Showing papers on "Feedback linearization published in 2018"

Combined Speed and Current Terminal Sliding Mode Control With Nonlinear Disturbance Observer for PMSM Drive

Abstract: A terminal sliding mode control (SMC) method based on nonlinear disturbance observer is investigated to realize the speed and the current tracking control for the permanent magnet synchronous motor (PMSM) drive system in this paper. The proposed method adopts the speed-current single-loop control structure instead of the traditional cascade control in the vector control of the PMSM. First, considering the nonlinear and the coupling characteristic, a single-loop terminal sliding mode controller is designed for PMSM drive system through feedback linearization technology. This method can make the motor speed and current reach the reference value in finite time, which can realize the fast transient response. Although the SMC is less sensitive to parameter uncertainties and external disturbance, it may produce a large switching gain, which may cause the undesired chattering. Meanwhile, the SMC cannot keep the property of invariance in the presence of unmatched uncertainties. Then, a nonlinear disturbance observer is proposed to the estimate the lump disturbance, which is used in the feed-forward compensation control. Thus, a composite control scheme is developed for the PMSM drive system. The results show that the motor control system based on the proposed method has good speed and current tracking performance and strong robustness.

Robust feedback linearization for nonlinear processes control

Abstract: In this research, a robust feedback linearization technique is studied for nonlinear processes control. The main contributions are described as follows: 1) Theory says that if a linearized controlled process is stable, then nonlinear process states are asymptotically stable, it is not satisfied in applications because some states converge to small values; therefore, a theorem based on Lyapunov theory is proposed to prove that if a linearized controlled process is stable, then nonlinear process states are uniformly stable. 2) Theory says that all the main and crossed states feedbacks should be considered for the nonlinear processes regulation, it makes more difficult to find the controller gains; consequently, only the main states feedbacks are utilized to obtain a satisfactory result in applications. This introduced strategy is applied in a fuel cell and a manipulator.

Feedback Linearization-Based Current Control Strategy for Modular Multilevel Converters

Abstract: Modular multilevel converters (MMCs) are multi-input multi-output (MIMO) nonlinear systems. The control systems for MMCs are required to simultaneously achieve multiple control objectives, e.g., output current regulation, submodule capacitor voltage control, and circulating ripple currents suppression. Existing cascaded control strategies for MMCs achieve those control objectives with relatively complex controllers, and the controller parameter design is normally difficult for such nonlinear systems with highly coupled states. In view of this, a feedback linearization-based current control strategy is proposed for an MMC system in this paper. The nonlinear state function model of the MMC is presented and transformed to a linearized and decoupled form with the help of the input–output feedback linearization technique. Based on the linearized system, simple linear controllers are employed to regulate the output and inner differential currents of the MMC, which significantly reduces the difficulty in controller design. The stability of the proposed control strategy is analyzed. The experimental verification results show that, compared to the conventional cascaded control strategies for MMCs, the proposed feedback linearization control strategy is able to achieve improved steady-state and dynamic performances. The robustness of the proposed control strategy against parametric uncertainties is experimentally investigated.

Neural Lander: Stable Drone Landing Control using Learned Dynamics

Abstract: Precise near-ground trajectory control is difficult for multi-rotor drones, due to the complex aerodynamic effects caused by interactions between multi-rotor airflow and the environment. Conventional control methods often fail to properly account for these complex effects and fall short in accomplishing smooth landing. In this paper, we present a novel deep-learning-based robust nonlinear controller (Neural Lander) that improves control performance of a quadrotor during landing. Our approach combines a nominal dynamics model with a Deep Neural Network (DNN) that learns high-order interactions. We apply spectral normalization (SN) to constrain the Lipschitz constant of the DNN. Leveraging this Lipschitz property, we design a nonlinear feedback linearization controller using the learned model and prove system stability with disturbance rejection. To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets. Experimental results demonstrate that the proposed controller significantly outperforms a Baseline Nonlinear Tracking Controller in both landing and cross-table trajectory tracking cases. We also empirically show that the DNN generalizes well to unseen data outside the training domain.

A Cyber-Physical Control Framework for Transient Stability in Smart Grids

Abstract: Denial of service attacks and communication latency pose challenges for the operation of control systems within power systems. Specifically, excessive delay between sensors and controllers can substantially worsen the performance of distributed control schemes. In this paper, we propose a framework for delay-resilient cyber-physical control of smart grid systems for transient stability applications. The proposed control scheme adapts its structure depending on the value of the latency. As an example, we consider a parametric feedback linearization (PFL) control paradigm and make it “cyber-aware.” A delay-adaptive design that capitalizes on the features of PFL control is presented to enhance the time-delay tolerance of the power system. Depending on the information latency present in the smart grid, the parameters and the structure of the PFL controller are adapted accordingly to optimize performance. The improved resilience is demonstrated by applying the PFL controller to the New England 39-bus and WECC 9-bus test power systems following the occurrence of physical and cyber disturbances. Numerical results show that the proposed cyber-physical controller can tolerate substantial delays without noticeable performance degradation.

Optimal Oxygen Excess Ratio Control for PEM Fuel Cells

Abstract: In this paper, a feedback linearization controller is proposed for the van compressor in the air supply system of a proton exchange membrane fuel cell. The control goal is to avoid oxygen starvation and reduce power consumption by tracking an optimal reference oxygen excess ratio. Specifically, an improved control-oriented third-order model of the air supply system is proposed with the model identification of the air compressor. The optimal reference oxygen excess ratio is obtained from experiments to maintain a maximum net power. Based on the air supply system model, a nonlinear controller is designed to track the optimal oxygen excess ratio using feedback linearization. Lyapunov-based technique is utilized to analyze the stability of the closed-loop system. Effectiveness of the proposed approach is illustrated by experimental results.

Control of an LLC Resonant Converter Using Load Feedback Linearization

Abstract: LLC resonant converter is a nonlinear system, limiting the use of typical linear control methods. This paper proposed a new nonlinear control strategy, using load feedback linearization for an LLC resonant converter. Compared with the conventional PI controllers, the proposed feedback linearized control strategy can achieve better performance with elimination of the nonlinear characteristics. The LLC resonant converter's dynamic model is built based on fundamental harmonic approximation using extended describing function. By assuming the dynamics of resonant network is much faster than the output voltage and controller, the LLC resonant converter's model is simplified from seven-order state equations to two-order ones. Then, the feedback linearized control strategy is presented. A double loop PI controller is designed to regulate the modulation voltage. The switching frequency can be calculated as a function of the load, input voltage, and modulation voltage. Finally, a 200 W laboratory prototype is built to verify the proposed control scheme. The settling time of the LLC resonant converter is reduced from 38.8 to 20.4 ms under the positive load step using the proposed controller. Experimental results prove the superiority of the proposed feedback linearized controller over the conventional PI controller.

A Voltage Modulated DPC Approach for Three-Phase PWM Rectifier

Abstract: In this paper, a voltage modulated direct power control for three-phase pulsewidth modulated rectifier is proposed. With the suggested method, the differential equations describing the rectifier dynamics are changing from a linear time-varying system into a linear time-invariant one. In this way, conventional feedback and feedforward controllers are applicable for the independent control of active and reactive powers. The proposed method has guaranteed that the closed system is globally exponentially stable. A feedback linearization method is also employed for generating the active power reference of inner loops. Finally, some experimental tests are conducted to verify its effectiveness.

Adaptive Speed Control of Induction Motor Drive With Inaccurate Model

Abstract: An adaptive controller for the speed control of induction motor (IM) drives with inaccurate models is designed in this paper. Specifically, we assume that every equation in the state-space model of the drive is subject to slowly varying error. The proposed controller is composed of an adaptive feedforward control term, which compensates for the nonlinear and uncertain factors, and a feedback control term, which guarantees the system stability. The proposed scheme is not only simple and easy to implement, but also it guarantees a precise and fast speed tracking. Stability of the proposed speed controller is confirmed using the Lyapunov theorem and a related lemma. The designed control algorithm is compared to a controller based on nonadaptive feedback linearization control (FLC), conventional field oriented control (FOC), and adaptive backstepping sliding mode control (ABSMC). Experiments on a developed IM drive prototype of rated power of 4 kW confirm good control performance [better robustness, smaller mean square, and maximum absolute errors (MAEs)] compared to the competitors, especially in the case of severe parameter mismatch between the real drive and model used for controller design.

Optimized adaptive tracking control for an underactuated vibro-driven capsule system

Abstract: This paper studies the issue of adaptive trajectory tracking control for an underactuated vibro-driven capsule system and presents a novel motion generation framework. In this framework, feasible motion trajectory is derived through investigating dynamic constraints and kernel control indexes that underlie the underactuated dynamics. Due to the underactuated nature of the capsule system, the global motion dynamics cannot be directly controlled. The main objective of optimization is to indirectly control the friction-induced stick–slip motions to reshape the passive dynamics and, by doing so, to obtain optimal system performance in terms of average speed and energy efficacy. Two tracking control schemes are designed using a closed-loop feedback linearization approach and an adaptive variable structure control method with an auxiliary control variable, respectively. The reference model is accurately matched in a finite-time horizon. The key point is to define an exogenous state variable whose dynamics is employed as a control input. The tracking performance and system stability are investigated through rigorous theoretic analysis. Extensive simulation studies are conducted to demonstrate the effectiveness and feasibility of the developed trajectory model and optimized adaptive control system.

Distributed Model Predictive Secondary Voltage Control of Islanded Microgrids With Feedback Linearization

Abstract: This paper presents a novel distributed secondary control method for both voltage and frequency regulation in islanded microgrids Firstly, the large-signal dynamic model of inverter-interfaced distributed generation (DG) is formulated in the form of a multi-input multi-output nonlinear system, which can be converted to a partly linear one using input–output feedback linearization Then, the linear-distributed model predictive controller is designated in each DG to realize the secondary voltage control by incorporating the forecasted behaviors of the local and neighboring DG units Through the receding optimization index of every update process, the implementation of optimal control action accelerates the convergence rate for voltage magnitudes to the reference value Following, after transforming the nonlinear DG dynamics into a first-order linear system, a distributed proportional integral algorithm is introduced in the frequency restoration while maintaining the accurate active power sharing Our approach utilizes the distributed architecture, which indicates superior reliability and flexibility compared to the centralized manner; moreover, it can accommodate diverse uncertainties in communication links, model parameters, and time delays Simulation results are provided to verify the effectiveness of the proposed control methodology

A Novel Design of PI Current Controller for PMSG-Based Wind Turbine Considering Transient Performance Specifications and Control Saturation

Abstract: This paper presents a novel design process of decoupled PI current controller for permanent magnet synchronous generator (PMSG)-based wind turbines feeding a grid-tied inverter through a back-to-back converter. Specifically, the design methodology consists of combining disturbance observer-based control (DOBC) with feedback linearization (FBL) technique to ensure nominal transient performance recovery under model uncertainty. By simplifying the DOBC under the feedback linearizing control, it is shown that the composite controller reduces to a decoupled PI current controller plus an additional term that has the main role of recovering the nominal transient performance of the FBL, especially under step changes in the reference. Additionally, an antiwindup compensator arises naturally into the controller when considering the control input saturation to design the DOBC. This permits removal of the effect of the saturation blocks required to limit the control input. The proposed control scheme is implemented and validated through experimentation conducted on 22-pole, 5 kW PMSG. The results revealed that the proposed technique can successfully achieve nominal performance recovery under model uncertainty as well as improved transient performances under control saturation.

Adaptive Nonlinear Disturbance Observer Using a Double-Loop Self-Organizing Recurrent Wavelet Neural Network for a Two-Axis Motion Control System

Abstract: This paper proposes an adaptive nonlinear disturbance observer (ANDO) for identification and control of a two-axis motion control system driven by two permanent-magnet linear synchronous motors servo drives. The proposed control scheme incorporates a feedback linearization controller (FLC), a new double-loop self-organizing recurrent wavelet neural network (DLSORWNN) controller, a robust controller, and an ${{\mathcal H}_\infty }$ controller. First, an FLC is designed to stabilize the XY table system. Then, a nonlinear disturbance observer (NDO) is designed to estimate the nonlinear lumped parameter uncertainties that include the external disturbances, cross-coupled interference, and frictional force. However, the XY table performance is degraded by the NDO error due to parameter uncertainties. To improve the robustness, the ANDO is designed to attain this purpose. In addition, the robust controller is designed to recover the approximation error of the DLSORWNN, while the ${{\mathcal H}_\infty }$ controller is specified such that the quadratic cost function is minimized and the worst-case effect of the NDO error must be attenuated below a desired attenuation level. The online adaptive control laws are derived using the Lyapunov stability analysis and ${{\mathcal H}_\infty }$ control theory, so that the stability of the ANDO can be guaranteed. The experimental results show the improvements in disturbance suppression and parameter uncertainties, which illustrate the superiority of the ANDO control scheme.

Optimal Feedback Linearization Control of Interior PM Synchronous Motors Subject to Time-Varying Operation Conditions Minimizing Power Loss

Abstract: This paper presents development of an optimal feedback linearization control for interior permanent magnet synchronous machines operating in a nonsteady-state operating point, i.e., varying torque and speed, to achieve precision tracking performance and energy saving by minimizing the copper loss. An isomorphism mapping between the dq axes phase voltages and two auxiliary control inputs over full ranges of torque and speed is established by the linearization controller using the notion of orthogonal projection. The auxiliary control inputs are defined to be exclusively responsible for torque generation and power consumption. Subsequently, an analytical solution for the optimal-linearization control is derived in a closed form by applying the Hamiltonian of optimal control theory in conjunction with the Pontryagin's minimum principle. The optimal controller takes the maximum voltage limit and torque tracking constraint into account while maximizing machine efficiency for nonconstant operational load torque and speed. Unlike the convectional quadratic regulator-based control of electric motors, the proposed control approach does not rely on steady-state operation conditions and hence, it is suitable for such applications as electric vehicles and robotics. Experimental results demonstrate torque-tracking and energy-efficiency performance of a motor operating with nonconstant torque.

PMSM vector control techniques — A survey

Abstract: This paper reviews permanent magnet synchronous motor vector control techniques and existing classifications of such techniques. This paper offers a generalized classification of vector control techniques that combines various principles of vector control and speed control approaches. Methods listed in the classification are characterized by their basic qualitative characteristics. On the basis of comparative analysis of techniques recommendations are given on their practical application.

Flatness-Based Model Predictive Control for Quadrotor Trajectory Tracking

Abstract: The use of model predictive control for quadro-tor applications requires balancing trajectory tracking performance and constraint satisfaction with fast computation. This paper proposes a Flatness-based Model Predictive Control (FMPC) approach that can be applied to quadrotors, and more generally, differentially flat nonlinear systems. Our proposed FMPC couples feedback model predictive control with feedforward linearization. The proposed approach has the computational advantage that, similar to linear model predictive control, it only requires solving a convex quadratic program instead of a nonlinear program. However, unlike linear model predictive control, we still account for the nonlinearity in the model through the use of an inverse term. In simulation, we demonstrate improved robustness over approaches that couple model predictive control with feedback linearization. In experiments using quadrotor vehicles, we also demonstrate improved trajectory tracking compared to classical linear and nonlinear model predictive control approaches.

Nonlinear fuzzy fault-tolerant control of hypersonic flight vehicle with parametric uncertainty and actuator fault

Abstract: This study presents a nonlinear fuzzy fault-tolerant control (FTC) and a fault observer for longitudinal dynamics of hypersonic flight vehicle (HFV) with parameter uncertainty and actuator gain loss fault via sliding-mode and backstepping theory. An affine nonlinear dynamic model of HFV with parameter uncertainty and actuator fault is established based on feedback linearization technology. A nominal sliding-mode control is developed to track the command of altitude and velocity. Unknown nonlinear functions in the controller are approximated by fuzzy logic system through updating the weight parameters online. In view of the occurrence of actuator fault, a backstepping sliding-mode observer is constructed to estimate the fault. A nonlinear fuzzy FTC is then designed with the estimate fault obtained from the observer to address the problem of actuator fault and parameter uncertainty. The stability of the controller is analyzed utilizing Lyapunov theory. Numerical simulation results demonstrate the validity and robustness of the proposed controller and observer.

Active disturbance rejection control based robust output feedback autopilot design for airbreathing hypersonic vehicles.

Abstract: Since motion control plant ( y ( n ) = f ( ⋅ ) + d ) was repeatedly used to exemplify how active disturbance rejection control (ADRC) works when it was proposed, the integral chain system subject to matched disturbances is always regarded as a canonical form and even misconstrued as the only form that ADRC is applicable to. In this paper, a systematic approach is first presented to apply ADRC to a generic nonlinear uncertain system with mismatched disturbances and a robust output feedback autopilot for an airbreathing hypersonic vehicle (AHV) is devised based on that. The key idea is to employ the feedback linearization (FL) and equivalent input disturbance (EID) technique to decouple nonlinear uncertain system into several subsystems in canonical form, thus it would be much easy to directly design classical/improved linear/nonlinear ADRC controller for each subsystem. It is noticed that all disturbances are taken into account when implementing FL rather than just omitting that in previous research, which greatly enhances controllers' robustness against external disturbances. For autopilot design, ADRC strategy enables precise tracking for velocity and altitude reference command in the presence of severe parametric perturbations and atmospheric disturbances only using measurable output information. Bounded-input-bounded-output (BIBO) stable is analyzed for closed-loop system. To illustrate the feasibility and superiority of this novel design, a series of comparative simulations with some prominent and representative methods are carried out on a benchmark longitudinal AHV model.

Distributed Adaptive Longitudinal Control for Uncertain Third-Order Vehicle Platoon in a Networked Environment

Abstract: The longitudinal control for a platoon of connected and automated vehicles is a popular topic in transportation engineering, nowadays. However, a majority of existing results about the cooperative/distributed platoon control are based on linear models that are derived from nonlinear vehicular dynamics by exact feedback linearization. This nonlinear–linear transformation asks for a complete priori knowledge of vehicular dynamics, which could be difficult to obtain in practice. To overcome this disadvantage and address multiuncertainties including both unknown plant parameters and unknown control coefficients in third-order vehicular node dynamics, this paper proposes a distributed backstepping control scheme in a networked environment. Unknown parameters are identified online, and both internal stability and string stability for constant distance spacing policy are established. Simulation studies are carried out by comparing the adaptive control proposed in this paper with the robust control in the state-of-the-art algorithms.

Design of PI Controller Together With Active Damping for Grid-Tied LCL -Filter Systems Using Disturbance-Observer-Based Control Approach

Abstract: The main advantage of the LCL filter in grid-tied converters is its ability to achieve a better harmonic mitigation in comparison with L filter. A proportional–integral (PI) regulator can be considered as a good candidate for controlling the LCL -filter system, as it is more convenient for real-time implementation. However, PI controller is not capable of stabilizing the grid-tied LCL -filter system when the resonance frequency of the LCL network is below a certain value that mainly depends on the sampling frequency. Such a problem can be overcome by adding an active damping (AD) to the PI controller. This paper presents the design, implementation, and performance testing of a stable PI current controller for a grid-tied inverter using LCL filter. The design process is based on combining feedback linearization (FBL) technique with a disturbance-observer-based control (DOBC). It turns out that the composite controller, consisting of FBL and DOBC, reduces to a PI current controller associated with a linear state-feedback control that plays the role of an AD. In addition, an antiwindup compensator arises naturally into the controller when considering the limits on the control input. An important feature of the proposed PI current controller is its ability to retain the nominal transient response achieved with state-feedback control even in the presence of model uncertainty and external disturbances. The proposed controller was verified through simulation and experimental tests. The results demonstrated the ability of the controller to provide good transient and steady-state performances.

An Efficient Model Predictive Control for Trajectory Tracking of Wheeled Inverted Pendulum Vehicles with Various Physical Constraints

Abstract: This study presents a robust model predictive control (MPC) strategy to handle the trajectory tracking problem for a underactuated two-wheeled inverted pendulum (WIP) vehicle, in addition to taking various physical constraints into account. To begin with, a saturated trajectory generator is proposed to produce the desired velocities by which all posture tracking errors converge to the compact sets as well as the saturation of velocities being guaranteed. In addition, a MPC approach is put up forward after the approximate feedback linearization is performed to decrease the burden of computation and increase the realtime performance of the control system. Particularly, various physical constraints can be readily assured by the presented MPC method although the equilibrium of WIP vehicle is unstable. Meanwhile, to validate the robustness and availability of the proposed approach, initial errors, pulse disturbance and random noise are introduced to test the control performance of the closed-loop system in the simulation environment. The results from both theoretical analysis and simulation show that the proposed control strategy are effective and feasible for practical implementation.

The Hierarchical Operational Space Formulation: Stability Analysis for the Regulation Case

Abstract: The Operational Space Formulation (OSF) from the 1980s is probably the most frequently applied task-space controller in robotics. In multipriority control of redundant robots via the OSF, a feedback linearization is performed on the first hierarchy level while lower-priority tasks are executed in the dynamically consistent null space of the Jacobian matrices of all higher-priority tasks without disturbing them. However, it has been observed in the past that a formal stability analysis for the overall closed loop is rather difficult, especially for the null space dynamics. Except for exponential stability on the main task level, a complete proof is still missing when the tasks conflict with each other. Here, we provide this formal proof of asymptotic stability for the regulation case of a passivity-based OSF controller by means of conditional stability theory and semidefinite Lyapunov functions. Simulations support the intuitive, energy-based interpretation of the proof. This stability analysis lifts the widely used OSF onto a more solid foundation.

Output tracking of stochastic nonlinear systems with unstable linearization

Abstract: Summary This paper studies the output tracking problem for a class of stochastic nonlinear systems whose linearization parts may have unstable modes via output-feedback control. This is in contrast with most of the existing results where only state-feedback control is considered. On the basis of the homogeneous domination technique, an output tracking controller is designed. It is shown that the expectation of tracking error can be made arbitrarily small while all the states of the closed-loop system remain to be bounded in probability. Finally, a simulation example is given to illustrate the effectiveness of the tracking controller.