safe and reliable nuclear power generation system. It is technically challenging to ensure that the core power output is always stable and operating within acceptable error bands. The core power control in TRIGA PUSPATI Reactor (RTP) Malaysia is designed based on the Feedback Control Algorithm (FCA), which includes the Proportional- Integral controller, Control Rod Selection Algorithm (CRSA), Control Rod Velocity Design (CRVD), and Power Change Rate Constraint (PCRC). However, the current setting generally produces an unsmooth transient response and a long settling time. The conventional CRSA suffers during transient and fine-tuning conditions due to the rod selection process only considers the rod position and ignores the rod worth value. The conventional PCRC has a constant gain, incapable of providing a sufficient amount of penalty and sensitivity effects on control rod velocity under all operating conditions. Thus, a new strategy for each component in the FCA is investigated to further improve overall core power tracking performance. To address the current CRSA problems, a novel CRSA called Single Control Absorbing Rod (SCAR) is designed based on the rod worth value and operational condition-based activation. The SCAR is not only reducing the complexity of the CRSA process but also reduces the time required for rod selection. In addition, a new saturation model and velocity value are studied for CRVD. On top of that, a fuzzy-based PCRC is proposed to produce a fast-tracking power response. Finally, a hybrid controller based on the integration of Model Predictive Control and Proportional controller is developed to exploit the benefits of both controllers via a switching control mechanism. In the present study, the RTP model is derived based on equations of neutronic, thermal-hydraulic, reactivity, and dynamic rod position. Both analytical and system identification models are considered. In the proposed design strategy, all of the safety design requirements based on the Final Safety Analysis Report are taken into account, ensuring that the outcome of the study is practical and reliable. The proposed strategy is designed via simulation with MATLAB Simulink and experimentation with actual hardware at the RTP. A stability analysis based on Lyapunov is derived to numerically guarantee the stability of the new power controller. An extensive comparison to the existing FCA is presented to demonstrate the compatibility and effectiveness of the proposed strategies in nuclear reactor environments. Overall, the results show that the response from hybrid Model Predictive Control-Proportional (MPC-P) offers better results than the FCA, in which reduces the rise time by up to 73 %, the settling time by up to 70 %, and the workload by up to 42 %. The hybrid MPC-P with multiple-component constraints is able to solve the unsmooth transient response and a long settling time tracking performance at the RTP and offers improvements in terms of fuel economic aspect in the long run and extending the lifetime of the plant operation

Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach

In this paper, historical data from a wholesale alcoholic beverage distributor was used to forecast sales demand. Demand forecasting is a vital part of the sale and distribution of many goods. Accurate forecasting can be used to optimize inventory, improve cash ow, and enhance customer service. However, demand forecasting is a challenging task due to the many unknowns that can impact sales, such as the weather and the state of the economy. While many studies focus effort on modeling consumer demand and endpoint retail sales, this study focused on demand forecasting from the distributor perspective. An ensemble approach was applied using traditional statistical univariate time series models, multivariate models, and contemporary deep learning-based models. The final ensemble models for the most sold product and highest revenue grossing product were able to reduce sales forecasting error by nearly 50% and 33.5%, respectively, in comparison to a statistical naive model. Additionally, this paper determined that there is no one size fits all demand model for all products sold by the distributor; each product needs an individually tuned model to meaningfully reduce error