Šandor Ileš

Real-time Predictive Control of 3D tower crane

In this paper a real-time Model Predictive Control (MPC) for a 3D tower crane, based on subsequent solving of three quadratic programs, is proposed. Three motions of the tower crane are considered as separate subsystems with couplings among them treated as a change in system parameters. Such linear parameter varying (LPV) system can be sampled and transformed into the corresponding polytopic model, by using a Tensor Product (TP) Model Transformation. Polytopic TP model of the tower crane is used to calculate the terminal set and the terminal cost via solving Linear Matrix Inequalities (LMI). In order to guarantee recursive feasibility, system states are kept in ellipsoidal approximation of worst case initial feasible set, while the asymptotic stability is ensured by using a dual-mode MPC strategy. The proposed approach is verified through simulation and experimental test on laboratory model of a 3D tower crane.

Tensor product based control of the Single Pendulum Gantry process with stable neural network based friction compensation

Fast and accurate positioning and swing minimization of the containers and other loads in crane manipulation are demanding and in the same time conflicting tasks. For accurate positioning, the main problem is nonlinear friction effect, especially in the low speed region. In this paper authors propose position controller realized as hybrid controller. It consists of the tensor product based nonlinear feedback controller with additional friction self-learning neural compensator. The experimental results show that friction compensator is able to remove position error in steady state.

Nonlinear predictive control of a tower crane using reference shaping approach

In this paper nonlinear Model Predictive Control of a tower crane based on reference shaping is proposed. MPC controller is used to calculate optimal reference for the inner control loop of the tower crane. The main objectives that the MPC controller needs to fulfill are tracking the reference position, suppressing the payload oscillations while satisfying operational constraints of the crane. The inner loop consists of P position controller and PI velocity controller which is common in industrial applications and easily implementable in standard frequency converters used in the cranes. The proposed approach is verified through simulation and experimental test on laboratory model of a 3D tower crane.

Stabilizing Model Predictive Control based on flexible set-membership constraints

This paper presents a stabilizing Model Predictive Control (MPC) algorithm based on the off-line computation of a sequence of 1-step controllable sets and a condition that enables flexible, non-monotone convergence towards a suitably chosen terminal set. Such an off-line computed sequence of sets leads to a large region where the MPC algorithm is feasible, regardless of the length of the prediction horizon, while the non-monotone convergence condition is used to improve performance. Both stability and recursive feasibility are guaranteed by construction. The benefits of such an approach are shown in illustrative examples.

Current reference governor of permanent magnet synchronous machine

This paper presents a control method for constrained control of a permanent magnet synchronous machine. The control system utilizes field oriented PI current control tuned using linear control theory to obtain good steady-state performance and a fast transient response. However, the linear control theory does not consider constraints, violation of which may lead to performance deterioration and even instability. In order to ensure constraint satisfaction an additional device called a reference governor is added to the existing control loop. The main advantage of such add-on device is in its simplicity and its low computational burden which enables real time implementation. The real time feasibility of the proposed method is shown using a processor-in-the loop simulation implemented on Texas Instruments F28335 150 MHz microcontroller.

Mechatronic design and control of rotary flexible joint

The model of rotational mechanical system is laboratory model intended to emulate a flexible joint on a robot or spacecraft and is also useful in the study of vibration analysis and resonance. This system is similar in nature to the control problems encountered in large geared robot joints where flexibility is exhibited in the gearbox. The aim of this paper is to design such mechatronic system with considerable elasticity, for educational purposes, using computer aided design (CAD). Control algorithm is proposed for designed mechanical system in order to reduce effects of flexibility.

Constrained field-oriented control of permanent magnet synchronous machine with field-weakening utilizing a reference governor

ABSTRACT This paper presents a complete solution for constrained control of a permanent magnet synchronous machine. It utilizes field-oriented control with proportional-integral current controllers tuned to obtain a fast transient response and zero steady-state error. To ensure constraint satisfaction in the steady state, a novel field-weakening algorithm which is robust to flux linkage uncertainty is introduced. Field weakening problem is formulated as an optimization problem which is solved online using projected fast gradient method. To ensure constraint satisfaction during current transients, an additional device called current reference governor is added to the existing control loops. The constraint satisfaction is achieved by altering the reference signal. The reference governor is formulated as a simple optimization problem whose objective is to minimize the difference between the true reference and a modified one. The proposed method is implemented on Texas instruments F28343 200 MHz microcontroller and experimentally verified on a surface mounted permanent magnet synchronous machine.

Self-learning model predictive control based on the sequence of controllable sets

This paper presents a stabilizing model predictive control (MPC) algorithm based on the off-line computation of sequence of 1-step controllable sets for linear parameter varying systems and a parameter learning technique that improves its performance. The presented MPC algorithm guarantees non-monotone convergence towards a suitably chosen terminal set regardless of the system parameters, while the parameter learning technique is used to improve performance. The benefits of such an approach are shown in application of the proposed algorithm on a laboratory rotary inverted pendulum system.

Flexible Lyapunov function based model predictive direct current control of permanent magnet synchronous generator

In this paper a dual-mode model predictive direct current control (MP-DCC) algorithm for a permanent magnet synchronous generator (PMSG), which minimizes switching losses in a two-level synchronous generator side converter (SGSC), is proposed. The algorithm consists of two different modes, namely tracking mode where the control objective is to steer the stator currents to a control invariant set where they are ultimately bounded and a minimization of switching losses mode once inside this set. The size of the control invariant set can be arbitrarily chosen within bounds defined with PMSG and SGSC parameters, therefore switching losses can be more or less penalized in the steady state. In order to guarantee recursive feasibility and stability of the proposed algorithm, regardless of a cost function, a flexible control Lyapunov function (CLF) is employed as an optimization problem constraint which enables penalizing switching losses even in the transient state. In that way a desired trade-off between low stator current ripple and a minimization of switching losses can be achieved by properly choosing the objective function for the corresponding optimization problem.

Reference governor in control of a tower crane based on control invariant sets

The constrained control of a crane system is often based on control algorithms that utilize control invariant sets to guarantee the stability and constraint satisfaction. This paper presents a reference governor as a way of enlarging the region of attraction for such algorithms under the assumption that the crane is modeled as a series of dynamically coupled subsystems which are controlled separately. The proposed reference governor is based on the fact that dynamics of each subsystem is invariant subject to change in position. Therefore the idea is to enlarge the region of attraction by calculating the sequence of piece-wise constant reference signals. The proposed approach is verified through simulation and experimental test on the laboratory model using controllers based on control invariant sets.