Conventional Control Approach Research Articles

Approximate Dynamic Programming Vector Controllers for Operation of IPM Motors in Linear and Overmodulation Regions

To improve the efficiency of a permanent magnet (PM) motor, it is usually required to extend the motor operation from linear to over or even six-step modulation regions based upon the space vector pulsewidth modulation technique. Traditionally, a PM motor is controlled by using the standard vector control technique. However, a recent study shows the decoupling inaccuracy of designing PM motor control using the conventional standard vector control approach. This issue has caused a challenge to control a PM motor, particularly when the motor operates from linear to overmodulation regions. In this article, a novel approximate dynamic programming (ADP) vector controller is developed to overcome the challenge. The ADP controller is developed using the full dynamic equation of a PM motor and implemented using an artificial neural network (ANN). A feedforward control strategy is integrated with the -based ADP controller to enhance the performance of the controller in both linear and overmodulation regions. The ANN-based ADP control is compared with the conventional standard vector control and a model predictive control. Simulation and hardware experiments demonstrate that the proposed controller can track reference changes better with high efficiency and reliability for PM motor operation in linear and overmodulation regions.

A discrete-time sliding mode congestion controller for wireless sensor networks

Congestion is a challenging problem for Wireless Sensor Networks (WSNs) because it results in energy waste, packet loss, low throughput and short lifecycle. By regulating incoming and outgoing packets at a particular node, this paper first proposes a new traffic-based discrete congestion control model in WSNs. Then, based on this model, an exponential-reaching-law-based discrete-time sliding mode congestion controller (DSMC) is designed, which effectively adjusts bottleneck nodes’ queue length to desired value. Extensive NS-2.35 simulation results demonstrate that the proposed DSMC outperforms conventional control approaches (such as Fuzzy control, PID control and Fuzzy-PID), it significantly controls queue length and avoids congestion, and has better performance such as short delay, low packet loss rate, high throughput, long network lifetime, fast convergence and strong stability.

Comparative study of algorithms for optimized control of industrial energy supply systems

Both rising and more volatile energy prices are strong incentives for manufacturing companies to become more energy-efficient and flexible. A promising approach is the intelligent control of Industrial Energy Supply Systems (IESS), which provide various energy services to industrial production facilities and machines. Due to the high complexity of such systems widespread conventional control approaches often lead to suboptimal operating behavior and limited flexibility. Rising digitization in industrial production sites offers the opportunity to implement new advanced control algorithms e. g. based on Mixed Integer Linear Programming (MILP) or Deep Reinforcement Learning (DRL) to optimize the operational strategies of IESS.This paper presents a comparative study of different controllers for optimized operation strategies. For this purpose, a framework is used that allows for a standardized comparison of rule-, model- and data-based controllers by connecting them to dynamic simulation models of IESS of varying complexity. The results indicate that controllers based on DRL and MILP have a huge potential to reduce energy-related cost of up to 50% for less complex and around 6% for more complex systems. In some cases however, both algorithms still show unfavorable operating behavior in terms of non-direct costs such as temperature and switching restrictions, depending on the complexity and general conditions of the systems.

Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions

Smart grid is the new trend for clean, sustainable, efficient and reliable energy generation, delivery and use. To ensure stable and secure operation is essential for the smart grid, which needs effective stability analysis and control. As the smart grid has evolved through a growing scale of interconnection, increasing integration of renewable energy, widespread operation of direct current power transmission systems, and liberalization of electricity markets, the stability characteristics of it are much more complex than the past. Due to these changes, conventional stability analysis and control approaches have a series of drawbacks in terms of speed, effectiveness and economy. On the contrary, the emerging artificial intelligence (AI) techniques provide powerful and promising tools for stability analysis and control in smart grids and have attracted growing attention. This paper aims to give a comprehensive and clear picture of recent advances in this research area. First, we present a general overview of AI, including its definitions, history and state-of-the-art methodologies. And then, this paper gives a comprehensive review of its applications to security assessment, stability assessment, fault diagnosis, and stability control in smart grids. These applications have achieved impressive results. Nevertheless, we also identify some major challenges these applications face in practice: high requirements on data, imbalanced learning, interpretability of AI, difficulties in transfer learning, the robustness of AI to communication quality, and the robustness against attack or adversarial examples. Furthermore, we provide suggestions for potential important future investigation directions to overcome these challenges and bridge the gap between research and practice.

Variable Rounding Level Control Method for Modular Multilevel Converters

This article proposes a new control method for modular multilevel converter (MMC), which is based on a modification of the conventional nearest level control (NLC) scheme implemented by a model predictive control (MPC) technique. Applying the proposed method, the simplicity of the NLC is maintained while the current control ability in terms of both phase current and circulating current is enhanced by the MPC technique. A proper rounding function is used for each arm of the MMC instead of the conventional nearest integer function. The rounding function is determined by evaluating a cost function within the frame of MPC. To validate the proposed technique, a three-phase MMC prototype is employed. The conventional NLC-based control approach of MMC is also evaluated for comparative purposes. Results show that the proposed method exhibits excellent current-regulating ability. Compared with the conventional control scheme, the overall performance is improved without significantly increasing the computational burden or adding relevant complexity to the control system design.

Incremental Model Predictive Control of Active Suspensions With Estimated Road Preview Information From a Lead Vehicle

Abstract In preview-based vehicle suspension applications, the preview of the road profile is highly dependent on the preview sensors. In some scenarios such as heavy traffic situations, the preview of road profile can only be estimated by other vehicles because the view of the preview sensors may be blocked by other vehicles. The estimated preview road information can contain errors, which thus requires the controller to have a good robust performance. In this paper, an incremental model predictive control (MPC) strategy for active suspension systems along with a road profile estimator using preview information from a lead vehicle is proposed. The efficacy of the proposed strategy is experimentally validated on two scaled-down active suspension stations with comparison to two conventional active suspension control approaches.

A Fast Finite-Level-State Model Predictive Control Strategy for Sensorless Modular Multilevel Converter

In this article, we propose a fast finite-level-state model predictive control (FFLS-MPC) strategy for solving the well-known challenges in the predictive control-regulated modular multilevel converter (MMC), which aims to overcome the high computational complexity and enhance the reliability of the control system. First, the presentable output voltage level is determined from solving the Diophantine equations' point of view, which has a good potential to avoid the online optimization process and the selection of weighting factors compared with the conventional finite control-set model predictive control (FCS-MPC) approach. In this sense, the overall computational effort is significantly decreased while achieving satisfactory control performance. Alternatively, in order to enhance the system reliability under sensor failures (e.g., arm current sensor and submodule (SM) voltage sensor), a novel control strategy for sensorless MMC is presented by combining an adaptive linear-neuron-based SM voltage estimation scheme with a currentless sorting-based capacitor-voltage-balancing approach to serve this purpose. We contribute two main points to the relevant literature. The first one is the improvement of computationally efficient by employing the proposed FFLS-MPC methodology. The second one is the elimination of the arm current sensor and SM voltage sensor while guaranteeing adaptability to different conditions. Finally, compared with the state-of-the-art FCS-MPC strategies, comprehensive simulation studies and experiments are presented to demonstrate the effectiveness and feasibility of the proposed methodology for MMC.

Hybrid statistical-machine learning ammonia forecasting in continuous activated sludge treatment for improved process control

In this work, a statistical stability metric and novel hybrid statistical-machine learning ammonia forecasting model are developed to improve the accuracy and precision of municipal wastewater treatment. Aeration for biological nutrient removal is typically the largest energy expense for municipal wastewater treatment plants (WWTP). Ammonia-based aeration control (ABAC) is one approach designed to minimize excessive aeration by adjusting air blower output from online ammonia measurements rather than from a dissolved oxygen (DO) sensor, which is the conventional aeration control approach. We propose a quantitative stability metric, Total Sample Variance, to compare system-wide variability of competing aeration control strategies. Using this metric, the performance of traditional DO and ABAC control strategies with varying setpoints and control parameters were compared in a medium-sized WWTP, and the most stable strategy was identified and implemented at the facility. To further improve ABAC performance, ammonia forecasting models were constructed using both statistical and machine learning to improve the accuracy of the aeration control system. Diurnal, diurnal-linear, artificial neural network (ANN), and hybrid diurnal-linear-ANN forecasting models were trained on real-time plant-wide process data. The diurnal-linear and diurnal-linear-ANN forecasts were found to most accurately forecast ammonia; improving upon the existing ammonia measurement by up to 32% and 46%, respectively, whereas the ANN model forecast was only able to improve by up to 8%. This work demonstrates the ease and flexibility of integrating statistics and machine learning methods for developing new treatment models in conventional WWTP for features in full-scale conventional activated sludge systems.

Hybrid Power Supply System of Rotor-Type Electromagnetic Separator Windings with Incomplete Determination of Parameters Load

Consideration is given to the control characteristics of rotating magnetic separators with a high gradient. There are provided structural schematics of control systems employing fuzzy logic controllers for high-gradient electromagnetic separation. A new structural diagram of the hybrid control system of the rotor-type electromagnetic separator, which combines conventional and intelligent control approaches, is proposed. Based on a rotary electromagnetic separator, it is demonstrated that the employment of hybrid control methods makes it possible to boost energy efficiency significantly in electrical technologies employing current sources in power circuits where system characteristics are uncertain.

Machine Learning for End-to-End Congestion Control

End-to-end congestion control has been extensively studied for over 30 years as one of the most important mechanisms to ensure efficient and fair sharing of network resources among users. As future networks are becoming more and more complex, conventional rule-based congestion control approaches tend to become inefficient and even ineffective. Inspired by the great success that machine learning (ML) has achieved in addressing large-scale and complex problems, researchers have begun to shift their attention from the rule-based method to an ML-based approach. This article presents a selected review of the recent applications of ML to the field of end-to-end congestion control. In this survey, we start with a brief review of the relationship between congestion control and ML. We then review the recent works that apply ML to congestion control. These works either help the agent to make an intelligent congestion control decision or achieve enhanced performance. Finally, we highlight a series of realistic challenges and shed light on potential future research directions.

A centralised cost management system: exploiting EVM and ABC within IPD

PurposeIntegrated project delivery (IPD) is highly recommended to be utilised with building information management (BIM), specifically with BIM level-3 implementation process. Extant literature highlights the financial management challenges facing the proposed integration. These challenges are mainly related to the IPD compensation and the conventional cost control approaches that are not consistent with IPD principles. As such, this paper presents an integration of several methods to support automating risk/reward sharing amongst project parties thus enhancing IPD core team members’ relationship.Design/methodology/approachThe literature review was used to highlight the challenges that face the IPD-based cost management practices such as the risk sharing/reward sharing amongst IPD core team members and potential methods to bridge the revealed IPD gap. A framework was developed by integrating the activity-based costing (ABC) – as a method to analyse the cost structure – and earned value management (EVM) to develop mathematical models that can determine the three main IPD financial transactions (i.e. …) fairly. To demonstrate the applicability of the developed system, a real-life case study was used, in which, promising results were collected in regard to visualising the cost control data and understanding of the accumulative status of the project cost and schedule for team members.FindingsA centralised cost management system (CCMS) for IPD is developed to enable the IPD cost structure as well as automating the risk-sharing/reward-sharing calculations. This system is linked with a web-based management system to display the output of proposed risk-sharing/reward-sharing models. Moreover, a novel grid is developed to show the project status graphically and to respect the diversity in core team members backgrounds. In addition, the case study showed that the proposed integration of different methods (ABC, EVM, BIM and web-based management system) is interoperable and applicable.Originality/valueThis research presents a comprehensive solution to the most revealed challenges in cost management practices in IPD implementation. The outcome of this research contributes to the body of knowledge through presenting new extensions of the EVM to be used with the IPD approach to calculate risk/reward. Moreover, the implementation of the proposed tools such as centralised cost management system (CCMS) and CCMS for IPD web system will enhance/foster the implementation of the IPD in conjunction with BIM process.

Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations

In this paper, a reliability aware multi-objective predictive control strategy for wind farm based on machine learning and heuristic optimizations is proposed. A wind farm model with wake interactions and the actuator health informed wind farm reliability model are constructed. The wind farm model is then represented by training a relevance vector machine (RVM), with lower computational cost and higher efficiency. Then, based on the RVM model, a reliability aware multi-objective predictive control approach for the wind farm is readily designed and implemented by using five typical state of the art meta-heuristic evolutionary algorithms including the third evolution step of generalized differential evolution (GDE3), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective particle swarm optimization (MOPSO), the multi-objective grasshopper optimization algorithm (MOGOA), and the non-dominated sorting genetic algorithm III (NSGA-III). The computational experimental results using the FLOw Redirection and Induction in Steady-state (FLORIS) and under different inflow wind speeds and directions demonstrate that the relative accuracy of the RVM model is more than 97%, and that the proposed control algorithm can largely reduce thrust loads (by around 20% on average) and improve the wind farm reliability while maintaining similar level of power production in comparison with a conventional predictive control approach. In addition, the proposed control method allows a trade-off between these objectives and its computational load can be properly reduced.

Robust Tracking in Underactuated Systems Using Flatness-Based ADRC With Cascade Observers

Abstract In this work, the problem of trajectory tracking in uncertain underactuated systems is considered. To solve it, a combination of differential flatness and active disturbance rejection control (ADRC) is proposed. The controller design is synthesized in the absence of detailed knowledge of the system model and focuses on dealing with over-amplification of measurement noise, typically seen in conventional single high-gain observer-centered control approaches. The introduced solution is based on fully utilizing the information already available about the governed system, without the necessity for additional measurement devices. To be easily implementable, it is expressed in an industry familiar error-based form with a straightforward tuning method. Through experimental verification, the proposed approach is shown to enhance the disturbance-rejection capabilities of the standard ADRC structure and reduce its sensitivity to measurement noise, thus increasing its practical appeal.

Combined ILC and PI regulator for wastewater treatment plants

Due to high nonlinearity with features of large time constants, delays, and interaction among variables, control of the wastewater treatment plants (WWTPs) is a very challenging task. Modern control strategies such as model predictive controllers or artificial neural networks can be used to deal with the non-linearity. Another characteristic of this system should be considered is that it works repetitively. Iterative learning control (ILC) is a potential candidate for such a demanding task. This paper proposes a method using ILC for WWTPs to achieve new results. By exploiting data from the previous iterations, the learning control algorithm can improve gradually tracking control performance for the next runs, and hence outperforms conventional control approaches such as feedback controller and model predictive control (MPC). The benchmark simulation model No.1-BSM1 has been used as a standard for performance assessment and evaluation of the control strategy. Control of the dissolved oxygen in the aerated reactors has been performed using the PD-type ILC algorithms. The obtained results show the advantages of ILC over a classical PI control concerning the control quality indexes, IEA and ISE, of the system. Besides, the conventional feedback regulator is designed in a combination with the iterative learning control to deal with uncertainty. Simulation results demonstrate the potential benefits of the proposed method.

Cooperative Control of Drive Motor and Clutch for Gear Shift of Hybrid Electric Vehicles With Dual-Clutch Transmission

This article deals with the cooperative control of drive motor and clutches for the gear shift of a parallel hybrid electric vehicle (HEV) with a dual-clutch transmission (DCT). An HEV with DCT powertrain requires the sophisticated control of two clutch actuators and power sources to achieve outstanding gear shift performances. In this article, a new shift control strategy based on the feedback of both speed and torque states is implemented to improve the shift quality. Different from previous approaches, the proposed control uses the dynamic torque observer to accurately track the desired transient torque during the inertia phase. Since a drive motor is installed in the HEV's driveline, the transient torque through the driveline can be effectively controlled by using the fast dynamic characteristics of the drive motor. A major difficulty arising from the shift control of DCT is the interactions between the speed and torque control loops. To treat the coupling effects effectively even in the presence of model uncertainties and disturbances, a robust multivariable control scheme using H-infinity loop shaping is suggested, especially for the inertia phase control. The performance of the final-observer-based controller is demonstrated through real-time experiments. A comparative study with the conventional control approach is also conducted, and detailed discussions of the results are provided.

Polynomial regressors based data-driven control for autonomous underwater vehicles

This paper proposes a data-driven control approach on the basis of polynomial regressors for autonomous underwater vehicles (AUVs). In contrast to conventional control approach, data-driven control does not require modeling for the systems. It only utilizes the analysis of massive stored dataset to predict the future control input, which can achieve the future output. Generally, the massive stored dataset can be analyzed by short-length vectors linearly. In this paper, a novel point is the improvement of existing data-driven control by polynomial regressors, which improve the control performance of AUVs. By numerical simulations, we illustrate the effectiveness of our proposed approach.

The Motion Trajectory-Based Finite-Time Control for the Marine Surface Vessel

This brief is devoted to the predesigned motion trajectory-based finite time dynamic positioning (DP) control for a marine surface vehicle (MSV) with unknown external disturbances. Firstly, a preset motion trajectory is presented through establishing the relationship function among position tracking errors and heading tracking error, facilitating the MSV to arrive in the equilibrium point along the pre-designed trajectory. Furthermore, a novel nonsingular and fast terminal sliding mode control (NTSMC) approach is investigated, which ensures faster convergence rate and better stability performance of the close-loop system than the conventional backstepping control approach. What’s more, by incorporating the adaptive technique with the NTSMC approach, an adaptive nonsingular and fast terminal sliding mode control (ANTSMC) strategy is addressed. Compared to the NTSMC approach, it strengthens robustness to disturbances and guarantees system states to converge to a closer neighborhood of the equilibrium point. Finally, simulation results illustrate the remarkable effectiveness of proposed control schemes.

Periodic Trajectory of Relative Motion Controlled by Constant Thrust

Formation flying has received widespread attention in recent years. However, the conventional control approaches require high demand for engine. To deal with this problem, the relative periodic trajectory can be achieved by multiple two-section constant thrust control proposed in this study. Firstly, the concept of the constant thrust control is presented. Furthermore, the strategy is presented to guarantees periodic trajectory considering J2 perturbation. Simulations are conducted to demonstrate the efficacy of the method. The proposed control approach is applied for engineering applications.

DC-Link Current Control with Inverter Nonlinearity Compensation for Permanent Magnet Synchronous Motor Drives

For permanent magnet synchronous motors (PMSMs) supplied with a voltage source inverter, current control strategies are commonly implemented under the synchronously rotating reference frame. In order to simplify the system structure, three-phase currents can be measured with a single DC-link current sensor using the phase current reconstruction technique. However, it still needs to follow the conventional AC current control approach. In this paper, a DC-link current control method for PMSMs is proposed to further simplify the control system. The problem of phase current control was separated into the problems of amplitude control and phase control. Then, amplitude control was achieved using a closed-loop controller directly tracking the DC-link current; while phase control was achieved by AC-side pulse width modulation (PWM) following the phase angle of back electromotive force. The compensation for nonlinear distortion of the inverter was taken into account during the control process. Finally, the proposed method realized three-phase current control with a single current sensor and controller, and achieved the purpose of electromagnetic torque control. Experimental results demonstrate the effectiveness of the proposed method.

Design and analysis of novel Chebyshev neural adaptive backstepping controller for boost converter fed PMDC motor

An adaptive backstepping Chebyshev neural network controller (ABCNNC) is proposed for the boost converter fed PMDC motor to track the angular velocity. The computational complexity of the neural network is avoided by the use of Chebyshev polynomials as the basis function. The online weight update of the Chebyshev neural network (CNN) is designed for the closed loop system based on the Lyapunov stability analysis to obtain the asymptotically stable system. A detailed analysis of the steady state and transient performance is performed and results are compared with that of conventional PI controller and radial basis function neural network controller (RBFNNC). To ensure the robustness of the proposed ABCNNC, it is being analysed for a wide range of variations in load torque and the set point changes and it is validated by comparing with the conventional PI control approach and RBFNNC. Comparison of results validates that the proposed ABCNNC shows the enhanced transient and steady state responses for the uncertainties caused by disturbances, than conventional PI controller and RBFNNC.