Fakulta elektrotechniky a informatiky / Faculty of Electrical Engineering and Informatics
Stálý URI pro tuto komunituhttps://hdl.handle.net/10195/3847
Práce obhájené před rokem 2008 jsou uloženy pouze v kolekci Vysokoškolské kvalifikační práce
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Konferenční objektpeer-reviewedpostprint Omezený přístup Application of Model Predictive Controller to Magnetic Levitation(IEEE (Institute of Electrical and Electronics Engineers), 2023) Novotný, Aleš; Honc, DanielModel Predictive Control (MPC) is an advanced process control method that is widely used for controlling both linear and under some modifications for non-linear systems. The aim of this work is to show a way how to apply MPC to a non-linear Magnetic Levitation System (MLS) and its capability of stabilization and closed-loop performance. This work is a continuation of the previous article where the laboratory plant CE 152 MLS was identified, and a non-linear model was designed. This paper proposes a control circuit consisting of linearized discretized non-linear MLS model, Extended Kalman Filter (EKF) algorithm for state estimation and linear MPC. The results are verified in simulation and real-world experiment.Konferenční objektpeer-reviewedpostprint (accepted version) Omezený přístup Explanation of the predictive controller and the effect of its tuning on the control quality(Springer Nature Switzerland AG, 2022) Honc, Daniel; Novotný, Aleš; Kupka, LiborThe Model Predictive Control (MPC) concept and its realization is explained together with some real-world laboratory application examples. Usually, future control errors and control action changes are penalized in the cost function (objective) by predictive controllers. The cost function can be seen as a function of future control actions utilizing the process model in a form of the predictor. A derivation of the predictors for the transfer function (external) and state-space (internal) model is indicated in the paper. An analytical solution to the given optimization problem is possible in an unconstrained case. A quadratic programming strategy must be used in case of the occurrence of the process constraints that should be respected by the controller. The authors apply both algorithms to two types of dynamical systems – to proportional (stable) system and to integrating (unstable) system and they demonstrate the influence of the penalization parameters (weights) in the cost function on the control quality. The authors aim to high-light the MPC strategy and its potential, and on the other hand, mention some bottlenecks and risks associated with model-optimisation-based methods.Článekpeer-reviewedpostprint Otevřený přístup Desired Terminal State Concept in Model Predictive Control: A Case Study(Hindawi limited, 2019) Dušek, František; Honc, DanielThe paper deals with an online optimization control method for dynamical processes called Model Predictive Control (MPC). It is a popular control method in industry and frequently treated in academic areas as well. The standard predictive controllers usually do not guarantee stability especially for the case of short horizons and large control error penalization. Terminal state is one way to ensure stability or at least increase the controller robustness. In the paper, deviation of the predicted terminal state from the desired terminal state is considered as one term of the cost function. Effect of the stability and control quality is demonstrated in the simulated experiments. The application area for online optimization methods is very broad including various logistics and transport problems. If the dynamics of the controlled processes cannot be neglected, the optimization problem must be solved not only for steady state but also for transient behaviour, e.g., by MPC.Konferenční objektpeer-reviewedpostprint Omezený přístup Predictive Controller Based on Feedforward Neural Network with Rectified Linear Units(Springer Nature Switzerland AG, 2019) Doležel, Petr; Honc, Daniel; Štursa, DominikThis paper deals with a nonlinear Model Predictive Control with a special form of the process model. Controller uses for the prediction purposes a locally valid linear sub-models. The sub-models are obtained from a neural model with the rectifier activation function in hidden neurons. Simulation example is given to demonstrate proposed solution - neural model design and predictive controller application.Konferenční objektpeer-reviewedpostprint Otevřený přístup Comparitive study of predictive controllers for trajectory tracking of non-holonomic mobile robot(IEEE (Institute of Electrical and Electronics Engineers), 2017) Kizhakke Illom, Rahul Sharma; Dušek, František; Honc, DanielThe paper deals with predictive control of non-holonomic mobile robot. The basic nonlinear kinematic equation is linearized into two different linear time varying models based on frame of reference-world coordinates and local coordinate of mobile robot. The non-linear model predictive control is applied to the trajectory tracking problem of a non-holonomic mobile robot with these models. The control law is derived from a cost function which penalizes the state tracking error, control effort and terminal state deviation error. Various simulation experiments are conducted and a comparative analysis has been made with respect to state-of-the-art approaches.Konferenční objektpeer-reviewedpostprint Otevřený přístup Predictive Control of Nonlinear Plant Using Piecewise-Linear Neural Model(IEEE (Institute of Electrical and Electronics Engineers), 2017) Honc, Daniel; Doležel, Petr; Gago, LumírA special form of a predictive controller is presented in this paper. Based on previous authors' work, a piecewise-linear neural model of nonlinear plant to be controlled is adopted to local linearization. The linearized model is then used for control action evaluation using a predictive controller. Although the linearization using piecewise-linear neural network is simple and efficient, it provides the model in a nonstandard form. Therefore, the proposed predictive controller is designed in order to handle that nonstandard model without any customization. At the end of the paper, the illustrative example demonstrates the main features of the introduced solution.Konferenční objektpeer-reviewedpostprint Otevřený přístup Optimal control with disturbance estimation(Nottingham Trent University, 2017) Dušek, František; Honc, Daniel; Kizhakke Illom, Rahul SharmaThe paper deals with a very common situation in many control systems and this is the fact that, for zero control action, the controlled variable is nonzero. This is often caused by the existence of another process input which is uncontrolled. Classic controllers do not take into account the second input, so deviation variables are considered or some feedforward controller is used to compensate the variable. The authors propose a solution, that the process is considered as a system with two inputs and single output (TISO). Here, the uncontrolled input is estimated with the state observer and the controller is designed as the multivariable controller. A Linear-quadratic (LQ) state-feedback control and model predictive control (MPC) of simple thermal process simulations are provided to demonstrate the proposed control strategy.Konferenční objektpeer-reviewedpostprint Omezený přístup PREDICTIVE CONTROL OF DIFFERENTIAL DRIVE MOBILE ROBOT CONSIDERING DYNAMICS AND KINEMATICS(EUROPEAN COUNCIL MODELLING & SIMULATION, 2016) Sharma K., Rahul; Honc, Daniel; Dušek, FrantišekThe paper deals with trajectory tracking of the differential drive robot with a mathematical model governing dynamics and kinematics. Motor dynamics and chassis dynamics are considered for deriving a linear state-space dynamic model. Basic nonlinear kinematic equations are linearized into a successively linearized state-space model. The dynamic and kinematic models are augmented to derive a single state-space linear model. The deviation variables are reference variables which are variables of an ideal robot following a reference trajectory which can be pre-calculated. Reference tracking is achieved by model predictive control of supply voltage of both the drive motors by considering constraints on controlled variables and manipulated variables. Simulation results are provided to demonstrate the performance of proposed control strategy in the MATLAB simulation environment.