Ioan Nascu

Identification and path following control of an AR.Drone quadrotor

This paper describes the process of identification and closed-loop control of an Parrot AR.Drone Unmanned Aerial Vehicle (UAV) as well as a path following application based on IMC position controllers. The research issue is to achieve position control of the AR.Drone quadrotor movement via its on-board sensory equipment and external webcam video stream. Firstly, transfer functions are detailed for pitch and altitude movements and a comparison is made between implemented PID and IMC controller performance for both simulation and practice. Furthermore, using IMC controllers, a path following application exhibits controller behavior from a practical point of view. It is concluded that the dynamic model and the controllers implemented on the quadrotor can serve as a reliable basis for more advanced applications.

Leader-follower string formation using cascade control for mobile robots

This paper proposes a simple yet effective control algorithm for a platoon of “car-like” robots. The formation used is a line, leader-follower formation, i. e. each robot is a follower for the previous robot and a leader for the next robot. The formation is implemented using speed measurements from optical encoders and distance measurements from image processing, but there is no communication between robots. String stability analysis on the resulting formation is performed. The results of the proposed method are verified by both simulations in Matlab® as well as measured data from the actual mobile robots.

Adaptive EPSAC predictive control of the hypnotic component in anesthesia

The applications presented in this paper are oriented towards the study of the possibilities to apply adaptive algorithms to a model based EPSAC (Extended Prediction Self-Adaptive Control) scheme for control of depth of anesthesia (DOA). The input variable is Propofol, an anesthetic substance used for both induction and maintenance control of DOA which is evaluated by means of the Bispectral index (BIS). An adaptive control algorithm based on an indirect adaptive predictive method is used. The unknown model parameters are estimated by recursive least square algorithm. The simulation tests performed on virtually generated models for a set of 12 patients show a fast response, optimal dosage and robustness of the algorithm regarding the control of the desired BIS setpoint during induction and maintainance phase.

Development and application of a PID auto-tuning method to a wastewater treatment process

This paper presents an application regarding a developed extension of the widely used relay-feedback PID auto-tuner. The proposed method consists of two steps: process identification and controller design. First, a non-iterative procedure is suggested for identification of one point on the process Nyquist curve. A second-order model (SOS) is obtained and then used for PID controller design based on the internal model principle (IMC). For the identification of the point on the Nyquist curve a relay in the feedback loop (as used in standard auto-tuning) which operates on the integral of the error is used. The method is illustrated on an application to a wastewater treatment process. In the considered process, the wastewater is treated in order to obtain an effluent having the substrate concentration within the standard limits. The performances of control algorithms are illustrated by simulation results. The influence of changing a design parameter in the desired closed loop behavior is shown.

Identification and control of a miniature rotorcraft Unmanned Aerial Vehicle (UAV)

We present in this paper an Unmanned Aerial Vehicle(UAV) based on a miniature helicopter. The UAV platform consists of sensors that sense the movement of the helicopter, wireless communication and a PC based controller. The model identification for the yaw movement is described. Based on the identified model, an incremental PID controller was designed and implemented, with very good results.

Development and application of a predictive adaptive controller to a wastewater treatment process

This paper presents an application regarding a single input single output (SISO) long range predictive adaptive controller. The Generalized Predictive Control (GPC) method uses long range prediction based on a dynamic model of the plant. The model is obtained in the adaptive context by recursive estimation. The parameter estimation algorithm, basically the recursive least square algorithm, has been modified to provide a robust parameter estimator. The proposed algorithms are used to control the biological part of a wastewater treatment process. The performances of control algorithms are illustrated by simulation results.

Development and Evaluation of a PID Auto-Tuning Controller

This paper presents comparative studies regarding a recently developed extension of the widely used relay-feedback PID auto-tuner. The proposed method consists of two steps: process identification and controller design. First, a non-iterative procedure is suggested for identification of two points on the process Nyquist curve. A second-order-plus-dead-time model is obtained and then used for PID controller design based on the internal model principle (IMC). For the identification of the two points on the Nyquist curve a pure relay in the feedback loop (as used in standard auto-tuning) and a relay which operates on the integral of the error are used. The paper compares the performance of autotuning methods with experimental studies on a commercial auto-tuning PID controller - a Shimaden PID auto-tuner

Evaluation of three protocols for automatic DOA regulation using Propofol and Remifentanil

The objective of this paper is to present the result of a simulation study in order to evaluate three different protocols for the administration of Propofol and Remifentanil. These protocols are used in a model based predictive control algorithm for automatic induction and control of depth of anaesthesia (DOA). In this study, DOA is monitored using the Bispectral Index (BIS) during the maintenance of general anaesthesia. The closed loop control tests are performed on a set of 24 virtually generated realistic patients. The closed loop control performance of the three different protocols are evaluated, showing that a unified approach leads to better overall performance.

Identification and modeling of the three rotational movements of a miniature coaxial helicopter

This paper presents the identification of a miniature coaxial helicopter system. First, the helicopter flying principles are described and the hardware setup of the developed platform is presented. Further, linear models are developed for the movements of the helicopter using prediction error identification methods. The results in this case are accurate and can be used for performant controller design in some operating points. But in order to model the complete dynamics of the helicopter, nonlinear models are developed using recurrent dynamic neural networks. In this case the models obtained present a higher accuracy compared with the linear case and also with the results published until now. In the end, the advantages of nonlinear modeling based on neural networks is emphasized and some conclusions are drawn.

RGA analysis and decentralized control for a wastewater treatment plant

A vital process in wastewater treatment is the biological treatment with activated sludge. In Romania, there are still plenty of cases where the biological treatment stage is operated manually, leading to inefficient operation and implicitly to higher operational costs, increased difficulty in maintaining the quality of the effluent and less disturbance rejection capabilities. In our paper, we show that using a medium-fidelity first principle model and by carrying out an RGA analysis, a decentralized control strategy using PI controllers can be easily developed and tested before implementation, the control strategy giving good results in disturbance rejection and trajectory tracking.