Model-based Control Strategy Research Articles

Identification of physically consistent dynamics parameter of the ABB IRB 360-6/1600 delta robot and its use for time-optimal motion planning under consideration of constraint forces

Model-based control schemes, forward dynamics simulations, constraint force computation and time-optimal motion planning have one major thing in common, they all depend on the dynamics parameters of the system. Physical consistency of the dynamics parameters ensures a positive definite mass matrix and correct constraint forces. The most common inverse dynamics identification method – the base-parameters – lack physical consistency. This paper proposes an identification method to identify physically consistent dynamics parameters for Delta-like robots while further showing the effects of friction in passive joints. A tailored model to compute the crucial constraint forces appearing in the mechanism based on the identified dynamics parameters is derived. This model is used to additionally consider constraint forces besides actuation torques for time-optimal motion planning of a typical pick and place task. This is done without any prior CAD data of the robot from the manufacturer.

Dynamic modelling and a model-based control strategy of joint servo system for power transmission line inspection robot

Abstract The time-varying load inertia is one of the important dynamic characteristics of the power transmission line inspection robots (PTLIRs) in joint space, which can heavily affect the stability of the robot in the inspection process. In this paper, with considering the time-varying characteristics of load inertia, a method for modelling and control of the flexible joint servo system of the PTLIR is studied. First, the control system of the PTLIR is established based on the two inertia system. In order to calculate the load inertia, a dynamic model of the inspection robot is established. Then, the model-based notch filter is designed to improve dynamic performance of the robot, and suppress the vibration amplitude change which caused by the time-varying load inertia. Finally, the comparison of the control performance between the traditional control strategy and the model-based control strategy with notch filter are compared. The simulation results show that the paper can provide an effective tool for modelling and improving the stability of the PTLIR.

Magnetic equivalent circuit modeling of a permanent magnet linear synchronous motor composed of curved segments

This paper advances magnetic equivalent circuit (MEC) modeling for permanent magnet linear synchronous motors (PMLSMs) with arbitrarily curved segments. As PMLSMs are increasingly utilized in industrial applications, accurate and computationally efficient modeling techniques are paramount. This research proposes a novel approach that meets these requirements and paves the way for real-time model-based control, observers, and estimation strategies. Existing MEC models for PMLSMs primarily consider straight stator segments, neglecting the impact of the curvature of curved segments commonly used in modern PMLSM designs. The approach proposed in this paper systematically incorporates these effects into the MEC model for PMLSMs to address this limitation. As demonstrated by several validation experiments, a calibration concept based on measurements further enhances the model’s accuracy. Finally, a method for efficiently implementing the proposed MEC model for a whole PMLSM setup significantly reduces the computation time compared to the standard implementation.

Rapid sloshing-free transport of liquids in arbitrarily shaped containers

AbstractWe present a model-based feedforward control strategy suitable for designing swift rest-to-rest maneuvers for liquids in arbitrarily shaped containers. We employ the commonly used equivalent pendulum model to represent the sloshing dynamics and suggest a novel parameter identification scheme suitable for arbitrary container shapes and any number of sloshing modes. By computing natural modes and fluid reaction forces and torques for imposed harmonic container motions via a finite element model, we obtain data for the identification scheme. A fitting procedure then yields highly accurate parameters for a physical pendulum model, where each pendulum represents one sloshing mode. We also provide a thorough analysis of parameter identifiability and guidelines for obtaining robust parameter estimates. The proposed feedforward control method uses a virtual tray pendulum on which we place the container (in the form of its equivalent pendulum model). Designing the virtual tray such that the fluid’s dominant sloshing mode cannot be excited by horizontally moving the tray pendulum pivot effectively zeros out any sloshing motion in this mode. We then exploit the flatness property of the resulting system to design rest-to-rest maneuvers where any residual sloshing motion (in higher modes) can be exactly stopped at the end of the maneuver. The effectiveness of the proposed method is demonstrated through extensive simulations and experimental results using a Martini cocktail glass, whose shape is challenging in terms of sloshing. The experimental results show the successful, accurate suppression of sloshing, validating the efficacy of the proposed concept.

Model-based sparse optimal control of the hydrogen sulfide synthesis process for acidic wastewater sulfidation

Hydrogen sulfide is one of the commonly used materials of the gas-liquid sulfidation process for treating nonferrous metallurgical acidic wastewater. Using hydrogen and sulfur to synthesize hydrogen sulfide can obtain hydrogen sulfide with higher purity and at a lower cost. Due to the complex multiphase reaction mechanism of the hydrogen sulfide synthesis (HSS) process and production safety conditions, the manual operation requires a long time to ensure the continued stability of the system under high temperatures and high pressure. In this paper, a dynamic model and a novel sparse control strategy of the HSS process for acidic wastewater sulfidation are proposed to enable the HSS process operates continuously and safely. Firstly, a multivariate and multiphase mechanism model with multiple inputs and outputs of the HSS process is established. Subsequently, the novel sparse feedback matrix is designed under the constraint of the fixed l0-norm and the solvability of the eigenvalue determinant equation. The sparse feedback matrix solutions are optimized by pole placement and dynamic programming for continuous and discrete systems, respectively. Finally, the effectiveness of the HSS model is validated with industrial data. The superiority of the proposed model-based control strategy is demonstrated in the HSS system compared with conventional control methods.

Model Predictive Control (MPC) of a Countercurrent Flow Plate Heat Exchanger in a Virtual Environment.

This research proposes advanced model-based control strategies for a countercurrent flow plate heat exchanger in a virtual environment. A virtual environment with visual and auditory effects is designed, which requires a mathematical model describing the real dynamics of the process; this allows parallel fluid movement in different directions with hot and cold temperatures at the outlet, incorporating control monitoring interfaces as communication links between the virtual heat exchanger and control applications. A multivariable and non-linear process like the plate and countercurrent flow heat exchanger requires analysis in the controller design; therefore, this work proposes and compares two control strategies to identify the best-performing one. The first controller is based on the inverse model of the plant, with linear algebra techniques and numerical methods; the second controller is a model predictive control (MPC), which presents optimal control actions that minimize the steady-state errors and aggressive variations in the actuators, respecting the temperature constraints and the operating limits, incorporating a predictive model of the plant. The controllers are tested for different setpoint changes and disturbances, determining that they are not overshot and that the MPC controller has the shortest settling time and lowest steady-state error.

Moral and formal model-based control strategy for autonomous vehicles at traffic-light-free intersections

Making an effective traffic strategy is a challenging problem that involves efficiency and safety, where traffic lights play an important role. Especially in cases without traffic lights, creating traffic strategies benefits significantly more complex and challenging. This complexity underscores the importance of research on autonomous vehicles, which has been trained widespread attention in recent years. In order to avoid these potential crises, the designed traffic strategy at traffic light-free intersections must be effective and reliable, ensuring the basic processes work properly. In this paper, we propose a novel method to formally model the traffic strategy for vehicles to make sure they safely and orderly pass through traffic light-free intersections. We use the state space analysis method to analyze the vehicle traffic model to ensure the decision made at each step is correct. By using Petri net, our proposed model achieves excellent analytical capabilities for asynchronous or highly concurrent systems than simpler Unified Modeling Language (UML) systems, as well as the ability to dynamically analyze system security and reliability.

Model-based control strategy with linear parameter-varying state-space model for rack-based cooling data centers

Cooling system energy consumption occupies approximately 40% of the total energy consumption in data centers, and efficient control is crucial for improving cooling efficiency and reducing the operational costs of data centers. This paper introduced a novel economic model predictive control (EMPC) strategy based on a linear parameter-varying state-space model. The primary objective was to maintain the thermal environment of data centers while minimizing the energy consumption of the cooling system. Therein, a linear parameter-varying state-space model was developed based on the parameter identification method to precisely predict the temperature field of the rack-based cooling data center in real-time. The energy management and thermal regulation performance of the EMPC and the proportion integration differentiation (PID) controller were comparatively analyzed through simulation experiments. Moreover, the impacts of the server workload level and optimization time domain on the performance of EMPC were comprehensively investigated. The results show that, the proposed EMPC was able to minimize the energy consumption by optimizing the supplied cold air temperature and airflow rate simultaneously. In comparison with the PID controller, EMPC not only achieves a 9.7% energy saving by preventing server over-cooling but also diminishes server temperature fluctuations, promoting the safe and stable operation of the server. The research shows that the EMPC reveals excellent performance along with all server workload levels. In addition, the length of the prediction horizon has a notable impact and three minutes is suggested for the rack-based cooling system.

Deep Reinforcement Learning for sim-to-real policy transfer of VTOL-UAVs offshore docking operations

This paper proposes a novel Reinforcement Learning (RL) approach for sim-to-real policy transfer of Vertical Take-Off and Landing Unmanned Aerial Vehicle (VTOL-UAV). The proposed approach is designed for VTOL-UAV landing on offshore docking stations in maritime operations. VTOL-UAVs in maritime operations encounter limitations in their operational range, primarily stemming from constraints imposed by their battery capacity. The concept of autonomous landing on a charging platform presents an intriguing prospect for mitigating these limitations by facilitating battery charging and data transfer. However, current Deep Reinforcement Learning (DRL) methods exhibit drawbacks, including lengthy training times, and modest success rates. In this paper, we tackle these concerns comprehensively by decomposing the landing procedure into a sequence of more manageable but analogous tasks in terms of an approach phase and a landing phase. The proposed architecture utilizes a model-based control scheme for the approach phase, where the VTOL-UAV is approaching the offshore docking station. In the Landing phase, DRL agents were trained offline to learn the optimal policy to dock on the offshore station. The Joint North Sea Wave Project (JONSWAP) spectrum model has been employed to create a wave model for each episode, enhancing policy generalization for sim2real transfer. A set of DRL algorithms have been tested through numerical simulations including value-based agents and policy-based agents such as Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) respectively. The numerical experiments show that the PPO agent can learn complicated and efficient policies to land in uncertain environments, which in turn enhances the likelihood of successful sim-to-real transfer.

Neural network survivability approach of a wave energy converter considering uncertainties in the prediction of future knowledge

To tune the wave energy converter (WEC) controller parameters such as damping to reduce the line force during extreme wave conditions, future knowledge of the line force is required. To achieve this, the incoming wave and system state should be predicted for a few seconds in the future. It is rather an arduous task to predict the future knowledge of waves and the system’s dynamic when dealing with breaking and steep waves, and the system is subject to various nonlinear forces. The classical model-based control strategies often rely on linear assumptions to estimate the WEC dynamics for the sake of simplicity. Unlike the model-based, the data-driven approaches are free from modeling errors and the algorithms are trained over the true and noisy data to predict non-linear system behaviors. Using data-driven approaches, we are able to model nonlinear dynamics. However, new questions emerge on the accuracy of the future wave and system state predictions, and how this uncertainty propagates into the final prediction of the line force. As incorrect damping may lead to excessive line force and detrimental damage to the system, these are the knowledge gaps that need to be addressed. The main purpose of this paper is to answer these questions through a survivability strategy for wave energy converters by providing a realistic perspective on the implementation of the neural network approaches by accounting for the errors in the input data. For this purpose, a series of neural networks is designed that first predicts the surface elevation for 0.36 s ahead, i.e. corresponding to 2 s in the full-scale WEC. This future knowledge of the wave elevation is then used to predict the system state (i.e. power take-off (PTO) translator position) for the same prediction horizon based on the PTO damping. This information is then fed to a convolutional neural network (CNN) that predicts the peak line force 0.36 s ahead. This paper also evaluates the predictive capabilities of neural network (NN) models, comparing them to classical autoregressive (AR) and Kalman filter methods. While the AR model exhibits slightly higher accuracy in fixed PTO system configurations, it falters in providing real-time predictions when system parameters undergo variations. In contrast, the NN prediction model excels by establishing a robust relationship between system configuration and output signals. Especially noteworthy is its ability to outperform classical methods with minimal training on a selective set of system configurations. Then, the sensitivity of the peak line force prediction to the uncertainties in the input data from NN models and the prediction horizon is analyzed. The neural network models are trained over the experimental data subjected to extreme sea states for a point absorber wave energy converter. The results suggest that the accuracy of the surface elevation prediction has an insignificant direct effect on the peak force prediction model. However, these uncertainties are reflected in the PTO translator position prediction, and the model is considerably sensitive to the accuracy of this prediction. This sensitivity nonetheless is less notable for higher PTO damping values.

Error field detection and correction studies towards ITER operation

In magnetic fusion devices, error field (EF) sources, spurious magnetic field perturbations, need to be identified and corrected for safe and stable (disruption-free) tokamak operation. Within Work Package Tokamak Exploitation RT04, a series of studies have been carried out to test the portability of the novel non-disruptive method, designed and tested in DIII-D (Paz-Soldan et al 2022 Nucl. Fusion 62 126007), and to perform an assessment of model-based EF control strategies towards their applicability in ITER. In this paper, the lessons learned, the physical mechanism behind the magnetic island healing, which relies on enhanced viscous torque that acts against the static electro-magnetic torque, and the main control achievements are reported, together with the first design of the asynchronous EF correction current/density controller for ITER.

A model-based control strategy for turbo-shaft engine

This paper takes the turbo-shaft engine (the power source of the helicopter) as the research object, and mainly conducts the simulation of its related control law and control structure. The working principle of the turbo-shaft engine is briefly introduced initially, and then the component-level model of the turbo-shaft engine is established. This paper focuses on the design of the control law upon the turbo-shaft engine, and verifies the designed control law through the results of simulation by MATLAB/Simulink. Finally, the relevant data collected from 240KW hybrid system is taken into the consideration to verify the control algorithm. The results show that the control accuracy of the corresponding control algorithm in this paper is high (accuracy≤5%).

RETROFIT: Real-Time Control of Time-Dependent 3D Point Cloud Profiles

Abstract In modern manufacturing processes, ensuring the precision of 3D profiles of products is crucial. Nonetheless, achieving this accuracy is challenging due to the complex interactions between process inputs and the data structure of the 3D profile data. Our solution, a 3D profile-based control framework, addresses this challenge by actively adapting and controlling the manufacturing process to enhance 3D shape accuracy. 3D profile scans represent the ultimate measure of desired part quality. Therefore, utilizing them as the system responses for control purposes yields the most direct and effective feedback. We leverage recent advancements from Koopman operator theory to create an effective model-based control strategy. Initially, we estimate the process model by exploring the relationship between 3D profiles and heterogeneous process inputs. Then, we formulate an online model predictive control law. Challenges include dealing with unstructured, high-dimensional 3D point cloud data, capturing spatial and temporal structures, and integrating heterogeneous, high-dimensional process input data into the control model. To overcome these challenges, we introduce RETROFIT, a solution designed for the real-time control of time-dependent 3D point cloud profiles. Unlike traditional models, RETROFIT is not bound by linear assumptions and can handle unstructured 3D point cloud data directly. We demonstrate its effectiveness through a wire arc additive manufacturing case study, highlighting its potential to enhance 3D profile accuracy in manufacturing processes.

Design of novel differential power processing scheme for small-scale PV array application operating under partial shading conditions: modelling and experimental validation

In this study, an innovative approach known as Series and Parallel Differential Power Processing (SP-DPP) converters has been introduced and applied to a small-scale 2 × 2 PV array configuration. Unlike conventional methods, this approach employs Bidirectional Ćuk DC-DC Converters (BCC) in lieu of bypass diodes within serially connected PV modules, facilitating precise adjustment of module currents. Additionally, the Differential Power Processing (DPP) mechanism, utilising an Inverted-Buck Converter (IBC) topology, replaces traditional blocking diodes across parallel string connections. The paper develops mathematical models for these converters based on their transfer functions in the Continuous Conduction Mode (CCM), establishing new design criteria for SP-DPP systems. This approach ensures a well-behaved transient response and mitigates non-minimum phase response effects. Empirical testing demonstrates the efficacy of the proposed system under various static and dynamic partial shading conditions (PSCs). Furthermore, a model-based control strategy employing P + I controllers are formulated and experimentally validated to optimise the operating conditions of PV modules, enabling them to operate at their Maximum Power Points (MPPs) in varying lighting conditions. Through simulation and experimental results, the study affirms a substantial increase in the total output power of the SP-DPP system (approximately 37–41 %) compared to conventional Tied-Crossties (TCT) array configuration relying solely on bypass and blocking diodes under various static shading conditions, such as Short-Narrow (SN), Short-Wide (SW), Long-Narrow (LN) and Long-Wide (LW). Ultimately, this research highlights the novelty of the proposed SP-DPP scheme over other existing DPP schemes in the literature, in terms of maximum power extraction, cost and complexity under various lighting conditions. This research extends to explore the potential economic viability and global implications of large-scale DPP system implementation.

Hybrid cooperative Markov-based method for decentralized voltage control and loss reduction via photovoltaic reactive power support

The problem of carrying out effective voltage control in distribution grids with significant photovoltaic penetration has received considerable attention in the recent literature. Hence, a variety of methods that leverage PV inverter reactive support capabilities have been proposed for enhancing the grid voltage profile. Despite its great relevance for the utility, active power loss is, quite notably, an often-neglected parameter in such methods. This work proposes a decentralized voltage control method that hybridizes a novel voltage-oriented Cooperative Partially Observable Markov Decision Process model-based control scheme with a heuristic loss minimization approach. To further improve implementation feasibility, a pilot node approach is adopted to drastically reduce the required number of measurements per voltage control zone. The proposed method, named Voltage Control and Loss Reduction Based Cooperative Partially Observable Markov Decision Process, is validated by means of simulation on a 123-bus test system. Results show that, compared to similar methods, it yields a favorable balance between voltage control and loss reduction without the need of real-time multi-objective optimization.

An identification algorithm of switched Box-Jenkins systems in the presence of bounded disturbances: An approach for approximating complex biological wastewater treatment models

This paper focuses on the development of linear Switched Box–Jenkins (SBJ) models for approximating complex dynamical models of biological wastewater treatment processes. We discuss the adaptation of these processes to the SBJ framework, showing the model's generality and flexibility as a class of switched systems that can offer accurate predictions for complex and nonlinear dynamics. This approach of modeling enables real-time data reconciliation from experiments and allows the design of model-based control strategies. Through the extension of the Outer Bounding Ellipsoids (OBEs) algorithm, the paper introduces an online two-stage parameter identification algorithm that effectively handles bounded disturbances for SBJ models. Using the OBE method relaxes the stochastic assumptions on disturbances, which may not be satisfied in practice, particularly for biological and environmental fluctuations. The proposed decomposed OBE algorithm separately identifies the switching patterns and parameters of linear submodels, conducting parameter identification in two distinct phases for input/output and disturbance/output submodels. The efficacy of this approach is shown via simulation results validated against both ADM1 and PBM models, demonstrating the proposed algorithm's capability to accurately predict outputs from different biological wastewater treatment models.

Model-Based Predictive Control of a Solar Reactor

The present paper deals with the modelling and control of a solar reactor designed to produce syngas, by exploiting concentrated solar power. A model of the reactor based on the thermodynamic equilibrium is developed. Two model-based predictive control strategies are proposed: the first strategy (MPC strategy 1) aims to maintain the reactor's temperature at its nominal value whereas the second strategy (MPC strategy 2) aims to maintain the reactor's temperature at its nominal value, while maximizing the use of solar energy. Finally, these strategies are compared to a reference strategy, which is based on a combination of a rule-based controller and an adaptive PID controller with optimized gains. The robustness of the MPC controller to forecast errors is also studied by testing different DNI forecasting models. Parts of this paper were published as journal articleKarout, Y.; Curcio, A.; Eynard, J.; Thil, S.; Rodat, S.; Abanades, S.; Vuillerme, V.; Grieu, S. Model-Based Predictive Control of a Solar Hybrid Thermochemical Reactor for High-Temperature Steam Gasification of Biomass. Clean Technol. 2023, 5, 329-351. https://doi.org/10.3390/cleantechnol5010018

Model-based optimal control strategy for multizone VAV air-conditioning systems for neutralizing room pressure and minimizing fan energy consumption

Control strategies for variable air volume (VAV) air-conditioning systems play a pivotal role in ensuring indoor environmental quality and energy efficiency. However, conventional approaches, such as static pressure reset (SPR) control, focus on managing indoor air temperature without considering the room pressure, which can lead to unbalanced room pressure and undesirable air leakage. Moreover, with the application of prevalent building pressure control strategies, such as airflow tracking control, to multizone VAV systems, neutralization of the room pressure is difficult across multiple zones in VAV systems. Therefore, this study introduces a model-based optimal control strategy for multizone VAV air-conditioning systems. The proposed strategy uses a multiobjective optimization framework to regulate fan frequencies and damper openings on both the supply and return sides. This holistic approach facilitates the simultaneous control of the indoor air temperature and room pressure while minimizing fan energy consumption. To assess the effectiveness of the proposed strategy, four control strategies were tested using a Python-based simulation testbed. The results demonstrate that the proposed strategy effectively maintains the indoor air temperature, neutralizes room pressure, and reduces fan energy consumption, thereby contributing to the overall efficiency of the VAV system. Moreover, the results highlight the limitations associated with combining airflow tracking control with SPR control for room pressure regulation in multizone VAV systems. This highlights the importance of adopting a model-based approach to address the complexities of concurrent room pressure and indoor air temperature control.

Reinforcement learning for optimal control of linear impulsive systems with periodic impulses

This paper focuses on the finite- and infinite-horizon optimal control problems of linear impulsive systems with periodic impulses under the quadratic performance index. Necessary and sufficient conditions for the optimal impulsive system are derived in terms of hybrid Riccati equations by utilizing the variational method and the collocation method combined with a time-varying Lyapunov function. Different from the existing model-based impulsive control schemes, three reinforcement learning (RL)-based algorithms are proposed to solve the optimal impulsive controller and hybrid controller for the impulsive system without the exact knowledge of the system dynamics. The asymptotical stability of the impulsive control system and the convergence of the RL-based algorithms are proved rigorously. Finally, a numerical simulation illustrates the effectiveness of the proposed control methods.

Model-based adaptive security control strategy against false data injection attacks in cyber–physical systems

This paper addresses the problem of adaptive security control for cyber–physical systems (CPSs) under external nonlinear disturbances and false data injection (FDI) attacks on actuators. To address FDI attacks, a full-order disturbance estimator is proposed for eliminating disturbances in the forward channel. Furthermore, an attack model based on the system state vector is developed to estimate the attack vector by considering potential attacks that can induce state errors between the reference model and the actual system. Subsequently, a novel model-based adaptive security control (MASC) strategy is devised using attack compensation and state feedback. The key advantage of our proposed strategy lies in its ability to effectively mitigate the adverse effects of attacks while accounting for nonlinear disturbances. Finally, experimental validation conducted on a brushless DC motor speed control system demonstrates the capability of achieving high output tracking accuracy.