Adaptive Model-based Predictive Control Research Articles

Enhanced Efficiency on ANPC-DAB through Adaptive Model Predictive Control

This work studies the DC-DC conversion stage in solid-state transformers (SST). The traditional two- or three-level dual active bridge (DAB) topology faces limitations in microgrid interconnection due to power and voltage limitations. For this reason, the use of multilevel topologies such as active neutral point clamped (ANPC) is a promising alternative. Additionally, the efficiency of the SSTs is a recurring concern, and reducing losses in the DC-DC stage is a subject to be studied. In this context, this work presents a new control technique based on an adaptive model- based predictive control (AMPC) to select the modulation technique of an ANPC-DAB DC-DC converter aimed at reducing losses and increasing efficiency. The single-phase shift (SPS), triangular, and trapezoidal modulation techniques are used according to the converter output power with the aim of maximizing the number of soft-switching points per cycle. The performance of the proposed control technique is demonstrated through real-time simulation and a reduced-scale experimental setup. The findings indicate the effectiveness of the AMPC control technique in mitigating voltage source perturbations. This technique has low output impedance and is robust to converter parameter variations. Prototyping tests revealed that, in steady-state, the AMPC significantly improves converter efficiency without compromising dynamic performance. Despite its advantages, the computational cost of AMPC is not significantly higher than that of traditional model predictive control (MPC), allowing for the allocation of time to other applications.

Adaptive model-based predictive control for cross-phase cavity pressure control in injection molding

Injection molding is a manufacturing process for the production of plastic parts. The traditional process control switches between an injection velocity control and a pressure control. If the switchover is not optimal, quality issues occur during production. Additionally, the setup of processes with the traditional process control requires a high setup effort. The objective of cross-phase process control strategies is to reduce both weaknesses by controlling only one process variable during molding. Disadvantages of available cross-phase control approaches are related to the use of multiple empirical models in the controller which have to be adapted manually to the used machine settings. This contribution proposes a novel adaptive cross-phase cavity pressure control concept based on only one nonlinear time-variant model for the entire process. The adaptive approach reduces the setup effort and compensates inaccuracies during modeling, thus avoiding the need to adjust the controller model when machine settings change. Herefore, the controller model is adapted to the time-varying process dynamics by use of an Unscented Kalman Filter which estimates all process states along with an additional lumped parameter. The time-variant model is then used in a Model-based Predictive Controller to track a given cavity pressure reference. For the adaptive concept to operate properly, the estimated parameter must be positive which is ensured by an introduced model tuning parameter (MTP) influencing the convergence behavior of the parameter estimation. The MTP allows not only to taylor the convergence behavior of the parameter estimation for safe operation and good performance but also to rapidly adapt the control topology for new processing conditions, e.g. different machines or part geometries. Tuning guidlines for the MTP are provided, resulting in a setup procedure of greatly reduced effort resulting in an easy-to-follow setup procedure. The controller concept is validated on two different industrial injection molding machines and for two different part geometries showing good reference tracking capabilities with a steady-state error ⩽4bar.

Model-based Predictive Control of an HVAC System

Abstract This paper presents the application of two model-based predictive control (MPC) algorithms on the cooling system of an office building. The two strategies discussed are a simple MPC, and an adaptive MPC algorithm connected to a model predictor. The cooling method used represents the air-conditioning unit of an HVAC system. The temperature of the building’s three rooms is controlled with fan coil units, based on the reference temperature and with different constraints applied. Furthermore, the building model is affected by dynamically changing interior and exterior heat sources, which we introduced into the controller as disturbances.

Affine Neural Network-Based Predictive Control Applied to a Distributed Solar Collector Field

This paper presents experimental results concerning the control of a distributed solar collector field, where the main objective concerns the regulation of the outlet oil temperature by suitably manipulating the oil flow rate. This is achieved by means of a constrained nonlinear adaptive model-based predictive control framework where the control action sequence is obtained by solving an open-loop optimization problem, subject to a set of constraints. The plant dynamics is approximated by an affine state-space neural network, whose complexity is specified in terms of the cardinality of dominant singular values associated with a subspace oblique projection of data-driven Hankel matrices. The neural network is first trained offline and subsequently improved through a recursive updating of its weights and biases, based on a dual unscented Kalman filter. The control scheme is implemented on the Acurex field of the Plataforma Solar de Almeria, Spain. Results from these experiments demonstrate the feasibility of the proposed framework, and highlight the ability to cope with time-varying and unmodeled dynamics, under the form of disturbances, and its inherent capability for accommodating actuation faults.

Modeling and Control of Temperature Field of the Secondary Cooling Zone in Continuous Casting of Steel as Distributed Parameter System

This paper presents some results on modeling and control of temperature field in a steel continuous casting, demonstrating the possibilities of using engineering methods for control of distributed parameter systems along with virtual engineering environments. A system to be controlled is considered as lumped-input and distributed-parameter-output system. Dynamics and control synthesis are decomposed into time and space components, respectively. Demonstrations of PID and adaptive model-based predictive control are being presented via cosimulation, utilizing virtual software environments for continuous casting simulation and distributed parameter control systems, respectively.

Data-driven adaptive model-based predictive control with application in wastewater systems

This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms.

Design of a waveform tracking system for a piezoelectric actuator

In this work an adaptive model-based predictive control (AMPC) is designed for high-frequency waveform tracking by a PZT bimorph actuator in industrial vibratory feeding. Since the actuator has a narrow bandwidth and significant dynamic hysteresis, the waveform tracking process contains both linear and non-linear errors and the coupling effect between these two needs to be considered before effective compensation can be implemented to obtain a precise vibration trajectory. In the adaptive control, two parallel loops were employed, a linear compensation loop and a non-linear offset loop, to suppress the hybrid errors and let the error eventually converge to an acceptable level by iteration. Since the system varies much more slowly than the control loops, a quasi-time invariant system is assumed in the modelling process and a single-layer neural network is used to compensate the system floating adaptively. Due to the fact that in this application the smoothness of the actuator motion waveform is of critical importance to ensure the mechanical force configuration required in industrial application, each driving waveform must be adapted as a whole, not as a series of discrete input points. Therefore instead of applying a set-point tracking method, a self-tuning process is used in the predictive filter design of the current cycle, and corresponding control is delayed to waveform generation of the next cycle. The control algorithm is then implemented into experiment for vibratory feeding and the result shows satisfactory hysteresis suppression and good waveform tracking.

ADAPTIVE MODELLING AND PREDICTIVE CONTROL OF AN IC ENGINE

This paper presents modelling of internal combustion (IC) engine with adaptive neural networks. A radial basis function network model with both centres and weights adapted and a model with only weights adapted are compared with a fixed parameter model. The developed models are used in model based predictive control (MPC) to form an adaptive nonlinear MPC scheme and applied to engine speed tracking control. The modelling and control are based on a generic mean value engine model and consists of three submodels that describe the fuel mass flow dynamics, the intake manifold filling dynamics and the crankshaft speed. Adaptive MPC is shown superior over the fixed parameter model based control.