Model Algorithmic Control Research Articles

Effect of Variable-Nozzle-Turbocharger-Coupled Exhaust Gas Recirculation on Natural Gas Engine Emissions and Collaborative Optimization

Equivalent combustion natural gas engines typically utilize exhaust gas recirculation (EGR) systems to tackle their high thermal burden and NOx emissions. Variable nozzle turbochargers (VNT) can increase the engine intake and EGR rate simultaneously, resulting in NOx reduction while ensuring robust power performance. Using a VNT along with engine bench testing, the impact of VNT- and EGR-coordinated control on the performance and emissions of equivalent combustion natural gas engines was investigated under different operating conditions. Subsequently, multi-objective optimization was performed using a support vector machine. The results demonstrated that the use of VNTs in equivalent combustion natural gas engines could bolster the capacity to introduce EGR under several operative conditions and extend the scope of EGR regulation, thereby decreasing the engine’s thermal burden, improving fuel efficiency, and curbing emissions. Owing to the implementation of a multi-objective optimization method based on a support vector regression model and NSGA-II genetic algorithm, VNT and EGR control parameters could be optimized to slightly improve the economy and significantly reduce NOx emissions while maintaining the original engine power performance. At 20 operating points optimized for validation, brake-specific fuel consumption (BSFC) and NOx decreased by 0.94% and 47.0%, respectively, and CH4 increased by 3.7%, on average.

Vibration control of an eleven-story structure with MR and TMD dampers using MAC predictive control, considering nonlinear behavior and time delay in the control system

In the last two decades, model predictive control has made significant progress in research and industrial process control. This achievement is through the high ability of model predictive control to solve industrial process control problems in the time domain. It is applicable in many control systems, such as delayed systems, univariate and multivariable systems, non-minimum phase systems, and unstable systems. This strategy is essentially for controlling delayed systems. It is possible to create a time delay in transmitting structure movement data in the systems that use sensors and receivers of sensor-recorded data, which is a factor to reduce the efficiency of the control system and even the instability of the structure. In this study, to investigate and compensate the effect of time delay in the hybrid vibration control system, an 11-story structure with a tuned mass damper and an MR damper was used by model algorithmic control (MAC) and nonlinear behavior of the structure in the control process. The linear and nonlinear structural models are created in OpenSEES, the control system in MATLAB; and the TCP/IP connection method was used to connect two programs. The fuzzy decision system is based on accelerating and decelerating movement. The structure has been subjected to seven earthquakes with maximum accelerations of 0.1 g to 1.0 g with an incremental step of 0.1 g. According to the results of incremental dynamic analysis, the average percentage of the fuzzy control system performance reduction, considering the time delay, is 15.28% and 38.57% for the maximum displacement and base shear in the linear structure, and 8.04% and 5.97% for the nonlinear structure, respectively. The assumed time delays have been effectively overcome using the model algorithmic control system (MAC), determining the prediction horizon of the structural behavior, and optimizing the control force in the control horizon. The performance of the MAC control system has been better than the fuzzy control system and the passive control with TMD. The average improvement percentage of the results of the maximum linear and nonlinear structural displacement response is respectively equal to 10.2 and 11.8 with the MAC control system, compared to the fuzzy control system without time delay. For the maximum base shear, it is equal to 8.32 and 2.49, respectively.

Non-Conservative Trajectory Planning for Automated Vehicles by Estimating Intentions of Dynamic Obstacles

Motion planning algorithms for urban automated driving must handle uncertainty due to unknown intention and future motion of Dynamic Obstacles (DOs). Considering a single future trajectory for each DO is not adequate, especially in urban frameworks where traffic participants exhibit very different behaviors. However, including multiple candidate trajectories representing different behaviors results in an excess of conservatism. We present a novel combination of the Interactive Multiple Model (IMM) algorithm and Stochastic Model Predictive Control (SMPC) that allows non-conservative safe motion planning in presence of DOs with unknown intention. We introduce a framework based on LQR and IMM to predict multiple candidate future trajectories, each interpreted as a high-level intention that the DO is pursuing, and dynamically estimate their probabilities. Then, the future trajectory of the automated vehicle is iteratively planned in an SMPC fashion, in which collision avoidance constraints are generated for multiple future trajectories of each DO, with a focus on the most likely. Our method improves safe motion planning fully exploiting the benefits of multi-modal predictions of the DOs, avoiding excessive conservatism. Advantages of the proposed method are discussed through simulations in the CARLA environment.

Modeling the Automating Process of the Creation, Checking and Updating Texts of Contracts

Purpose of research. Is to increase the degree of automation of contractual relations legal support of the parties as in the case of publishing activities through the development of model-algorithmic control and software for the automated creation and updating of texts of publisher-author license agreements.Methods. When analyzing the processes of automating legal activities in relation to the creation, development and updating texts of contracts, Russian and foreign technologies and software that allow automating the processes of contractual activities have been considered. Conceptual and set-theoretics models of the process of automated building texts of contracts have been proposed.Results. An original classification of tasks and ways of automating the legal activities including a list of promising information technologies have been proposed. Conceptual and set-theoretics models of the process of automated building texts of contracts have been developed; they differ in the use of a set of public acts, information resources, and expert information available as sources of information to create the necessary document in the selected subject area based on the algorithm of data verification, generation, visualization, contract updating, which provide contract creation and updating in an automated mode. An algorithmic model for automated processing of contract parameters which differs in the distribution of tasks between experts (a lawyer, a programmer) and software tools at has been developed. The main stages of the document life cycle involve creating, updating, deleting a contract, which, based on the event model, checks for updates in public acts and automatically corrects the text values of the parameter of the contract. The proposed solutions have been tested in case of a license agreement between an author of the article and the publishing house issuing Informatics and Automation journal.Conclusion. The development of model-algorithmic control and software for processing text information available from public acts, information resources provided by representatives of subject areas necessary for the automated generation, verification, and visualization of individual acts contributes to an increase in the degree of automation, reduces the time of preparation of legal documents that meet the requirements of existing public acts. Means of automating contractual activities should be built on the principle of openness, be dynamic, and also be externally controlled. The software services of the St. Petersburg Federal Research Center of the Russian Academy of Sciences for the automation of legal activities developed within the framework of this study are available at http://legaltech.viwo.ru/.

A wind power smoothing strategy based on two-layer model algorithm control

The inherent uncertainty of wind power generation leads to a mismatch between actual wind power generation and electric grid demand power, while the occurrence of sudden loads makes real-time scheduling of wind farms difficult. To address these issues, a cooperative compensation strategy of battery is proposed to compensate for the unbalanced power and sudden loads. Positive unbalanced power is used directly to compensate for sudden loads, and the energy storage absorbs the remaining positive unbalanced power to indirectly compensate for negative unbalanced power and sudden loads when the energy storage has excess power. The fluctuation rate of wind power after compensation is considered, and a smoothing model based on two-layer model algorithm control (MAC) is established. A variable mode decomposition (VMD) adaptive frequency division method is proposed to decompose the compensated power in order to smooth the high-frequency and low-frequency parts of the compensated power using different components of the hybrid energy storage to achieve accurate compensation.

Geolocation accuracy improvement for NovaSAR-1 imagery acquired through TLE orbit

NovaSAR-1 is a small S-band Synthetic Aperture Radar (SAR) mission mainly for land and maritime applications. Since SAR has side-looking geometry, thus slant and ground range SAR products have geometric distortions. To correct these geometric distortions, orthorectification needs to be done using SAR Rigorous Sensor Model (RSM) algorithm and Digital Elevation Model (DEM). NovaSAR-1 provides the satellite state vectors either through a Global Positioning System (GPS) or through a Two-Line Element (TLE) orbit source. It is observed that, after orthorectification, it shows poor geolocation accuracy (200 m to 9000 m) for the TLE orbit source. In this paper, geolocation accuracy improvement has been done for NovaSAR-1 ground range imagery acquired through TLE orbit source by generating orthorectified products using SAR Rigorous Sensor Model (RSM) algorithm and Ground control point (GCP). After orthorectification, geolocation accuracy of the NovaSAR-1 imagery acquired through TLE orbit source is reduced to less than 10 m.

An Output Probabilistic Constrained Optimal Control Algorithm Based on Multivariable MAC and its Application in Looper Control System

Most actual industrial processes are multivariable and constrained complex systems. The state and output of the system also have uncertainty due to the existence of random disturbances. The output of the system is easier to be measured than the state and can more intuitively reflect the running state of the system. Considering the limitations of industrial equipment and the benefits of production, it is generally allowed to have a small portion of the output that can exceed the constraints. As a result, the outputs can be represented as corresponding single probabilistic constraints. In this paper, therefore, an output probabilistic constrained optimal control algorithm based on multivariable model algorithm control (MMAC) is proposed. First, the feedback correction link of the MMAC algorithm is improved, and the predicted outputs are corrected by taking the weighted average of the errors. Then, assuming that the disturbances obey Gaussian distribution, the output probabilistic constraints are transformed into deterministic ones. Next, the optimal control problem is solved as a quadratic programming (QP) problem after combining them with the performance index function of the MMAC. Finally, the proposed algorithm is applied to the looper control system in hot strip rolling process and compared with the single MMAC algorithm to verify its effectiveness.

Generic Model Control for Lithium-Ion Batteries

Safety is of prime importance while using Lithium ion batteries. An efficient Battery Management System (BMS) should be able to keep the battery safe at all times, which require the accurate estimation of the internal states such as State of Charge (SOC) and State of Health (SOH) of the battery [1]. Cost and safety related issues also call for intelligent charging of batteries, which could enable full utilization of the batteries. The models for lithium-ion batteries vary in their computational complexity, accuracy and internal state predictions. While physics based models [2, 3] give key insights inside the battery, and could be used for estimating the internal states, these can’t easily be directly employed for online control because of their high computational requirements. To meet these requirements, various approaches are continuously attempted by different researchers. Multivariable control techniques have already been shown for a variety of systems. Dynamic matrix control (DMC) [4], internal model control (IMC) [5] and model algorithmic control (MAC) [6] have already been illustrated in the past. These models rely on the linear approximations of the experimentally obtained step response data. Physics based battery models are highly non-linear in nature and controlling them efficiently would ideally require the battery models to be imbedded directly in the controller. This presents challenges in convergence and efficiency of the algorithms. In our past work, we explored the use of simultaneous optimization [7, 10] and control vector parameterization (CVP) [8, 9, 10] optimization strategies to predict the optimal charging current profiles. Northrop et. al. and Subramanian et. al. [11, 12] did model reformulation to reduce the computation time. In this work, we present an alternative approach to control batteries applying the concept of differential geometry. This approach is called as Generic Model Control [13], or Reference Control Synthesis [14]. This work enables robust stabilization and control of battery models to set point as an alternative approach. As compared to the generic model control approaches implemented by previous researchers, we implement the same using the direct DAE numerical solvers including iteration free solvers recently developed in our group [15]. The results are presented for single input single objective (maximizing charge stored with current input), single input multiple objective (maximizing charge stored with minimizing fade and temperature rise), constrained problems (controlling the temperature rise and fade while charging), along with models and plants with time delays. The proposed new approach is compared with simultaneous and direct optimization strategies. It is expected that the proposed approach will be more robust if not globally optimal. Acknowledgements The work presented herein was funded in part by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275 and the Clean Energy Institute at the University of Washington, Seattle.

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system’s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to 'learn'the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.

Predictive Control of Permanent Magnet Synchronous Motor Based on Optimization Model Algorithmic Control

This paper presents an optimization model algorithmic control (OMAC) for permanent magnet synchronous motor drive system. The OMAC controller is designed based on the second order ARMA model of permanent magnet synchronous motor. It optimizes the correction coefficient of calculation error and prediction error coefficient of output as well as the amount of the weighting factor in the traditional model algorithmic control. Then selecting only one future time of predictive values to calculate the optimal control law simplifies the calculation process and so overcomes the shortcomings of traditional control needed to select the number of prediction horizon and required to choose a longer sampling period to calculate appropriate control quantity. The algorithm is simple and robustness of the system can be guaranteed. Therefore, the OMAC is well positioned to meet the dynamic and rapid motor real-time requirements. Simulation is conducted to verify the performance of the proposed control scheme.

Nonlinear Model Algorithmic Control of a pH Neutralization Process

Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm (named NLH-MAC) is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative (PID) controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.

A Power Smoothing Control Strategy and Optimized Allocation of Battery Capacity Based on Hybrid Storage Energy Technology

Wind power parallel operation is an effective way to realize the large scale use of wind power, but the fluctuations of power output from wind power units may have great influence on power quality, hence a new method of power smoothing and capacity optimized allocation based on hybrid energy storage technology is proposed in terms of the uncontrollable and unexpected characteristics of wind speed in wind farms. First, power smoothing based on a traditional Inertial Filter is introduced and the relationship between the time constant, its smoothing effect and capacity allocation are analyzed and combined with Proportional Integral Differential (PID) control to realize power smoothing control of wind power. Then wavelet theory is adopted to realize a multi-layer decomposition of power output in some wind farms, a power smoothing model based on hybrid energy storage technology is constructed combining the characteristics of the Super Capacitor (SC) and Battery Energy Storage System (BESS) technologies. The hybrid energy storage system is available for power fluctuations with high frequency-low energy and low frequency-high energy to achieve good smoothing effects compared with a single energy storage system. The power fluctuations filtered by the Wavelet Transform is regarded as the target value of BESS, the charging and discharging control for battery is completed quickly by Model Algorithm Control (MAC). Because of the influence of the inertia and the response speed of the battery, its actual output is not completely equal to the target value which mainly reflects in high-frequency part, the difference part uses SC to compensate and makes the output of battery and SC closer to the target value on the whole. Compared with the traditional Inertial Filter and PID control method, the validity of the model was verified by simulation results. Finally under the premise of power grid standards, the corresponding capacity design had been given to reduce the size of the energy storage devices as far as possible, which has a certain practical engineering value.

Chance Constrained Model Predictive Control Based on Set-Valued Optimal Algorithm

This paper adopts one kind of classical predictive control, namely model algorithmic control, to control the distillation process. Due to finite cognition degree for the distillation system and existence noise in the chemical industry, we present a chance constrained model predictive control algorithm to eliminate the effect of parameter pertubation and noise. In view of the linear impulse response model, we introduce set-valued optimal algorithm and orthogonal standardization method to transform the chance constrained model predictive control to a general model predictive control problem. For the determinate MPC, efficient quadratic programming exsits to solve such problem. Applying such controller to a distillation system, output constraint condition will be satisfied with a predefined probability.

Constrained Model Algorithmic Control for the Generalized Hammerstein Model with Impulse Response

A Generalized Hammerstein Model with Impulse Response for symmetric nonlinear systems was presented in this paper. A hyper-quadratic object function was developed by adding highest order control input term with a symbolic function into the object function, and a constrained multi-step model algorithmic control for the non-minimum phase systems with open-loop stable characterization was established by forcing the control input with saturated limitation. The algorithm with one control policy can guarantee the simulative results without steady state deviation and the control input being converged to a varying region centered in the zero-point. Simulated results validated the constrained model algorithmic control for Generalized Hammerstein Model with impulse response is reasonable and applicable.

Model algorithm control for path tracking of wheeled mobile robots

This paper proposes a model algorithm control (MAC) method for the path tracking control of differentially steered wheeled mobile robots (WMRs) subject to nonholonomic constraints. The continuous dynamic model of the wheeled mobile robot is presented and used as the model to be controlled. The MAC controller is designed based on the sampled-data representation of the system. In this paper the case that there exists time delay in the control input is also considered. A time discretization method using the Taylor series and the zero-order-hold (ZOH) assumption is proposed to discretize the continuous dynamic model of the WMR. This time discretization method is especially useful in the case of the time delayed system. It can provide finite dimensional and more accurate discretized form model of the mobile robot with input time delay and convert it into a general nonlinear time discretized form to which the MAC controller can be applied. Simulations are conducted to show the performance and feasibility of the proposed control strategy. In these simulations the WMR is controlled to track two difference reference paths such as the “8” shape path and the circular path. The bounded inertial parameters uncertainties and some disturbance are also considered in the model of the control system.

Path Tracking Control for a Wheeled Mobile Robot

In this paper a model algorithm control (MAC) method is proposed to do the path tracking control of a wheeled mobile robot (WMR). This mobile robot is a three-wheel differentially steered wheeled mobile robot subject to nonholonomic constraints. The kinematic model of this mobile robot is presented and used as the mobile robot model to be controlled. Simulations are conducted to show the performance and feasibility of the proposed control strategy for the path tracking of a wheeled mobile robot.

Controller design for nonlinear systems with time delay using model algorithm control (MAC)

This paper presents a digital model algorithm control (MAC) for nonlinear time-delayed systems. The MAC controller is designed based on the sampled-data representation of the dynamic system. A new discretization method using the Taylor series and zero order hold assumption is proposed to discretize the continuous nonlinear time-delayed system. The proposed discretization method can provide accurate time discretization results and convert the discrete-time system into a general nonlinear system, so that the MAC method can be employed. Several simulations are conducted to verify the performance of the proposed method.

A new modeling and control scheme for thyristor-controlled series capacitor

In order to design an optimal controller for the thyristor controlled series capacitor (TCSC), a novel TCSC control model is developed. In the model, the delay angle of thyristor valves is the input, and the inductor current is chosen as the output. Theoretical analysis and simulation studies show that TCSC is a non-linear system and its parameters vary with the operating point. In consideration of the special characteristics of the TCSC, an improved model algorithmic control (IMAC) scheme is proposed to control TCSC effectively. The good performance can be observed from simulation results when IMAC is applied to a series compensated radial system.

MODEL ALGORITHM TRACKING CONTROL OF WHEELED MOBILE ROBOTS

This paper proposed a model algorithm control (MAC) method for trajectory tracking control of the differentially steered wheeled mobile robots (WMRs) subject to nonholonomic constraint. The dynamic model of the wheeled mobile robot is presented and used as the model to be controlled. The performance of the proposed control algorithm is verified via computer simulations in which the WMR is controlled to track two different reference paths. It is shown that the control strategy is feasible.

Two-degree-of-freedom output feedback controllers for discrete-time nonlinear systems

A discrete-time, model-based output feedback control structure for nonlinear processes is developed in the present work. The structure makes use of a closed-loop observer, while at the same time it guarantees that the overall feedback controller possesses integral action. An algebraic transformation is applied on the observer states to insure that the input/output gain of the observer matches the model upon which the static state feedback control law is based. The resulting control algorithm is a two-degree-of-freedom control law, in the sense that the output and the set point are processed in different ways. The control structure is shown not only to have the same properties as the standard model-state feedback structure, but also that it emerges from a model algorithmic control framework. Finally, a simulation example using an exothermic CSTR operating at an open-loop unstable steady state is used to evaluate the closed-loop performance of the proposed method.