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Online parameter estimation of permanent magnet synchronous machines is critical for improving their control performance and operational reliability. This paper provides an overview of the recent achievements of online parameter estimation of PMSMs with examples. The critical issues in parameter estimation are firstly analysed, especially the rank-deficient issue and inverter nonlinearities. Then, the state-of-the-art online parameter estimation modelling techniques are reviewed and assessed. Finally, some typical applications and examples are outlined, e.g. estimation of mechanical parameters, improvement of sensored and sensorless control performance, thermal condition monitoring, and fault diagnosis, together with future research trends.

In the forthcoming era of the Tactile Internet, haptic communication is foreseen as one of its major use cases with impact in manufacturing, healthcare, education, as well as the service industry. Recent efforts in networking attempt to meet the requirements of such use cases providing the latency and reliability for bilateral teleoperation, the main component of haptic communication. However, the impact of changes in latency on bilateral teleoperation system performance varies among different control schemes and is dependent on the application domain. Furthermore, while recent efforts to reduce latency in wireless communication with tailored configurations have been successful, an increasing number of haptic communication flows could potentially compete when sharing network resources. In this paper, we provide a tractable model for teleoperation system performance that captures the impact of latency on different performance criteria. We then use this performance model to shape queuing prioritisation of different traffic flows. The proposed framework considers the requirements of high and low priority flows to suggest the best possible control scheme option to be used by the high priority one and at the same time keep the impact of the network scheduling discipline on the low priority one at minimum.

There are mechanical coupling interference and hydraulic coupling interference in the vehicle wheel bridge simulation loading system based on secondary regulation, which can cause the control accuracy of the system to decrease obviously and seriously affect the control performance of the system. In order to eliminate the coupling interference between systems, two decoupling methods are proposed, one way to do this is to join the decoupling element, and the other is to join the robust compensator. The simulation results show that the decoupling control of decoupling element has a good decoupling effect on mechanical coupling, but it has no effect on hydraulic coupling. Robust decoupling control has a good decoupling effect on mechanical coupling and hydraulic coupling.

In this study, a DC micro‐grid consisting of multiple paralleled energy resources interfaced by both bidirectional AC/DC and DC/DC boost converters and loaded by a constant power load (CPL) is investigated. By considering the generic dq transformation of the AC/DC converters' dynamics and the accurate nonlinear model of the DC/DC converters, two novel control schemes are presented for each converter‐interfaced unit to guarantee load voltage regulation, power sharing and closed‐loop system stability. This novel framework incorporates the widely adopted droop control and using input‐to‐state stability theory, it is proven that each converter guarantees a desired current limitation without the need for cascaded control and saturation blocks. Sufficient conditions to ensure closed‐loop system stability are analytically obtained and tested for different operation scenarios. The system stability is further analysed from a graphical perspective, providing valuable insights of the CPL's influence onto the system performance and stability. The proposed control performance and the theoretical analysis are first validated by simulating a three‐phase AC/DC converter in parallel with a bidirectional DC/DC boost converter feeding a CPL in comparison with the cascaded PI control technique. Finally, experimental results are also provided to demonstrate the effectiveness of the proposed control approach on a real testbed.

In order to improve the control performance of the valve-controlled asymmetric cylinder system with negative overlap, it is proposed that the complex nonlinear system is transformed into a linear system based on the neural network inverse system. The structure of the inverse system is been constructed by the state model of the system, and optimised by adding state parameter feedback. So the pseudo-linear system is reduced to second order. An improved back-propagation neural network based on a genetic algorithm is used to solve the inverse model. Aiming at the problem of negative overlap of servo valves and laminar and turbulent flow, the sectional inverse system switched by reference speed is established to improve the accuracy of the inverse system model. For pseudo-linear systems, an internal model controller is designed to improve the control performance of the composite system. The proportion–integration–differentiation and the internal model control (IMC) of the sectional inverse system are compared with that of AMESIM and Simulink joint platform. The results show that the system controlled by the IMC of the sectional inverse system has better response performance, eliminates the asymmetric characteristics in extending and retracting motion response and is less affected by load fluctuation.

This paper reports the optimal control problem on the interior permanent magnet synchronous motor (IPMSM) systems. The control performance of the traditional model predictive control (MPC) controller is ruined due to the parameter uncertainty and mismatching. In order to solve the problem that the MPC algorithm has a large dependence on system parameters, a method which integrates MPC control method and parameter identification for IPMSM is proposed. In this method, the d-q axis inductances and rotor permanent magnet flux of IPMSM motor are identified by the Adaline neural network algorithm, and then, the identification results are applied to the predictive controller and maximum torque per ampere (MTPA) module. The experimental results show that the optimized MPC control proposed in this paper has a good steady state and robust performance.

This paper presents and discusses a mineralogy-driven control strategy for a multiple hearth furnaces in kaolin production. The objective of the advanced control is to maximize capacity and to minimize energy consumption while preserving the desired product quality. The control is based on two main soft sensors: the mullite content indicator for capacity improvement and the spinel phase reaction rate indicator for energy use reduction. In this simulation study, the control strategy is tested and compared with an industrial controller based on a proportional-integral scheme as a benchmark. The results show that the capacity of the process is significantly improved and the energy use is notably diminished.

This study concerns adaptive feedforward and hybrid (combined feedback and feedforward) control systems for active vibration suppression of smart flexible beam with a tip mass. By analysing traditional feedforward filtered-X least mean square (FXLMS) algorithm and filtered-X variable step size least mean square (FX-VSSLMS) algorithm, a new hybrid FX-VSSLMS algorithm is developed to improve the control performance of feedforward controllers. A stainless steel beam with a tip mass is employed to simulate flexible robot arm with varying loads. Piezoelectric actuators and sensors are attached to the surface of the beam. Comparison experiments show the effectiveness and benefits of the proposed hybrid FX-VSSLMS method.

Limitations of the delayed feedback control and of its extended versions have been fully treated in the literature. The oscillating delayed feedback control appears as a promising scheme to overcome this problem. In this work, two methods based on oscillatory delayed feedback control schemes for the continuous time case are dealt with. For both of them, stabilization of an equilibrium point in the general non-linear scalar case is rigorously proven. Additionally, their control performance and stability parameters region are respectively studied., Comment: 19 pages, 11 figures

In order to improve the braking performance of electric vehicles, a novel brake-by-wire actuator based on an electro-magnetic linear motor was designed and manufactured. For the purpose of braking force regulation accuracy and high robust performance, the state observer and the anti-disturbance controller were designed in this paper after describing the actuator structure, braking principle, and mathematical model. The simulation and experimental results showed that the brake actuator responded rapidly, since its response time was only 15 ms. Compared to traditional PID (Proportion Integration Differentiation) methods, the controller proposed in this paper is able to regulate the braking force more precisely and has better anti-disturbance performance, thus the braking process can be accurately controlled according to the driver&rsquo, s demand. The vehicle simulation results showed that the braking distance and braking time were shortened by 12.19% and 15.54%, respectively compared with those of the conventional anti-lock brake system (ABS) in the same braking conditions.

Iterative Learning Control (ILC) enables high control performance through learning from measured data, using limited model knowledge, typically in the form of a nominal parametric model. Robust stability requires robustness to modeling errors, often due to deliberate undermodeling. The aim of this chapter is to outline a range of design approaches for multivariable ILC that is suited for engineering applications, with specific attention to addressing interaction using limited model knowledge. The proposed methods either address the interaction in the nominal model, or as uncertainty, i.e., through robust stability. The result is a range of techniques, including the use of the structured singular value (SSV) and Gershgorin bounds, that provide a different trade-off between modeling requirements, i.e., modeling effort and cost, and achievable performance. This allows control engineers to select the approach that fits best the modeling budget and control requirements. This trade-off is demonstrated in case studies on industrial printers. Additionally, two learning approaches are presented that are compatible with, and provide extensions to, the developed multivariable design framework: model-free iterative learning, and ILC for varying tasks.

With the development of photovoltaic power generation technology, a large number of micro-power grid with distributed generation system have appeared in remote areas. It has the longer transmission line between the traditional power grid and the inverter of the distributed generation system. This caused the equivalent network impedance would be increased and would weak the power-grid characteristics. When the photovoltaic inverter is connected to a weaker power grid, the bandwidth of the control system of the inverter will be reduced. Using the traditional PR control to solve the problem is not ideal. This paper proposes fuzzy quasi-PRD control to improve the current quality. The inverter with the LCL filter has the peak of the resonance oscillation, it will affect the stability of the system. The paper adopts the adjustable virtual damping to suppress resonance. At last, the idea is verified by MATLAB/Simulink. The results show that the fuzzy quasi-PRD control method has better control performance, and dynamic tracking characteristics.

International audience; Replay mode simulations enable to evaluate AO control performance (residual variance) using on-sky telemetry. This paper proposes simple procedures to remove estimation bias and to evaluate the impact of on-sky measurement noise propagation.

Forward and Backward Reaching Inverse Kinematics (FABRIK) is a recent iterative inverse kinematics solver that became very popular because of its simplicity, convergence speed and control performance, especially in models with multiple end effectors. In this paper, we extend and/or adjust FABRIK to be used in problems with leaf joints and closed‐loop chains and to control a fixed inter‐joint distance in a kinetic chain with unsteady data. In addition, we provide optimisation solutions when the target is unreachable and a proof of convergence when a solution is available. We also present various techniques for constraining anthropometric and robotic joint models using FABRIK and provide clarifications and solutions to many questions raised since the first publication of FABRIK. Finally, a human‐like model that has been structured hierarchically and sequentially using FABRIK is presented, utilising most of the suggested joint models it can efficiently trace targets in real time, without oscillations or discontinuities, verifying the effectiveness of FABRIK. Copyright © 2015 John Wiley & Sons, Ltd.AbstractThis paper addresses the problem of manipulating articulated figures in an interactive and intuitive fashion for the design and control of their posture using the Forward and Backward Reaching Inverse Kinematics algorithm the algorithm has been extended to support a variation of different joints and has been evaluated on a humanoid model. 1 online ID: 843 In: Computer animation and virtual worlds, Vol. 27, no. 1 ( 2016), p.35-57. Summary: AbstractForward and Backward Reaching Inverse Kinematics (FABRIK) is a recent iterative inverse kinematics solver that became very popular because of its simplicity, convergence speed and control performance, especially in models with multiple end effectors. In this paper, we extend and/or adjust FABRIK to be used in problems with leaf joints and closed‐loop chains and to control a fixed inter‐joint distance in a kinetic chain with unsteady data. In addition, we provide optimisation solutions when the target is unreachable and a proof of convergence when a solution is available. We also present various techniques for constraining anthropometric and robotic joint models using FABRIK and provide clarifications and solutions to many questions raised since the first publication of FABRIK. Finally, a human‐like model that has been structured hierarchically and sequentially using FABRIK is presented, utilising most of the suggested joint models it can efficiently trace targets in real time, without oscillations or discontinuities, verifying the effectiveness of FABRIK. Copyright © 2015 John Wiley & Sons, Ltd.AbstractThis paper addresses the problem of manipulating articulated figures in an interactive and intuitive fashion for the design and control of their posture using the Forward and Backward Reaching Inverse Kinematics algorithm the algorithm has been extended to support a variation of different joints and has been evaluated on a humanoid model.

Nowadays, the management of any modern enterprise requires a real-time information system which should allow the continuous and quick display of the data which is critical in order to control the company in the current economic context. The performance dashboard is such an information system. It is made up of quantity, quality or financial management indicators or, in other words, of greatly significant pieces of information put together, which have immediate meaning for the person reading it. We must take into consideration the fact that the data is extremely dense and it is in continuous movement, being used in forming plans, in supporting decisions and in achieving control. The quality of decision and the achievement of performance depend on the quality of the information supplied. That is the reason why, in order to be useful for the decision-making process, the information must be reliable, up-to-date, complete, pertinent and accessible for the decision makers. Thus, an efficient performance dashboard is the one that allows the assessment and management of performance by means of the progress methods set in the strategy, and that helps the management face the changes and the challenges in the current economic climate.

In this study, the previously proposed Support Vector Machines Based Generalized Predictive Control (SVM-Based GPC) method [1] has been applied in controlling the experimental three-tank system. The SVM regression algorithms have been successfully employed in modeling nonlinear systems due to their advantageous peculiarities such as assurance of the global minima and higher generalization capability. Thus, the fact that better modeling accuracy yields better control performance has motivated us to use an SVM model in the GPC loop [1]. In the method, the SVM model of the unknown plant is used to predict future behavior of the plant and also to extract the gradient information which is used in the Cost Function Minimization (CFM) block. The experimental results have revealed that SVM-Based GPC provides very high performance in controlling the system, i.e., the liquid level of the system can track the different types of reference inputs with very small transient-and steady-state errors even in a noisy environment when it is controlled by SVM-Based GPC. © Springer-Verlag 2009.

The paper presents a computational model for real-time control tasks, with the primary goal of simplifying the control and scheduling codesign problem. The model combines time-triggered I/O and inter-task communication with dynamic, reservation-based task scheduling. To facilitate short input-output latencies, a task may be divided into several segments. Jitter is reduced by allowing communication only at the beginning and at the end of a segment. A key property of the model is that both schedulability and control performance of a control task will depend on the reserved utilization factor only. This enables controllers to be treated as scalable real-time components. The model has been implemented in a real-time kernel and validated in a real-time control application.