Feedforward Term Research Articles

A Novel Dominant Dynamic Elimination Control for Voltage-Controlled Inverter

The inverter control plays a critical role in microgrid (MG) hierarchical control structure. In this paper, a dominant dynamic elimination control is studied for the voltage-controlled MG inverter. A compact control structure is designed to include separated static state feedback and feedforward terms. Benefitting from the designed control structure, the dominant dynamic can be canceled by the static feedforward control via the fundamental zero-pole cancelation principle. Thanks to the cancelation of the dominant dynamic, the load disturbance-rejection performance is improved, and the approximate unity gain from the reference to output is also achieved from the perspective of the application control layer, thereby the performance of power control for the upper application is improved. Furthermore, the strong robustness to parameter variations and low total harmonic distortion are also achieved. Comparative simulations and experiments are adopted to verify the effectiveness and superiority of the designed approach.

Internal Model Current Control of Brushless Doubly Fed Induction Machines

In the wind energy generation system, the brushless doubly-fed induction machine (BDFIM) has shown significant application potential, since it eliminates the electric brush and slip ring. However, the complicated rotor structure increases the control difficulty, especially resulting in complicated coupled terms in the current sub-system, which deteriorates the dynamic performance and reduces the system robustness. In order to address the problems caused by complex coupled terms, an internal model current control strategy is presented for the BDFIM, and an active damping term is designed for suppressing the disturbance caused by the total resistance. The proposed method simplifies the controller parameters design, and it achieves the fast-dynamic response and the good tracking performance, as well as good robustness. On the other hand, the feedforward term composed by the grid voltage is added to the internal model controller in order to suppress the disturbance when the symmetrical grid voltage sag happens. Finally, the simulation and experimental results verify the feasibility and effectiveness of the proposed method.

Neural network-based asymptotic tracking control of unknown nonlinear systems with continuous control command

ABSTRACT This paper proposes a robust tracking controller for a class of nonlinear second-order systems with time-varying uncertainties. The controller is mainly based on the robust integral of the sign of the error (RISE) control approach to achieve an asymptotic stability result with a continuous control command in the presence of additive uncertainties. An adaptive feedforward neural network control term is blended with a new RISE controller to improve the system's transient performance. The proposed RISE controller is a modified version of the existing saturated RISE controller such that only sign of the derivative of the output is needed. The stability of the closed-loop system is well studied, where a local asymptotic stability is proven. The controller performance is validated through simulations on a two-degree-of-freedom lower limb robotic exoskeleton.

Evaluation of Compensatory Movement by Shoulder Joint Torque during Gain Adjustment of a Powered Prosthetic Wrist Joint.

Powered prostheses with low degree of freedom (DoF) have been developed for people with disabilities to assist daily tasks. These prostheses neglect the user's compensatory movements caused by the low degree of freedom. We assume that the movements can be reduced by well-designed controller of the devices. This paper explores an optimal control gain of the powered prosthesis to prevent the user from compensatory movements through experiments. In the experiments, we developed 1-DoF hand prosthesis with a position-controlled servo, which includes the constant gain as a feed-forward term. The compensatory movements are regarded as a joint torque at a shoulder (abduction/adduction). 4 intact subjects performed a pick-and-place task, using the prosthesis with several control gains. The empirical results show that there was the optimal gain for each subject, which reduces their compensatory movement.

Synchronised trajectory tracking for a network of MIMO non‐minimum phase systems with application to aircraft control

In this study, the problem of synchronised trajectory tracking for a network of multi-input–multi-output (MIMO) non-minimum phase (NMP) systems is addressed under a switching communication topology. First, an approach to convert the original system equation into a normal form is proposed, where a coordinate transformation matrix is introduced. Then, a control solution is developed, which is made up of a distributed observer network to generate an estimate of the reference system, a causal stable inversion to estimate the bounded state reference and feedforward term, and a local state-trajectory tracker to achieve simultaneously asymptotic output tracking and internal dynamics stabilisation. Finally, the solution is applied to the synchronised flight-path angle and velocity tracking problem of a network of F-16 aircraft, and further compared with the classic proportional–integral–derivative control-based approach used in many real-life applications. Simulation results demonstrate the effectiveness of the proposed solution, as well as the performance advantages owing to a systematic consideration of the NMP property.

Bounded adaptive output feedback tracking control for flexible-joint robot manipulators

This paper presents a bounded adaptive output feedback tracking control approach for flexible-joint robot manipulators with parametric uncertainties and bounded torque inputs, from a systematic perspective of different (weak or strong) joint flexibilities. The singular perturbation theory and integral manifold concept are applied to decouple the dynamics of flexible-joint robot manipulators into a slow subsystem and a fast subsystem. A class of saturation functions is used to make the control law bounded, ensuring the torque control inputs are within the output limitation of the joint actuators. An adaptive control law of the projection type is adopted to handle the feed-forward term of the slow sub-controller with parametric uncertainties. Meanwhile, an approximate differential filter and a high-gain observer are utilized in the slow and fast subsystems, respectively, to estimate the unmeasurable states, making the complete closed-loop control with only position measurements of motors and links. Importantly, a corrective control scheme is proposed to break through the traditional singular perturbation approach and to make it feasible for robot manipulators with strong joint flexibility. Furthermore, an all-round and strict stability analysis of the whole control system is given. Finally, simulation results verify the superior dynamic performance of the proposed approach.

Standalone Photovoltaic Water Pumping System Using Induction Motor Drive With Reduced Sensors

A simple and efficient solar photovoltaic (PV) water pumping system utilizing an induction motor drive (IMD) is presented in this paper. This solar PV water pumping system comprises two stages of power conversion. The first stage extracts the maximum power from a solar PV array by controlling the duty ratio of a dc–dc boost converter. The dc bus voltage is maintained by the controlling the motor speed. This regulation helps in the reduction of motor losses by reducing motor currents at higher voltage for the same power injection. To control the duty ratio, an incremental conductance based maximum power point tracking (MPPT) control technique is utilized. A scalar-controlled voltage source inverter serves the purpose of operating an IMD. The stator frequency reference of IMD is generated by the proposed control scheme. The proposed system is modeled, and its performance is simulated in detail. The scalar control eliminates the requirement of a speed sensor/encoder. Consequently, the need of motor current sensor is also eliminated. Moreover, the dynamics are improved by an additional speed feedforward term in the control scheme. The proposed control scheme makes the system inherently immune to the variation in the pump constant. The prototype of PV-powered IMD emulating the pump characteristics is developed in the laboratory to examine the performance under different operating conditions.

An acceleration-based hybrid learning-adaptive controller for robot manipulators

The robust periodic trajectory tracking problem is tackled by employing acceleration feedback in a hybrid learning-adaptive controller for n-rigid link robotic manipulators subject to parameter uncertainties and unknown periodic dynamics with a known period. Learning and adaptive feedforward terms are designed to compensate for periodic and aperiodic disturbances. The acceleration feedback is incorporated into both learning and adaptive controllers to provide higher stiffness to the system against unknown periodic disturbances and robustness to parameter uncertainties. A cascaded high gain observer is used to obtain reliable position, velocity and acceleration signals from noisy encoder measurements. A closed-loop stability proof is provided where it is shown that all system signals remain bounded and the proposed hybrid controller achieves global asymptotic position tracking. Results obtained from a high fidelity simulation model demonstrate the validity and effectiveness of the developed hybrid controller.

Decentralized Feedback Control of Pumping Losses and NOx Emissions in Diesel Engines

A novel decentralized control architecture is developed based on a feedback from the pressure difference across the engine which is responsible for the pumping losses and the exhaust gas recirculation (EGR) flow in diesel engines. The controller is supplemented with another feedback loop based on NOx emissions measurement. Aiming for simple design and tuning, the two control loops are designed and discussed: one manipulates the variable geometry turbine (VGT) actuator and the other manipulates the EGR valve. An experimentally validated mean-value diesel engine model is used to analyze the best pairing of actuators and set points. Emphasis is given to the robustness of this pairing based on gain changes across the entire operating region, since swapping the pairing needs to be avoided. The VGT loop is designed to achieve fast cylinder air charge increase in response to a rapid pedal tip-in by a feedforward term based on the real-time derivative of the desired boost pressure. The EGR loop relies on a feedback measurement from a NOx sensor and a real-time estimation of cylinder oxygen ratio, χcyl. The engine model is used for evaluating the designed controllers over the federal test procedure (FTP) for heavy duty (HD) vehicles. Results indicate that the control system meets all targets, namely fast air charge and χcyl control during torque transients, robust NOx control during steady-state operation, and controlled pumping losses in all conditions.

Mathematical Modeling and Control of Standalone DFIG-DC System in Rotor Flux Reference Frame

The mathematical modeling of doubly fed induction machine (DFIG)-dc system, including dominant harmonic (fifth and seventh), along with its control in rotor flux reference frame is presented in this paper. Modeling of the system provides the feedforward terms that consist of the fundamental and oscillatory components of the current. The use of nonderivative feedforward terms with proportional integral can reduce the steady-state current ripple, but for controlling the transient current ripple use of resonant controller is suggested such that the use of derivative feedforward terms can be avoided. The resonant controller is capable of controlling the current ripple by itself, but its use with suitable feedforward terms can enhance the performance of the system. DC voltage regulation of standalone DFIG-dc system is obtained by four combinations of above mentioned controllers and feedforward terms. A comparison is made on performance of these control structures. Feedforward terms used in the control structure require decoupled currents. Therefore, decoupling of various currents in different d-q axes are obtained with mathematical understanding. The proposed control schemes are verified on 5.5-KW slip ring induction machine for standalone application.

Double-Stage Three-Phase Grid-Integrated Solar PV System With Fast Zero Attracting Normalized Least Mean Fourth Based Adaptive Control

This paper deals with a control technique of a three-phase grid connected solar photovoltaic (SPV) system, which is based on the fast zero attracting normalized least mean fourth algorithm. This control algorithm achieves the objectives of mitigation of power quality issues such as harmonics reduction and power factor correction, together with extraction of peak power generated by the PV array. The system uses a PV array, a voltage-source converter (VSC), and linear and nonlinear loads. Here, VSC is connected to a PV array to transfer the active power to the three-phase load and the grid. The dependence on tuning of proportional-integral controller is reduced because of the feedforward term of the PV power. Due to this, the system dynamic response is improved, which makes the system quite robust. The system goals are to mitigate power quality problems and to provide current conditioning while operating in coherence with a weak distribution grid, which has poor quality of power in terms of voltage distortions with imbalances. MATLAB/Simulink is used to develop the model of the proposed system. The validation of proposed control is done at varying linear and nonlinear loads and under different environmental conditions on a prototype developed in the laboratory.

New effective PV battery charging algorithms

Energy security and independence is one of the most growing concerns around the globe, combined with the environmental impact of traditional energy sources, renewable energy systems have become of great importance. Photovoltaic (PV) systems are the most popular form of renewable energy, as price have become reasonable, and deployment is scalable. However, in many schemes, PV systems require rechargeable batteries for energy storage, and increased system dependability. In this paper a numerical solution and two new control methodologies are proposed for effective battery charging from PV systems.The first method is a modified single stage charge controller with a new maximum power point tracking (MPPT) algorithm. This method uses the battery voltage as a feed-forward term while optimizing the duty cycle of the converter to maximize the output power harvested from the PV cell.The second method is a modified two stage charge controller. The proposed approach optimizes the duty cycle of the second converter, which includes maximum power passage and maximum current into the battery.The main contribution of the proposed methods, is that the dynamic models of both the battery and the PV cell are taken into consideration, this greatly increases overall system efficiency.

Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories

In this paper, we prove that the dynamical model of a quadrotor subject to linear rotor drag effects is differentially flat in its position and heading. We use this property to compute feed-forward control terms directly from a reference trajectory to be tracked. The obtained feed-forward terms are then used in a cascaded, nonlinear feedback control law that enables accurate agile flight with quadrotors. Compared to state-of-the-art control methods, which treat the rotor drag as an unknown disturbance, our method reduces the trajectory tracking error significantly. Finally, we present a method based on a gradient-free optimization to identify the rotor drag coefficients, which are required to compute the feed-forward control terms. The new theoretical results are thoroughly validated trough extensive comparative experiments.

Model-based feedforward and cascade control of hydraulic McKibben muscles

McKibben artificial muscle actuators are predominantly pneumatically powered. Recently, hydraulic operation has gained interest due to its higher rigidity and efficiency. While there has been extensive control system development for pneumatic artificial muscles, little has been conducted hydraulically. This paper investigates three different controllers developed for a loaded robotic arm actuated with oil-hydraulic McKibben muscles. The goal was to achieve good angular position tracking over a range of frequencies up to 1 Hz. The first scheme, serving as the baseline, is a proportional-integral controller. The second architecture adds a nonlinear model-based feedforward term to the baseline controller; the feedforward includes the expected flowrate demands based on the actuator kinematics as well as the valve flow gain. The last scheme adds an inner pressure feedback loop to the second architecture. All controllers were evaluated with frequency and step response experiments. The results show that a simple proportional-integral controller has significant phase lag and attenuation at the higher frequencies tested; including the feedforward term almost completely eliminates these. The cascaded loop improves rise and settling times.

Observer based control scheme for DC-DC boost converter using sigma–delta modulator

PurposeIn actual application of a DC-DC boost converter, the input voltage and resistive load may be changed frequently, and these variations deteriorate the conventional controller performance. The purpose of this paper is to present an observer-based control scheme for a DC-DC boost converter with an unknown resistive load and input voltage.Design/methodology/approachTo estimate the unknown input voltage and resistive load, a nonlinear observer is designed by using the Lyapunov stability theorem. In addition, the closed-loop stability of the proposed control scheme for the DC-DC boost converter is proven. To convert the continuous control input to discrete mode, a sigma–delta modulator is used.FindingsThe proposed control scheme is validated in different situations. The adaptive structure of the proposed control scheme is tested by the input voltage, load and reference signal variation, and the simulation results confirm the capability of the proposed observer-based control strategy.Originality/valueThe contribution of this paper is twofold: according to nonlinear controller design, the feedforward term of the nonlinear controller is obtained via the observer, and unlike the proportional–integral controller, performance deterioration in the input voltage and load variations are unraveled. The effectiveness of this method is validated by experimental implementation in the presence of load and input voltage variations, and the experimental results confirm the efficacy of the proposed strategy.

Unbalanced harmonic power sharing and voltage compensation of microgrids using radial basis function neural network‐based harmonic power‐flow calculations for distributed and decentralised control structures

A new hierarchical control scheme is proposed to improve power sharing of multi-distributed energy resources (DERs) microgrids including non-linear and unbalanced loads. The electronically coupled DERs are responsible to perform the harmonic and unbalance compensation to reduce the voltage harmonics at the point of common coupling (PCC) and improve power quality. The proposed scheme uses a complementary control loop for small/large-signal stability enhancement, and moreover exploits new concept for fundamental and harmonic virtual impedance scheme for positive and negative sequences based on IEEE standards. Compared to conventional virtual impedance methods that add only line current feed-forward terms to the voltage reference, here, the line current and voltage at the PCC regulate the virtual impedance at fundamental and harmonic frequencies, respectively. So, mismatches in the feeder and line impedances are compensated. Also, a harmonic power calculation is presented based on the non-linear mapping capability of radial basis function neural networks to obtain voltage harmonics and active and reactive powers for balanced/unbalanced operation modes. To show the effectiveness of the proposed control scheme, offline time-domain simulation studies have been performed on a sample microgrid using MATLAB/Simulink software and OPAL-RT real-time digital simulator for verification.

Statistically optimized FOPID for output force control of SEAs

Fractional order PID (FOPID) controllers have recently found an increasing application in different fields of control. Comparing to traditional PID algorithms, FOPID controllers provide more flexibility and better performances. The simple and non-model-based structure of FOPID controllers has boosted their usage in real-world applications. However, due to having two more control parameters than regular PID controllers and the non-linear structure of FOPID controllers, the tuning procedure of these controllers is still a challenge. The authors of the present paper have recently proposed a Taguchi-based gain tuning algorithm for tuning of control parameters of FOPID controller. The present paper is an experimental evaluation of the proposed method. A custom made SEA, FUM-LSEA, is used as the test bed in this study. Deriving a dynamic model of the FUM-LSEA, feed-forward terms are added to the controller to compensate for disturbances from motions of the output block. Optimal gains and orders of the controller are obtained through a set of experiments suggested by the Taguchi method. The Taguchi optimized controller is also compared to a Ziegler–Nichols tuned controller. The experimental results indicate 45% improvements in force tracking error.

Decoupling Network Design for Inner Current Loops of Stand-Alone Brushless Doubly Fed Induction Generation Power System

Brushless doubly fed induction generator (BDFIG) is of high reliability due to its brushless structure. However, brushless structure also results in the complicated d-q vector model, in which the d and q channels are seriously coupled. Traditionally many feedforward (FF) terms were added during control for decoupling purpose. However the FF method features several drawbacks: 1) it requires extra sensors; 2) the rotor position information needs to be known for d-q transformation; and 3) the decoupling effect highly depends on the parameter accuracy. To overcome these problems, this letter proposes a decoupling method based on the decoupling network (DN). With DN method, the BDFIG and the load are first modeled as a dual-input-dual-output system, and then the DN is designed. From control point of view, the DN is in series with the BDFIG and the load, and thus, form a new control plant with highly decoupling feature. As a result, the controllers in d and q channels can be easily designed. The proposed DN method requires neither extra sensors nor rotor position information, and the robustness is enhanced since parameter variations of BDFIG and load can be fully considered during DN design. In this letter, such a DN method is presented and applied on the inner current loops of a stand-alone BDFIG system to obtain the good decoupling feature in the overall operational range, and the design results are verified by the experiments from a stand-alone BDFIG system platform.

Asymmetric Steering Hydrodynamics Identification of a Differential Drive Unmanned Surface Vessel

This paper identifies the asymmetric steering characteristics of a Wave Adaptive Modular Vessel (WAM-V) deployed as an Unmanned Surface Vessel (USV). Differentially steered propellers create a virtual rudder movement without explicitly inducing lateral rudder forces. However, a rotating propeller will generate a small lateral force, depending on its rotational direction and speed, also known as propeller walk. The WAM-V USV uses two similar propellers to manoeuvre, hence the propeller walk effects are doubled. Consequentially, the vessel has asymmetric turning characteristics which result in different steering behaviours when turning port or starboard. Heading measurements and virtual rudder movements suffice for identifying these turning characteristics at a certain speed. To do so, a first order Nomoto model was chosen as identification model. Three varieties of this model were identified: one for turning port, one for turning starboard, and one that averages the two aforementioned cases. These offline identified Nomoto models can serve multiple objectives. They can be used for simulation purposes, which themselves can be used to test control algorithms offline. Moreover, the coefficients of the Nomoto model itself can be used to tune a Proportional Integral Derivative (PID) controller. Finally, the Nomoto models can also be used as a feed forward term in control algorithms.

An Improved Adjustable Step Adaptive Neuron-Based Control Approach for the Grid-Supportive SPV System

A grid-supportive two-stage three-phase three-wire solar photovoltaic (SPV) system is presented in this paper, wherein a boost converter is used as a first stage to serve the function of maximum power point tracking and a three-leg voltage source converter is used to feed the extracted SPV energy, along with the supporting distribution system for improvement in the power quality. The harmonics elimination, grid currents balancing, and compensation for nonactive part of the load currents are extra features offered by the proposed system other than conventional features of the solar inverter. The true power reflecting part of the load current is estimated using an improved adjustable step adaptive neuron-based control approach. Moreover, a feed-forward term is added as photovoltaic (PV) array contribution to grid currents, which helps in fast dynamic response due to ambience changes. The output of which is a current component reflected on grid side to instantaneously regulate the dc-link voltage. In the proposed approach, the load, PV array, and loss contributions are kept decoupled. The feasibility of the proposed control algorithm is confirmed via experimental results.