Classical Feedback Control Research Articles

Deep reinforcement learning based resource allocation for electric vehicle charging stations with priority service

Deep reinforcement learning based resource allocation for electric vehicle charging stations with priority service

Finite-Region State Feedback Control for 2-D Switched Systems with Actuator Saturation Under the Weighted Average Dwell Time Technique

By using a weighted average dwell time approach, the finite-region stability and stabilization problems are investigated for a class of two-dimensional switched systems with actuator saturation in the Fornasini–Marchesini local state-space model. Firstly, we analyze the finite-region stability of the system studied by introducing the weighted average dwell time approach for the first time. Secondly, the classical state feedback control method is introduced to the system. Based on the established conditions, sufficient criteria are derived to ensure the resulting closed-loop system is finite-region-stable. Finally, a numerical example is provided to show the effectiveness of the provided design approach.

Eksik Tahrikli Döner Ters Sarkaç Sisteminin Yukarı Yükseltilmesi için Enerji Tabanlı Doğrusal Olmayan Kontrol Algoritması ve Deneysel Doğrulaması

Bu çalışmada, döner ters sarkaç sistemi hareketli koluyla sınırlı bir bölge içinde, salınımı kontrol edilerek ve sarkaç enerjisi artırılarak, kararlı denge noktasından kararsız denge noktasına çıkarılması hedeflenmektedir. Döner ters sarkaç sisteminin doğrusal olmayan modeli ve denge noktalarındaki doğrusal modelleri verildikten sonra sarkaç yukarı yükseltme algoritması tanıtılmaktadır. Yukarı yükseltme algoritması, sarkaç salınırken belli noktalarda kol yardımıyla sarkaca enerji yüklemesine dayanmaktadır. Burada kol ivmelendirilmesi sabit bir ivme ile hareket ettirilmektedir. Yukarı yükseltme işlemindeki algoritma, sarkacın hızının en fazla olduğu yere yani kararlı denge noktasına ilerlerken ve periyodunu tamamlarken devreye girmektedir. Bu analizler hesaplanmış sınırlar dikkate alınarak yapılmaktadır. Sarkacın yukarı yükselmesi gerçekleştiğinde doğrusal model üzerinden elde edilmiş temel bir tam durum geri beslemeli kontrolcü devreye girmektedir ve sarkacı kararsız denge noktasında tutmaktadır. Böylece, anahtarlamalı bir kontrol yapısı elde edilmektedir. Önerilen kontrol algoritmasının doğrulaması için önerilen algoritma bir döner sarkacı üzerinde gerçek zamanlı olarak test edilmiştir. Farklı kol ivmelendirme değerleri ile elde edilen karşılaştırmalı sonuçlar sunulmuştur. Kol ivmesi ±32 rad/s2’den ±64 rad/s2’ye çıkarıldığında sarkacın yukarı yükselme süresi yaklaşık 9 s’den 5 s’ye düşmektedir. Bu sonuçlar, önerilen algoritmanın deneysel başarısını göstermektedir.

Point-to-point virtual model control of a flexible joint robotic manipulator using trajectories of motion

This paper considers the use of trajectories of motion as a means of addressing the problem of unwanted oscillations and improving steady-state accuracy in the point-to-point motion of a flexible joint robotic manipulator using a novel third-order Poynting–Thomson virtual model control. A differentially flat model of the manipulator was exploited for classical controller design. The trajectories of motion were generated through a Newton interpolation procedure. The conditions for motion from rest to rest were derived and used to generate the trajectories required to steer the manipulator from one static point to another. Simulation and laboratory experiments confirmed that with the use of the trajectories of motion, rest-to-rest displacement is achieved without the unwanted swings about the final position. A marginal improvement in trajectory tracking performance was also verified by comparing it with the classical controller and state feedback controller. Moreover, the new controller showed better external disturbance rejection than the classical flat controller.

Perturbation estimation based single-loop decoupling control strategy for DC-based DFIG to augment MPPT and FRT capabilities

Perturbation estimation based single-loop decoupling control strategy for DC-based DFIG to augment MPPT and FRT capabilities

Inferential control strategies using neural soft sensor in a high purity distillation column

High-purity distillation columns are processes in which you want to minimally increase the purity of a key component by separation. These processes are sensitive to disturbances, where small changes in the feed flowrate stream cause drastic changes in product compositions. Furthermore, when one wants to apply traditional control and optimization techniques to these processes, some difficulties have to be faced: the process is generally non-linear and has a long response time, there are many immeasurable disturbances, it is difficult to keep the process in steady-state, and the bottom and top compositions are highly coupled. Therefore, this paper presents a methodology for the development of soft sensors (SS), here in applied to an industrial 1,2-Dichloroethane separation plant. The process was simulated and validated with real data from the industrial plant. In the step second an algorithm based on multivariate statistics was developed for the selection of inputs SS variables. The configuration for training multilayer artificial neural network (ANN's) was optimize, accounting for the ANN's prediction capacity of responses and the rejection of disturbances inserted in the process. Thereafter, a temperature control was implemented to maintain the impurity compositions within specifications but failed due to the small temperature variations in the high purity column. To overcome these difficulty three inferential control strategies (impurity composition estimated by the SS) were implemented: a classic feedback control, a cascade control, and a ratio-cascade control. All control strategies acted to minimize the process disturbances effects. However, the inferential ratio-cascade control shown to be the most robust, because of the lowest integral error criteria value and percentage overshoot.

Using a Flywheel to Stabilize a Self-Balancing Bicycle

The designs of two linear control systems approach to stabilize the balance of an unmanned bicycle system are presented. Both approaches are based on the use of a reaction wheel or flywheel to balance the bicycle. The two linear control approaches, based on the linearization of a nonlinear model obtained using Lagrange formalism, are the classic linear controllers, PID and State Feedback control. The performance of both controllers is verified by digital simulation and real-time experimental results.

Sufficient active control of uncertain low-frequency space micro-vibrations near measurement limit of acceleration sensors

Sufficient active control of uncertain low-frequency space micro-vibrations near measurement limit of acceleration sensors

Experimental study on an enhanced integral force feedback controller for active damping

In this paper, an enhanced integral force feedback controller is proposed for implementing active damping. Compared with the classical integral force feedback, the pure integrator is replaced with a combination of a first-order low-pass filter and a proportional term. The control performance of the proposed controller is examined on a single-degree-of-freedom host system. In order to better understand the physics behind, a full equivalent mechanical model is derived. It is found that the first-order low-pass filter is mechanically equivalent to a spring and a damper connected in parallel. The proportional term mechanically represents a spring which is connected in series with the inherent actuator spring. The optimal control parameters are numerically obtained with the ℋ∞ optimisation criterion. The analysis shows that the optimal feedback gain can be practically taken the same as that of the classical integral force feedback controller in the sense that the difference between the resultant control performance is negligible. The numerical study is also experimentally validated. The obtained results are found to correspond well with the theoretical developments.

STABILITY ROBUSTNESS OF CONTROL SYSTEMS OF MULTIROTOR UNMANNED AERIAL VEHICLES

Multirotor unmanned aerial vehicles (UAVs) are broadly used in various military and civilian areas and tasks. In real flights of the multirotor UAVs, certain unexpected situations may occur, which can lead to improper functionality and even failures of elements or devices of the UAV’s control system. In some cases, this can even lead to the complete collapse of the UAV. Therefore, the safety of UAV flights is nowadays of prime importance. A method of analysis of the stability robustness of the UAV’s control systems with respect to the parameters multiplicative uncertainties (or perturbations), including possible efficiency degradation of DC motors, is proposed in the paper. Stability robustness is determined by a simple graphical procedure on the complex plane of the Nyquist or Nichols hodographs of the control system’s separate channels. That procedure is quite similar to the well-known classical feedback control procedure of determining the peak gain of the single-input, single-output closed-loop control system by the Nyquist hodograph of the open-loop system. A numerical example illustrating the proposed method is given.

Iterative learning control for piecewise arc path tracking with validation on a gantry robot manufacturing platform

The piecewise arc path tracking problem is a common feature of manufacturing systems operating in a repetitive mode, e.g. assembly production lines. Here, the system end-effector must follow a spatial path without any specific temporal tracking constraints, which makes the temporal profile not fixed a priori. The technique of iterative learning control (ILC) is well-suited to handle this problem, since compared to classical feedback control methods, ILC is capable of learning from previous trial information to minimize the tracking error over repeated trials. This paper extends the ILC task description to address piecewise arc path tracking tasks, and further formulates a more general design framework than existing spatial ILC approaches. A comprehensive ILC algorithm is designed to handle this class of piecewise arc path tracking problems, and practical implementation instructions are provided. Validation is conducted on a gantry robot manufacturing testbed to confirm its feasibility and efficiency in practice with a comparison to existing methods showing its higher path tracking accuracy.

Robustness of energy landscape controllers for spin rings under coherent excitation transport

Abstract The design and analysis of controllers to regulate excitation transport in quantum spin rings presents challenges in the application of classical feedback control techniques to synthesize effective control and generates results in contradiction to the expectations of classical control theory. This paper examines the robustness of controllers designed to optimize the fidelity of an excitation transfer to uncertainty in system and control parameters. We use the logarithmic sensitivity of the fidelity error as the robustness measure, drawing on the classical control analog of the sensitivity of the tracking error. Our analysis shows that quantum systems optimized for coherent transport demonstrate significantly different correlation between error and the log-sensitivity depending on whether the controller is optimized for readout at an exact time T or over a time-window T ± Δ/2.

A methodology to implement a closed-loop feedback-feedforward level control in a laboratory-scale flotation bank using peristaltic pumps

This paper describes the implementation of a level control strategy in a laboratory-scale flotation system. The laboratory-scale system consists of a bank of three flotation tanks connected in series, which mimics a flotation system found in mineral processing plants. Besides the classical feedback control strategy, we have also included a feedforward strategy to better account for process disturbances. Results revealed that the level control performance significantly improves when a feedforward strategy is considered. This methodology uses peristaltic pumps for level control, which has not been extensively documented even though: (1) peristaltic pumps are commonly used in laboratory-scale systems, and (2) the control implementation is not as straightforward as those control strategies that use valves. Therefore, we believe that this paper, which describes a proven methodology that has been validated in an experimental system, can be a useful reference for many researchers in the field.•Preparation of reagents to ensure that the froth stability of the froth layer is representative of an industrial flotation froth.•Calibration of instruments - convert the electrical signal from PLCs to engineering units.•Tuning PI parameters using SIMC rules by performing step-changes in each flotation cell.

Active Disturbance Rejection Control for Static Power Converters in Flexible AC Traction Power Supply Systems

Flexible traction power supply systems (FTPSSs) can solve problems in traditional systems and envision a promising future for rail transit power supply. The traction network voltage is determined by static power converters (SPCs), so the voltage control of SPCs is very important for the safe and stable operation of the system. The uncertainty of the train load, the power redistribution in the case of SPC disconnections from the network because of SPC internal faults, and the real-time cooperative control of multiple SPCs bring challenges to the voltage control of SPCs. Compared to classical error feedback control, active disturbance rejection control (ADRC) has better transient performance and immunity. An ADRC-based voltage-current dual-loop control strategy for SPCs is proposed in this paper. The equivalent block diagram and transfer function are deduced. The stability and immunity of the system are analyzed. Finally, the effectiveness and superiority of the proposed control strategy under different operating conditions are verified by simulation and experiment.

Enhanced Observer Control of a Cubic Stewart Platform With Image Sensor Measurement

A cubic Stewart platform with an image sensor meausring is proposed for a line-of-sight (LOS) pointing and stabilization. The charge couple device (CCD) as a image sensor mounted on the Stewart platform can provide the LOS error only in the azimuth and elevation directions, so a decoupled control strategy through the transformation of the Jacobian matrix is introduced to transform a Multiple-Input Multiple-Output (MIMO) control into Single-Input Single-Output (SISO) control. Due to the low bandwidth of the Stewart platform and the finite sampling rate of the CCD, the closed-loop performance is greatly restricted. An optimal observer control based on Youla parameterization is proposed to improve the tracking performance in the CCD-based Stewart platform control system. This improved controller relies on the LOS error combined with the controller output to produce high gain, plugging into the tradditional closed-loop structure, which results in minimizing the error attenuation function by designing an appropriate Q-filter. Both the simulations and experiments are carried out to prove the effectiveness of the proposed observer compared with the classical feedback controller.

Motion control with optimal nonlinear damping: From theory to experiment

Optimal nonlinear damping control was recently introduced for the second-order SISO systems, showing some advantages over a classical PD feedback controller. This paper summarizes the main theoretical developments and properties of the optimal nonlinear damping controller and demonstrates, for the first time, its practical experimental evaluation. An extended analysis and application to more realistic (than solely the double-integrator) motion systems are also given in the theoretical part of the paper. As comparative linear feedback controller, a PD one is taken, with the single tunable gain and direct compensation of the plant time constant. The second, namely experimental, part of the paper includes the voice-coil drive system with relatively high level of the process and measurement noise, for which the standard linear model is first identified in frequency domain. The linear approximation by two-parameters model forms the basis for designing the PD reference controller, which fixed feedback gain is the same as for the optimal nonlinear damping control. A robust sliding-mode based differentiator is used in both controllers for a reliable velocity estimation required for the feedback. The reference PD and the proposed optimal nonlinear damping controller, both with the same single design parameter, are compared experimentally with respect to trajectory tracking and disturbance rejection.

State feedback based on grey wolf optimizer controller for two-wheeled self-balancing robot

Abstract The two-wheeled self-balancing robot (TWSBR) is based on the axletree and inverted pendulum. Its balancing problem requires a control action. To speed up the response of the robot and minimize the steady state error, in this article, a grey wolf optimizer (GWO) method is proposed for TWSBR control based on state space feedback control technique. The controller stabilizes the balancing robot and minimizes the overshoot value of the system. The dynamic model of the system is derived based on Euler formula and linearized to state space representation to enhance the control technique. Then, the GWO optimizes the state feedback controller parameters. Simulation results show that the system reaches the zero steady-state error with less than 2 ms, which proves the effectiveness of the proposed controller over the classical state feedback controller in terms of fast response, very small overall error, and minimum overshoot.

Adaptive neural dynamic surface control for uniform energy exploitation of floating wind turbine

To avoid the harmful effects of global warming on the earth planet and its atmosphere, the expansion of wind energy consumption based on new control techniques leads to improved energy capacity. The development of wind power plants on the sea is more efficient owing to the stronger wind flows in comparison with the onshore structure. Furthermore, stable installation of the offshore wind energy base meets the energy management requirements of today's consumers. As a result, dependency on fossil fuels as the main source of global warming decreases. The feedback control system and on-line sensor signals provide the stability and increased efficiency of the floating wind turbine. The aggressive environment and large size structure of the turbine lead to entering uncertainty in the turbine model and exogenous noises that should not be effectively compensated by conventional control methods. Therefore, a radial based functional neural network (RBFNN) controller is proposed to estimate and compensate the effect of uncertainty on feedback control of the wind turbine. For comparison purposes, a linear quadratic regulator (LQR) state feedback is also developed for optimal control of the floating wind turbines. The neural network adaptively determines the upper bound of uncertainty/noise. Therefore, conservative high-gain control actions to achieve robustness of the classical feedback controllers against structural deviations of the turbine body are decreased. In our newly proposed adaptive control algorithm, using a basic raised cosine function guides to restore the computational efficiency of RBFNN. With the Lyapunov-based stability analysis, the final limits of the closed-loop system and the convergence caused by the terminal sliding mode (TSM) tracking error are determined. Detailed software simulations of both the LQR and the designed RBFNN control systems indicate the superiority of the neural network-based approach.

A robust model-free controller for a three-phase grid-connected photovoltaic system based on ultra-local model

In this paper, a robust model-free controller for a grid-connected photovoltaic (PV) system is designed. The system consists of a PV generator connected to a three-phase grid by a DC/AC converter. The control objectives of the overall system are to extract maximum power from the PV source, to control reactive power exchange and to improve the quality of the current injected into the grid. The model-free control technique is based on the use of an ultra-local model instead of the dynamic model of the overall system. The local model is continuously updated based on a numerical differentiator using only the input–output behavior of the controlled system. The model-free controller consists of a classical feedback controller and a compensator for the effects of internal parameter changes and external disturbances. Simulation results illustrate the efficiency of the controller for grid-connected PV systems.

Feedback Control of Melt Pool Area in Selective Laser Melting Additive Manufacturing Process

Selective laser melting (SLM), a metal powder fusion additive manufacturing process, has the potential to manufacture complex components for aerospace and biomedical implants. Large-scale adaptation of these technologies is hampered due to the presence of defects such as porosity and part distortion. Nonuniform melt pool size is a major cause of these defects. The melt pool size changes due to heat from the previous powder bed tracks. In this work, the effect of heat sourced from neighbouring tracks was modelled and feedback control was designed. The objective of control is to regulate the melt pool cross-sectional area rejecting the effect of heat from neighbouring tracks within a layer of the powder bed. The SLM process’s thermal model was developed using the energy balance of lumped melt pool volume. The disturbing heat from neighbouring tracks was modelled as the initial temperature of the melt pool. Combining the thermal model with disturbance model resulted in a nonlinear model describing melt pool evolution. The PID, a classical feedback control approach, was used to minimize the effect of intertrack disturbance on the melt pool area. The controller was tuned for the desired melt pool area in a known environment. Simulation results revealed that the proposed controller regulated the desired melt pool area during the scan of multiple tracks of a powder layer within 16 milliseconds and within a length of 0.04 mm reducing laser power by 10% approximately in five tracks. This reduced the chance of pore formation. Hence, it enhances the quality of components manufactured using the SLM process, reducing defects.