Showing papers on "Feedback linearization published in 2020"

Cooperative Control of Heterogeneous Connected Vehicle Platoons: An Adaptive Leader-Following Approach

Abstract: Automatic cruise control of a platoon of multiple connected vehicles in an automated highway system has drawn significant attention of the control practitioners over the past two decades due to its ability to reduce traffic congestion problems, improve traffic throughput and enhance safety of highway traffic. This paper proposes a two-layer distributed control scheme to maintain the string stability of a heterogeneous and connected vehicle platoon moving in one dimension with constant spacing policy assuming constant velocity of the lead vehicle. A feedback linearization tool is applied first to transform the nonlinear vehicle dynamics into a linear heterogeneous state-space model and then a distributed adaptive control protocol has been designed to keep equal inter-vehicular spacing between any consecutive vehicles while maintaining a desired longitudinal velocity of the entire platoon. The proposed scheme utilizes only the neighbouring state information (i.e. relative distance, velocity and acceleration) and the leader is not required to communicate with each and every one of the following vehicles directly since the interaction topology of the vehicle platoon is designed to have a spanning tree rooted at the leader. Simulation results demonstrated the effectiveness of the proposed platoon control scheme. Moreover, the practical feasibility of the scheme was validated by hardware experiments with real robots.

Feedback Linearization Based on Gaussian Processes With Event-Triggered Online Learning

Abstract: Combining control engineering with nonparametric modeling techniques from machine learning allows for the control of systems without analytic description using data-driven models. Most of the existing approaches separate learning , i.e., the system identification based on a fixed dataset, and control , i.e., the execution of the model-based control law. This separation makes the performance highly sensitive to the initial selection of training data and possibly requires very large datasets. This article proposes a learning feedback linearizing control law using online closed-loop identification. The employed Gaussian process model updates its training data only if the model uncertainty becomes too large. This event-triggered online learning ensures high data efficiency and thereby reduces computational complexity, which is a major barrier for using Gaussian processes under real-time constraints. We propose safe forgetting strategies of data points to adhere to budget constraints and to further increase data efficiency. We show asymptotic stability for the tracking error under the proposed event-triggering law and illustrate the effective identification and control in simulation.

A Novel Assorted Nonlinear Stabilizer for DC–DC Multilevel Boost Converter With Constant Power Load in DC Microgrid

Abstract: The multilevel boost converter (MBC) has been widely adopted in the dc microgrid systems due to its high voltage gain and simple structure. In recent years, the power electronic loads, which usually behave as constant power loads (CPLs), are penetrating in microgrids. The incremental negative impedance of CPLs degrades the stability of microgrid systems. To ensure effective power flow and guarantee system stability, eliminating the undesired effects of CPLs is a necessity. In this article, a novel assorted nonlinear stabilizer, which is integrated with an extended nonlinear disturbance observer (NDO) and an adaptive backstepping controller, is proposed for stabilizing the MBC-fed microgrid system with CPLs. First, the reduced-order MBC model is transformed into the Brunovsky's canonical form using the exact feedback linearization technique. Second, owing to the NDO, fast system dynamic responses are achieved. Using the estimations of NDO, an adaptive backstepping controller is developed to strictly guarantee the microgrid bus voltage stability in the sense of a large signal. Simulation and experiment results are presented to verify the effectiveness and feasibility of the proposed stabilizer.

Error-Driven Nonlinear Feedback Design for Fuzzy Adaptive Dynamic Surface Control of Nonlinear Systems With Prescribed Tracking Performance

Abstract: This paper addresses an error-driven nonlinear feedback design technique to improve the dynamic performance of fuzzy adaptive dynamic surface control (DSC) for a class of uncertain multiple-input-multiple-output nonlinear systems with prescribed tracking performance. The highlight of the error-driven nonlinear feedback technique is that the feedback gain self-regulates versus different levels of output and virtual tracking errors, this reflects the classical control design criterions commendably: relatively high feedback gains can be implemented to guarantee disturbances and uncertainties attenuation and so on to improve the control performance when small tracking errors are measured, and relatively small feedback gains can be implemented to circumvent the problems of actuator and states saturations when large tracking errors are measured. The complexity problem of the traditional backstepping design is circumvented owe to the peculiarity of DSC method. Caused by the compound error functions of nonlinear feedback dynamics, a nonquadratic Lyapunov function is used to deduce the conditions of closed-loop stability. Fuzzy logic systems and error transformation-based method are used in the online learning of completely unknown dynamics and the prescribed performance tracking, respectively. Comparative results are presented to demonstrate the effectiveness and preponderance of the proposed control scheme with comparison to existing ones.

Performance Recovery of Dynamic Feedback-Linearization Methods for Multivariable Nonlinear Systems

Abstract: We show, in this paper, how a classical method for feedback linearization of a multivariable invertible nonlinear system, via dynamic extension and state feedback, can be robustified. The synthesis of the controller is achieved by means of a recursive procedure that, at each stage, consists in the augmentation of the system state space, to the purpose of rendering feedback-linearization possible, and in the design of a high-gain extended observer, to the purpose of estimating the state of the plant as well as the perturbations due to model uncertainties. As a result, a closed-loop system is obtained that, for any bounded set of initial conditions and any bounded input, recovers the performance that would have been obtained by means of the classical technique of feedback linearization via dynamic state feedback.

Prescribed Performance Controller Design for DC Converter System With Constant Power Loads in DC Microgrid

Abstract: In this paper, a composite prescribed performance control strategy is developed for stabilizing dc/dc boost converter feeding constant power loads. First, by employing the exact feedback linearization technique, the nonlinear uncertain dc converter system is first transformed into the Brunovsky’s canonical form. Then, a nonlinear disturbance observer is utilized to evaluate the dynamic change of load power and the accuracy of output voltage regulated by feedforward compensation. Next, the prescribed performance controller is elaborately designed to ensure that the tracking error of output voltage is always within the margin of predefined error bounds. Based on the backstepping design approach, the composite nonlinear controller with prescribed performance is determined. Finally, the numerical simulation results are presented to demonstrate the tracking performance of the proposed controller.

Feedback linearization based sensorless direct torque control using stator flux MRAS-sliding mode observer for induction motor drive.

Abstract: The high-performance Direct Torque Control (DTC) requires accurate knowledge of flux and speed information. Furthermore, the elimination of sensors leads to reduced overall cost and size of the electric drive system and subsequently improving its reliability. This paper proposes an effective sensorless direct torque control scheme for induction motor drive. The proposed scheme consists of enhancing the decoupling structure and variable estimation as well. Therefore, an enhanced direct flux and torque control based on feedback linearization is implemented in one hand. This allows obtaining a linear decoupled control together with minimized flux and torque ripples. In another hand, a combined sliding mode observer and model reference adaptive system is associated with the control scheme as sensorless algorithms for rotor speed and flux estimation. This conjunction is intended to enhance the sliding mode observer performances especially at low speed operations and reduce its sensitivity to noise and system uncertainties as well. The effectiveness of the proposed control algorithm has been verified through simulation and experimental work using MATLAB/Simulink software and dSpace 1104 implementation board respectively.

Linearized Price-Responsive HVAC Controller for Optimal Scheduling of Smart Building Loads

Abstract: The need to optimize the energy consumption of commercial buildings– responsible for over 40% of U.S. energy consumption–has recently gained significant attention due to the call for energy efficiency. Moreover, the ability to participate in the retail electricity markets through proactive demand-side participation has recently led to the development of an economic model predictive control (EMPC) for these buildings’ Heating, Ventilation, and Air Conditioning (HVAC) systems. The objective of this paper is to develop a price-responsive operational model for buildings’ HVAC systems while considering inflexible loads and other distributed energy resources (DERs), including photovoltaic (PV) generation and battery storage systems. A Nonlinear Economic Model Predictive Controller (NL-EMPC) is presented to minimize the net cost of energy usage by the buildings’ flexible loads, i.e., HVAC systems while satisfying the comfort-level of buildings’ occupants. To improve the computational efficiency of the HVAC system controller, we propose a linearized economic model predictive controller (L-EMPC). The L-EMPC is a novel approximate linearized model for the NL-EMPC and is based on the feedback linearization technique. The proposed approach results in a controller for the building with the reduced complexity that accurately approximates the original nonlinear plant dynamics with its economic constraints. The efficiency of the proposed EMPC controllers are evaluated using several simulation case studies.

Multiobjective Optimization of Droop-Controlled Distributed Generators in DC Microgrids

Abstract: DC microgrids (DCMGs) are becoming more popular in modern power systems due to their simplicity, efficiency, and reliability. Autonomous control of DCMG is primarily based on the droop control. Typically, the droop coefficients of each distributed generator (DG) are fixed and assigned based on their capacity. This article introduces a multiobjective optimization (MOO) based intelligent computation approach to derive the optimal droop coefficients for DGs in an islanded DCMG. The proposed approach takes into consideration not only the capacities of the DGs but also the system voltage regulation, system total loss minimization, and enhanced current sharing among the DGs. The Pareto optimal front of the constructed MOO problem is obtained using the elitist nondominated sorting genetic algorithm (NSGA II). A best compromise solution is extracted from the generated Pareto optimal front by employing a fuzzy membership function approach. Moreover, a state feedback linearization based controller is introduced to facilitate the control actions to experimentally validate the effectiveness and the applicability of the generated optimal droop relationships. The proposed approach was tested with a parallel connected dc 9-bus system, IEEE 30-bus system, and experimentally validated on a 5-bus system. However, the same concept can be extended to any other bus system without loss of generality.

A Separation-Based Methodology to Consensus Tracking of Switched High-Order Nonlinear Multi-Agent Systems

Abstract: This work investigates a reduced-complexity adaptive methodology to consensus tracking for a team of uncertain high-order nonlinear systems with switched (possibly asynchronous) dynamics. It is well known that high-order nonlinear systems are intrinsically challenging as feedback linearization and backstepping methods successfully developed for low-order systems fail to work. At the same time, even the adding-one power-integrator methodology, well explored for the single-agent high-order case, presents some complexity issues and is unsuited for distributed control. At the core of the proposed distributed methodology is a newly proposed definition for separable functions: this definition allows the formulation of a separation-based lemma to handle the high-order terms with reduced complexity in the control design. Complexity is reduced in a twofold sense: the control gain of each virtual control law does not have to be incorporated in the next virtual control law iteratively, thus leading to a simpler expression of the control laws; the order of the virtual control gains increases only proportionally (rather than exponentially) with the order of the systems, dramatically reducing high-gain issues.

Dynamic Performance Improving Sliding-Mode Control-Based Feedback Linearization for PV System Under LVRT Condition

Abstract: The low-voltage ride through condition (LVRT) attracts wider attention with the increased power of central grid-connected photovoltaic (PV) systems. The conventional double-loop-based control strategy designed for the nominal voltage is slow and potentially unstable under the LVRT operation. In this article, a novel sliding-mode control-based feedback linearization is proposed to improve response speed of the PV system under LVRT condition. To increase the robustness of the control to parameter variations, sliding-mode control is combined with the feedback linearization in this article. Further, the complete linearization of the PV system can be achieved over the whole operating range. Moreover, the dc-link voltage operating point of the PV panel can be dynamically adjusted based on the drop depth of the grid voltage, which will reduce the load changes during the LVRT and subsequently ensure the ac current levels to stay within the design limits in any fault situation and enhance the LVRT capability of the central grid-connected PV system. Finally, a 100-kW PV inverter is built to validate the feasibility and effectiveness of the proposed strategy for LVRT.

Nonlinear Robust Optimal Control via Adaptive Dynamic Programming of Permanent-Magnet Linear Synchronous Motor Drive for Uncertain Two-Axis Motion Control System

Abstract: In this article, a nonlinear robust optimal control (NROC) scheme for uncertain two-axis motion control system via adaptive dynamic programming (ADP) and neural networks (NNs) is proposed. The two-axis motion control system is an X – Y table actuated by permanent-magnet linear synchronous motor servo drives. First, the motions of the tracking contour in X -axis and Y -axis of the X–Y table are stabilized through feedback linearization control (FLC) laws. However, the control performance may be destroyed due to parameter uncertainties and compounded disturbances. Therefore, to improve the robustness of the control system, an NROC is designed to achieve this purpose. The tracking control problem of the X–Y table with uncertainties is transformed to a regulation problem. Then, it is solved by an infinite horizon optimal control using a critic NN. Consequently, the NN is developed via ADP learning algorithm to facilitate the online solution of the Hamilton–Jacobi–Bellman equation corresponding to the nominal system for approximating the optimal control law. The uniform ultimate boundedness of the closed-loop system is proved utilizing the Lyapunov approach and the tracking error asymptotically converges to a residual set. The validation of the proposed control schemes are carried out through experimental analysis. The control algorithms have been implemented using a DSP control board. A comparison of control performances using FLC, adaptive FLC, and FLC-based NROC is investigated. From the experimental results, the dynamic behaviors of the two-axis control system using the proposed FLC-based NROC can achieve robust optimal control performance against parameter uncertainties and compounded disturbances.

Reactive Power Based MRAS for Speed Estimation of Solar Fed Induction Motor With Improved Feedback Linearization for Water Pumping

Abstract: This article presents an efficient method for control of a solar water pumping system consisting of an induction motor drive (IMD) with a model reference adaptive system (MRAS) based adaptive mechanism of speed estimation. An improved perturb and observe control technique is implemented to achieve maximum power point tracking. The gating signals are produced by utilizing a space vector pulsewidth modulation scheme for a three-phase inverter. An improved third order integrator (ITOI) is proposed for adaptation of flux for improvement in the performance of the drive. The MRAS is used for estimation of slip speed and synchronous speed two estimated speed signals are used to generate the motor speed. The proposed control methodology with the MRAS-based reactive power technique for the estimation of speed has improved the system stability for a wide range of drive speed. This system with a single-stage photovoltaic (PV) topology and switching logic obtained from space vector modulation operating three-phase voltage source inverter fed IMD with a pump as a load is designed and implemented in MATLAB/Simulink. The suitability of the developed system is validated through experimentation in the laboratory.

Integral Sliding Mode Control for Starting Speed Sensorless Controlled Induction Motor in the Rotating Condition

Abstract: Starting in the rotating condition is an important technology for induction motor (IM) speed sensorless control. The key of this technology is that the initial speed of the IM in the rotating condition should be estimated quickly and accurately. In this article, an integral sliding mode control method based on the nonlinear model of the rotor back electromotive force is designed to start a speed sensorless controlled IM in the rotating condition. The stability is guaranteed by Lyapunov stability analysis, and the robustness to rotor time constant and disturbance are analyzed. Meanwhile, in comparison with the proposed control method, the existing input–output feedback linearization (IOFL) control method is introduced and the stability and robustness are also analyzed. Compared with the existing IOFL control method, the proposed control method can estimate the initial speed in the rotating condition without overshoot and has better dynamic performance and robustness. In addition, the proposed control method can be applied not only to the restart operation but also to the normal operation. Only a single control strategy is required from the restart operation to the normal operation. The proposed method is verified by simulation and experiments using a 7.5 kW IM.

Sliding Mode Control of Induction Motor Drive Based on Feedback Linearization

Abstract: In this paper, sliding mode control theory is demonstrated to design and control the rotor speed and rotor flux of the feedback linearized induction motor (IM) drive. Dynamic equations of the IM in...

Control Design for UAV Quadrotors via Embedded Model Control

Abstract: In this paper, a control system for unmanned aerial vehicles (UAVs) is designed, tested in simulation by means of a high-fidelity simulator, and then applied to a real quadrotor UAV. A novel approach is proposed for the control design, based on the combination of two methodologies: feedback linearization (FL) and embedded model control (EMC). FL allows us to properly transform the UAV dynamics into a form suitable for EMC; EMC is then used to control the transformed system. A key feature of EMC is that it encompasses a so-called extended state observer (ESO), which not only recovers the system state but also gives a real-time estimate of all the disturbances/uncertainties affecting the system. This estimate is used by the FL-EMC control law to reject the aforementioned disturbances/uncertainties, including those collected via the FL, allowing a robustness and performance enhancement. This approach allows us to combine FL and EMC strengths. Most notably, the entire process is made systematic and application oriented. To set-up a reliable UAV attitude observer, an effective attitude sensors fusion is proposed and also benchmarked with an enhanced complementary filter. Finally, to enhance the closed-loop performance, a complete tuning procedure, encompassing frequency requirements, is outlined, based on suitably defined stability and performance metrics.

Modeling and Fuzzy Decoupling Control of an Underwater Vehicle-Manipulator System

Abstract: This paper presents the detailed modeling and simulation of the dynamic coupling between an autonomous underwater vehicle (AUV) and a manipulator. The modeling processes are described with the incorporation of the most dominating hydrodynamic effects such as added mass, lift and drag forces. The hydrodynamic coefficients are derived using strip theory and are adjusted according to dynamical similarity. A fuzzy decoupling controller (FDC) is proposed for an autonomous underwater vehicle-manipulator system (UVMS) which consists of two subsystems, an underwater vehicle and a manipulator. The proposed controller uses a fuzzy algorithm (FA) to adaptively tune the gain matrix of the error function (EF). The EF is described by the integral sliding surface function. This technique allows the off-diagonal elements developed for decoupling the system to be incorporated in the gain matrix. Tracing the FA and EF back to the principle of feedback linearization, one further obtains evidence about the decoupling and stability of the system. Moreover, a desired trajectory with the consideration of the dynamic coupling of the AUV is designed to reduce the thruster forces and manipulator's torques. This technique provides high performance in terms of tracking error norms and expended energy norms. A major contribution of this study is that it adopts the off-diagonal elements to exploit the dynamic coupling between the degrees of freedom of the subsystem and the dynamic coupling between the two subsystems. Simulation results demonstrate the effectiveness and robustness of the proposed technique in the presence of parameter uncertainties and external disturbances.

Distributed Cooperative Guidance for Multivehicle Simultaneous Arrival Without Numerical Singularities

Abstract: Kang Li∗ Westlake University, 310024 Hangzhou, People’s Republic of China Jianan Wang Beijing Institute of Technology, 100081 Beijing, People’s Republic of China Chang-Hun Lee Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea Rui Zhou Beijing University of Aeronautics and Astronautics, 10083 Beijing, People’s Republic of China and Shiyu Zhao Westlake University, Westlake Institute for Advanced Study, 310024 Hangzhou, People’s Republic of China

Design and Experimental Validation of a 3-DOF Underactuated Pendulum-Like Robot

Abstract: This article presents the design and the experimental validation of a novel 3-degree-of-freedom (DOF) pendulum-like cable-driven robot capable of executing point-to-point motions by leveraging partial feedback linearization control and on-line trajectory planning based on adaptive frequency oscillators (AFOs). Unlike most cable-suspended parallel robots, which rely on at least $n$ actuated cables to control $n$ DOF, the proposed robot is capable of performing 3-DOF point-to-point motions, from a starting pose to a goal one within its dynamic workspace, by means of two actuators only. Feedback linearization allows the dynamics of the variable-length pendulum to be decoupled from the orientation of the end effector, enabling the device to use parametric excitation to control the oscillations of the variable-length pendulum, akin to a playground swing. A pool of AFOs is introduced to enable smooth, lag free on-line estimations of the current phase of the pendulum oscillation to inform the on-line planner and the parametric excitation controller. Experimental results demonstrate feasibility of the proposed design and control approach.

An optimal adaptive hybrid controller for a fourth-order under-actuated nonlinear inverted pendulum system:

Abstract: In this paper, a combination of approximate feedback linearization and sliding mode control approaches is applied to stabilize a class of fourth-order nonlinear systems. In order to improve the per...

MRAC-Based Voltage Controller for Three-Phase CVCF Inverters to Attenuate Parameter Uncertainties Under Critical Load Conditions

Abstract: This paper investigates a robust model reference adaptive control (MRAC) method for a three-phase constant-voltage constant-frequency (CVCF) inverter with an output LC filter. The proposed MRAC method is designed to stabilize the error dynamics of the system by a feedback control term in the steady state and attenuate the parameter uncertainties of the system by an updated MRAC term. Unlike the conventional proportional–derivative control (PDC) scheme, the proposed MRAC scheme ensures the fast convergence of the output errors to the exponential trajectories predefined by the reference models. Furthermore, the adaptive state-feedback mechanism can guarantee the fast dynamic response in the transient state without using load current sensors or observers. The asymptotic stability is mathematically proven by a Lyapunov theory. The feasibility of the proposed controller is confirmed through extensive experimental studies on a prototype three-phase CVCF inverter with a TI TMS320LF28335 DSP. Finally, comparative experimental results of three control methods (i.e., conventional PDC, feedback linearization control, and proposed MRAC) are provided to validate the superior performance of the proposed method such as fast transient response, low total harmonic distortion, and robustness to parameter uncertainties under critical load conditions (i.e., abrupt load changes, unbalanced loads, and distorted nonlinear loads).

Towards Dynamic Transparency: Robust Interaction Force Tracking Using Multi-Sensory Control on an Arm Exoskeleton

Abstract: A high-quality free-motion rendering is one of the most vital traits to achieve an immersive human-robot interaction. Rendering free-motion is notably challenging for rehabilitation exoskeletons due to their relatively high weight and powerful actuators required for strength training and support. In the presence of dynamic human movements, accurate feedback linearization of the robot’s dynamics is necessary to allow for a linear synthesis of interaction wrench controllers. Hence, we introduce a virtual model controller that uses two 6-DoF force sensors to control the interaction wrenches of a multi-DoF torque-controlled exoskeleton over the joint accelerations and inverse dynamics. Furthermore, we propose a disturbance observer for controlling the joint acceleration to diminish the influence of modeling errors on the inverse dynamics. To provide a high-bandwidth, low-bias estimation of the system’s acceleration, we introduce a bias-observer which fuses the information from joint encoders and seven low priced IMUs. We have validated the performance of our proposed control structure on the shoulder and arm exoskeleton ANYexo. The experimental comparison of the controllers shows a reduction of the felt inertia and maximum reflected joint torque by a factor of more than three compared to state of the art. The controllers’ robustness w.r.t. a model mismatch is validated. The experiments show that the closed-loop acceleration control improves the tracking, particularly at joints with low inertia. The proposed controllers’ performance sets a new benchmark in haptic transparency for comparable devices and should be transferable to other applications.

Parameter Convergence for Adaptive Control in Nonlinear System

Abstract: Feedback linearization uses an effective linearizing control to make the input-output dynamics of a nonlinear plant linear such that various linear control strategies can be applied for tracking the output trajectories to the desired one. One of the key open issues in adaptive control of feedback linearizable structures is the estimation of the coefficients of the feedback control law. Therefore, a complete structure is explored without any assumptions or prior knowledge of the system parameters using the finite-time parameter estimator. The weights of the system dynamics are estimated and updated online to robustify the exact cancellation of non-linear terms required by the linearization method. The proposed method exhibits the improved transient response and global convergence of coefficients. It can be applied as a general tool for developing a high-quality control system for a higher dimensional system which is exactly input-output linearizable with uncertain plant parameters. The simulation results of inverted pendulum and buck converter has verified the efficacy of the proposed methodology.

Nonlinear Control Operation of DFIG-Based WECS Incorporated With Machine Loss Reduction Scheme

Abstract: This article presents a novel adaptive backstepping based nonlinear control scheme incorporated with machine loss reduction and parameter uncertainties for grid-connected doubly fed induction generator (DFIG) driven wind energy conversion system (WECS). The proposed nonlinear controller is developed to stabilize both the grid and rotor side current control loops of direct-drive DFIG-based WECS. Traditional feedback linearization controllers are sensitive to system parameter variations and disturbances on DFIG-based WECS, which demands advanced control techniques for stable and efficient performance considering the nonlinear system dynamics. The proposed nonlinear controller incorporates the system uncertainty and nonlinearities while ensuring the stability of the drive system through Lyapunov stability criteria. A machine loss reduction algorithm is also incorporated to achieve enhanced efficiency. The performance of the proposed nonlinear scheme is compared with conventional benchmark fixed gain proportional-integral control and sliding mode control scheme for the rotor-side converter controller. The proposed nonlinear controller for DFIG-based WECS integrated with machine loss reduction scheme is successfully implemented in real time using DSP board DS 1104 for a prototype 350 W DFIG. The simulation and experimental results prove the efficacy of the proposed scheme under variable operating conditions such as wind speed variation, grid voltage disturbances, and parameter uncertainties.