Sergiu Stan

A predictive control approach for the inverse pendulum on a cart problem

A model-based predictive control algorithm that uses a limited number of control sequences for on-line simulation of future behaviour of the process is presented in the paper. Each control sequence used in simulation generates a predicted sequence of the output signal. The output predicted sequences are analysed and evaluated and then, using a set of rules, the optimal control signal is computed. For simulating the future behaviour of the process it used a process model and also the previous sequences of the input and output signals from the process. The way it works, the algorithm permits the utilization of a nonlinear model of the process. For exemplifying the performance of the algorithm, the well-known problem of the inverted pendulum on a cart was chosen, in the position in which the cart movement is done in a limited space. The realized algorithm balances the position of the pendulum as well as the position of the cart.

Modeling and control of an electric arc furnace

Model based predictive control is a class of computer algorithms that explicitly use a process model to predict future plant outputs and compute an appropriate control action through on-line optimization of a cost objective over a future horizon, subject to various constraints. The cost function is defined in terms of the tracking error (the difference between the predicted output and set-point). Using this scheme, many different MBPC algorithms have been proposed in the literature. This paper presents an adaptive-predictive control algorithm, which uses on-line simulation and rule-based control. The algorithm is applied to an electrode position system of an electric arc furnace. Electric arc furnaces are commonly used in steelmaking and in smelting of nonferrous metals. To obtain the electric arc, usually there are used three graphite electrodes. The power level depends by the positions of the electrodes. As a result, the realization of a competitive control system is very important because it led to reduction of the energy consumption, pollution, and increases the safety of the process.

Applications of a model based predictive control to heat-exchangers

Model based predictive control (MBPC) is an optimization-based approach that has been successfully applied to a wide variety of control problems. When MBPC is employed on nonlinear processes, the application of this typical linear controller is limited to relatively small operating regions. The accuracy of the model has significant effect on the performance of the closed loop system. Hence, the capabilities of MBPC will degrade as the operating level moves away from its original design level of operation. This paper presents an MPC algorithm which uses on-line simulation and rule-based control. The basic idea is the online simulation of the future behaviour of control system, by using a few control sequences and based on nonlinear analytical model equations. Finally, the simulations are used to obtain the optimal control signal. These issues will be discussed and nonlinear modeling and control of a single-pass, concentric-tube, counter flow or parallel flow heat exchanger w ill be presented as an example.

Dynamic analysis of a Triglide parallel robot

This paper presents the analytical dynamic modeling of a Triglide parallel robot and numerical simulation using Maple and Adams software. The Lagrange with multipliers method was successfully applied to develop the closed-form dynamic model using the Maple software. Next, an equivalent dynamic virtual model of the Triglide parallel robot was developed and simulated in ADAMS program on different task. The simulation of the theoretical dynamic models (closed-form model) and ADAMS numerical approach confirm the validity of the analytical dynamic model.

Comparative dynamic analysis of two parallel robots

The paper presents a comparative dynamic modeling and VR (virtual reality) simulation for two 3 DOF medical parallel robots: a three coupled motions structure (Orthoglide robot) versus two coupled motion parallel structure (robot of type 1PRRR+2PRPaR). Kinematical and dynamical models, followed by a VR application with control aspects are presented for these two parallel robots. The innovative user interface for high-level control of the two parallel robots, presented in the paper, was developed in MATLAB - Simulink and SimMechanics environment, while the closed form dynamic models were obtained in MAPLE program. This kind of parallel robots can be successfully applied for medical applications where accuracy and high dynamic behavior are required. This research will lay a good foundation for the development of medical parallel robots.

CAD Based Design Optimization of Planar Parallel Manipulators

In this paper a new method for determination and optimization of the workspace of parallel manipulators is presented. The proposed method is based on a geometrical approach, and offers the possibility to generate automatically the workspace in a CAD environment. Thus, the relationship between the geometrical design parameters of the parallel manipulator and its workspace can be analyzed without difficulty. Optimization problem considered in this paper consists in determining the dimensions of a parallel manipulator having the closest workspace to a prescribed task region. Finally, numerical applications of two types of planar parallel manipulators are presented in order to illustrate the proposed approach.

Dynamic modeling and VR Simulation of 3DOF medical parallel robots

The paper presents dynamic modeling and VR (virtual reality) simulation issues for two 3 DOF medical parallel robots with three coupled motions (Orthoglide robot and with one coupled motion, robot of type 2PRRR+1PRPaR). Kinematical and dynamical models, followed by a VR application with control aspects are presented for these robots. The innovative user interface for high-level control of the two parallel robots, presented in the paper, was developed in MATLAB - Simulink and SimMechanics environment. This kind of parallel robots can be successfully applied for medical applications where accuracy and high dynamic behavior are required. This research will lay a good foundation for the development of medical parallel robots.

Nonlinear Control Using a Model Based Predictive Control Algorithm

This paper presents some applications of a model based predictive control (MBPC) type algorithm which is applied to nonlinear processes. The basic idea of the algorithm is the on-line simulation of the future behaviour of the control system, by using a few candidate control sequences. Then, using rule based control these simulations are used to obtain the optimal control signal. The efficiency and applicability of the proposed algorithm are demonstrated through applications.

Nonlinear Control Using On Line Simulation and Rule Based Control

Model based predictive control (MBPC) is a class of computer algorithms that explicitly use a process model to predict future plant outputs and compute an appropriate control action through on-line optimization of a cost objective function over a future horizon, subject to various constraints. This paper presents an MBPC type algorithm applied to nonlinear processes. The basic idea of the algorithm is the on-line simulation of the future behavior of the control system, by using a few candidate control sequences. Then, using rule based control, these simulations are used to obtain the optimal control signal. The efficiency and applicability of the proposed algorithm are demonstrated through applications

Optimization of the workspace of PKM with 2 DOF

In this paper a mono-objective optimum design procedure for parallel robot is outlined by using optimality criterion of workspace and numerical aspects. A mono-objective optimization problem is formulated by referring to a basic performance of parallel robots. Additional objective functions can be used to extend the proposed design procedure to more general but specific design problems. A kinematic optimization was performed to maximize the workspace of the mini parallel robot. Optimization was performed using genetic algorithms and simulated annealing