Showing papers on "Feedback linearization published in 2021"
TL;DR: 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 with a newly proposed definition for separable functions.
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. 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 power of the virtual and actual control laws increases only proportionally (rather than exponentially) with the order of the systems, dramatically reducing high-gain issues.
53 citations
50 citations
TL;DR: A practical terminal sliding mode control framework based on an adaptive disturbance observer (ADO) is presented for the active suspension systems and a detailed comparison with the active disturbance rejection method has been provided.
Abstract: In this article, a practical terminal sliding mode control (TSMC) framework based on an adaptive disturbance observer (ADO) is presented for the active suspension systems. The proposed controller requires no exact feedback linearization about the suspension dynamics. The ADO is designed to estimate the unknown dynamics and control errors produced by the motor actuator. To guarantee the fast convergence and high control accuracy, a TSMC-type surface and a continuous sliding mode reaching law are designed. The finite-time convergence of the controlled system is guaranteed based on the Lyapunov stability theory. To evaluate the performance improvement of the proposed control framework, a detailed comparison with the active disturbance rejection method has been provided. Finally, a practical hardware-in-loop experiment is implemented to validate the effectiveness of the proposed control scheme.
50 citations
TL;DR: In this article, a model-based adaptive event-triggered control scheme for a class of uncertain single-input and single-output nonlinear continuous-time systems is presented.
37 citations
TL;DR: A trained artificial neural network input–output feedback linearization (IOFL) control strategy is proposed for a grid-connected nine-level packed E-cell (PEC9) converter under various operating variations and for the first time, a second-order dynamic model in d–q reference frame is developed for PEC9 converter.
Abstract: In this article, a trained artificial neural network (ANN) input–output feedback linearization (IOFL) control strategy is proposed for a grid-connected nine-level packed E-cell (PEC9) converter under various operating variations. For the first time, a second-order dynamic model in d–q reference frame is developed for PEC9 converter. This model enforces the converter to employ IOFL-based proportional–resonance compensators for accurately contributing to current tracking enhancement, mitigation at the current ripple, and consequently, the capacitors dc voltages ripple. As another contribution, the current tracking ability of the proposed controller is much more promoted by decoupling the d–q dynamic components. Indeed, the effect of each proposed control component is fortified through decoupling feedback coefficients. In addition, as the third contribution, ANN as the complementary controller adapts the proposed IOFL-based controller coefficients to improve efficiency and stability of the whole control loop system, including the PEC9 capacitor voltages in unstable operation of load, grid, and dc source as well as in the presence of parameters mismatch and disturbances. To support the aforementioned contributions with more details, the closed-loop description and the dynamic model of the proposed controller-based PEC9 converter are utilized to present several stability evaluation processes in both time and frequency domains. This assessment process is accurately executed to confirm the stable performance of PEC9 when the errors of the PEC9 converter currents are changed as well as the decoupling feedback coefficients are varied. Finally, comparative dSPACE setup-based experimental and simulation results are attained to further verify the validity of the proposed control strategy under various operating conditions.
36 citations
TL;DR: A model-free active input–output feedbacklinearization technique based on an improved active disturbance rejection control paradigm is proposed to design feedback linearization control law for a generalized nonlinear system with a known relative degree.
Abstract: Traditional input–output feedback linearization requires full knowledge of system dynamics and assumes no disturbance at the input channel and no system’s uncertainties. In this paper, a model-free...
33 citations
TL;DR: A control solution for an Unmanned Aerial Vehicle encapsulating a nonlinear inner-loop based on the application of feedback linearization to the attitude and altitude dynamics is proposed in this paper.
Abstract: A control solution for an Unmanned Aerial Vehicle encapsulating a nonlinear inner-loop based on the application of feedback linearization to the attitude and altitude dynamics is proposed in this paper. Linear quadratic controllers with integrative action are implemented not only to the resulting inner-loop chain of integrators, but also to the outer-loop, that controls the horizontal movement and, consequently, stabilizes the zero-dynamics. The required full state-feedback relies on measurements from motion sensors and on-flight estimates provided by Kalman filters and a nonlinear attitude filter. In simulation, the capacity of trajectory tracking and withstanding significant deviations of the mass and inertia values of the proposed control structure are evaluated while considering saturation and noisy measurements. The simulations results were experimentally validated using a commercially available drone. The modeling and control system architecture are validated by the experimental results. Additionally, a comparison with the results achieved with a linear control solution developed in a previous work is drawn.
31 citations
01 Jan 2021
TL;DR: This letter derives direct adaptive control algorithms for nonlinear systems nominally contracting in closed-loop, but subject to structured parametric uncertainty from methods based on feedback linearization or backstepping.
Abstract: This letter derives direct adaptive control algorithms for nonlinear systems nominally contracting in closed-loop, but subject to structured parametric uncertainty. The approach is more general than methods based on feedback linearization or backstepping as it does not require invertibility or the system be in strict-feedback form. More broadly, it can be combined with learned controllers that must remain effective in the presence of structured parametric uncertainty. Simulation results illustrate the approach on a system with extended matched uncertainty.
30 citations
TL;DR: In this paper, a robust linear quadratic regulator-based neural-fuzzy (NF) control scheme is proposed to address the effect of payload uncertainties and external disturbances during passive-assist gait training.
Abstract: The design of an accurate control scheme for a lower limb exoskeleton system has few challenges due to the uncertain dynamics and the unintended subject’s reflexes during gait rehabilitation. In this work, a robust linear quadratic regulator- (LQR-) based neural-fuzzy (NF) control scheme is proposed to address the effect of payload uncertainties and external disturbances during passive-assist gait training. Initially, the Euler-Lagrange principle-based nonlinear dynamic relations are established for the coupled system. The input-output feedback linearization approach is used to transform the nonlinear relations into a linearized state-space form. The architecture of the adaptive neuro-fuzzy inference system (ANFIS) and used membership function are briefly explained. While varying mass parameters up to 20%, three robust neural-fuzzy datasets are formulated offline with the joint error vector and LQR control input. Thereafter, to deal with external interferences, an error dynamics with a disturbance estimator is presented using an online adaptation of the firing strength matrix. The Lyapunov theory is carried out to ensure the asymptotic stability of the coupled human-exoskeleton system in view of the proposed controller. The gait tracking results for the proposed control scheme (RLQR-NF) are presented and compared with the exponential reaching law-based sliding mode (ERL-SM) controller. Furthermore, to investigate the robustness of the proposed control over LQR control, a comparative performance analysis is presented for two cases of parametric uncertainties and external disturbances. The first case considers the 20% raise in mass values with a trigonometric form of disturbances, and the second case includes the effect of the 30% increment in mass values with a random form of disturbances. The simulation runs have shown the promising gait tracking aspects of the designed controller for passive-assist gait training.
30 citations
TL;DR: A novel moving-target enclosing control scheme is proposed by using the feedback linearization approach, in which the design procedure of the cooperative controller is more straightforward and the corresponding stability analysis is more concise.
Abstract: This article investigates the moving-target circular formation control problem for multiple nonholonomic vehicles under a directed graph. First, a novel moving-target enclosing control scheme is proposed by using the feedback linearization approach, in which the design procedure of the cooperative controller is more straightforward. Compared with the existing literature, the designed controller relaxes some existing constraints and the corresponding stability analysis is more concise. Second, based on the distance measurements, the observers, including sliding-mode observer and relative position observer, are designed to estimate the relative position so that the global position measurements are not required. Therefore, the observer-based controller becomes more suitable for practical application. Numerical simulations are conducted to illustrate the effectiveness of the proposed controllers.
30 citations
TL;DR: In this paper, the authors investigated the problem of event-triggered model-free adaptive iterative learning control (MFAILC) for a class of nonlinear systems over fading channels, where the fading phenomenon existing in output channels was modeled as an independent Gaussian distribution with mathematical expectation and variance.
Abstract: This article investigates the problem of event-triggered model-free adaptive iterative learning control (MFAILC) for a class of nonlinear systems over fading channels. The fading phenomenon existing in output channels is modeled as an independent Gaussian distribution with mathematical expectation and variance. An event-triggered condition along both iteration domain and time domain is constructed in order to save the communication resources in the iteration. The considered nonlinear system is converted into an equivalent linearization model and then the event-triggered MFAILC independent of the system model is constructed with the faded outputs. Rigorous analysis and convergence proof are developed to verify the ultimately boundedness of the tracking error by using the Lyapunov function. Finally, the effectiveness of the presented algorithm is demonstrated with a numerical example and a velocity tracking control example of wheeled mobile robots (WMRs).
TL;DR: Numerical studies and simulation results validate the effectiveness of the proposed controller design algorithm in both tracking and synchronization performance of the SMMS system, and robustly handling the stochastic and nondifferentiable nature of communication delays.
Abstract: Communication time delays in a bilateral teleoperation system often carries a stochastic nature, particularly when we have multiple masters or slaves. In this paper, we tackle the problem for a single-master multislave (SMMS) teleoperation system by assuming an asymmetric and semi-Markovian jump protocol for communication of the slaves with the master under time-varying transition rates. A nonlinear robust controller is designed for the system that guarantees its global robust ${H_{\infty}} $ stochastic stability in the sense of the Lyapunov theory. Employing the nonlinear feedback linearization technique, the dynamics of the closed-loop teleoperator is decoupled into two interconnected subsystems: 1) master–slave tracking dynamics (coordination) and 2) multislave synchronization dynamics. Employing an improved reciprocally convex combination technique, the stability analysis of the closed-loop teleoperator is conducted using the Lyapunov–Krasovskii methodology, and the stability conditions are expressed in the form of linear matrix inequalities that can be solved efficiently using numerical algorithms. Numerical studies and simulation results validate the effectiveness of the proposed controller design algorithm in both tracking and synchronization performance of the SMMS system, and robustly handling the stochastic and nondifferentiable nature of communication delays.
TL;DR: A robust tracking control method is developed for omnidirectional mobile robots (OMRs) with uncertainties in the kinematics and dynamics that significantly degrade the OMR tracking control performance and a robust backstepping-like feedback linearization tracking controller is proposed to compensate for these uncertainties.
Abstract: In this article, a robust tracking control method is developed for omnidirectional mobile robots (OMRs) with uncertainties in the kinematics and dynamics. The kinematic and dynamic uncertainties that significantly degrade the OMR tracking control performance should be simultaneously compensated, which has not been achieved by the existing OMR tracking control methods. Therefore, the OMR dynamics containing the actuator dynamics and dynamic uncertainties are obtained by including the friction present in the OMR and are identified using a system identification method for the actual OMR system. The kinematic uncertainties present in the OMR kinematics are then derived unlike the existing studies that do not consider them. A sliding mode disturbance observer is proposed to estimate these kinematic uncertainties. A robust backstepping-like feedback linearization tracking controller using estimates of both the kinematic and dynamic uncertainties is also proposed to compensate for these uncertainties. The proposed method is validated through a stability analysis and simulation and experimental results using an actually implemented OMR system.
TL;DR: These two basic concepts of keeping and shaping of the natural inertia are investigated and compared including aspects such as interaction behavior, tracking performance, tuning parameters, influence of modeling errors, and effective feedback gains.
01 Mar 2021
TL;DR: In this article, a learning-based predictive path following control scheme was proposed for a quadrotor path following task under unknown wind disturbances, where Gaussian Processes were used to learn the uncertain environmental disturbances online and track the reference state accurately with a probabilistic stability guarantee.
Abstract: Accurate path following is challenging for autonomous robots operating in uncertain environments. Adaptive and predictive control strategies are crucial for a nonlinear robotic system to achieve high-performance path following control. In this letter, we propose a novel learning-based predictive control scheme that couples a high-level model predictive path following controller (MPFC) with a low-level learning-based feedback linearization controller (LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level LB-FBLC utilizes Gaussian Processes to learn the uncertain environmental disturbances online and tracks the reference state accurately with a probabilistic stability guarantee. Meanwhile, the high-level MPFC exploits the linearized system model augmented with a virtual linear path dynamics model to optimize the evolution of path reference targets, and provides the reference states and controls for the low-level LB-FBLC. Simulation results illustrate the effectiveness of the proposed control strategy on a quadrotor path following task under unknown wind disturbances.
TL;DR: A pseudo-reference-based multiple-input multiple-output (MIMO) model predictive control (MPC) integrated with the feedback linearization is proposed for the HER regulation and the pressure balance of electrodes and it is shown that the more accurate hydrogen regulation performance of the proposed MPC can be achieved.
TL;DR: Maximum power extraction frameworks operating the state-of-the-art optimization methods are presented for permanent magnet synchronous generator–based wind energy conversion system and a hybrid maximum power point tracking approach that combines feedback linearization technique with fractional-order calculus is presented.
Abstract: The most important issue in the use of wind energy conversion systems is to ensure maximum power extraction in terms of efficiency. Therefore, maximum power point tracking algorithms are as importa...
TL;DR: In this article, a novel input/output feedback linearization control method by utilizing nonlinear disturbance observer (NDOB) is proposed for a quadruple-tank liquid level (QTLL) system.
Abstract: A novel input/output feedback linearization control method by utilizing nonlinear disturbance observer (NDOB) is proposed for a quadruple-tank liquid level (QTLL) system in this paper. Firstly, the mathematical model of QTLL system is established by using Bernoulli’s law and mass conservation. Secondly, in view of the nonlinear and coupling characteristics of the QTLL system, a input/output feedback linearization controller is designed. Then, a NDOB is proposed to estimate disturbances and applied to compensation control. Finally, simulation and experimental results show that the proposed strategy has better control performances than PID control and the disturbance observer-based sliding mode control (DOBSMC).
TL;DR: Two different nonlinear model predictive control schemes (with and without terminal cost and constraints) are proposed for stabilizing the translational dynamics of thrust-propelled vehicles, guaranteeing the closed-loop asymptotic stability.
Abstract: We propose two different nonlinear model predictive control (NMPC) schemes (with and without terminal cost and constraints) for stabilizing the translational dynamics of thrust-propelled vehicles. Both approaches make use of an elaborated nonlinear feedback linearization controller and its associated ellipsoidal invariant set under restrictive input constraints, hence guaranteeing the closed-loop asymptotic stability. The terminal constraint set of the corresponding NMPC design is easy to tune due to its clear formulation expressed directly in terms of the tuning variables, while for the NMPC scheme without terminal constraint, the design allows to stabilize the system with a significantly shorter prediction horizon in comparison with the existing method in the literature. Simulation and experimental tests over a nano-drone platform validate the proposed approaches.
TL;DR: By integrating the control strategies of locomotion and active deformation of a class of magnetic soft robot made of ferrofluid, it is demonstrated that the soft robot possesses the capability of conducting complex tasks such as passing through narrow environment and transporting multiple objects.
Abstract: Magnetic robots have shown great potential in small-scale applications due to their wireless control mode. However, the existing efforts only deal with solid magnetic materials that could not deform. In this article, we focus on integrated locomotion and deformation of a class of magnetic soft robot made of ferrofluid. To this end, the magnetic model and dynamics model that takes the nonlinearity into account are first established. Then, the corresponding motion controllers are proposed, based on the results of feedback linearization and frequency-domain test results. Furthermore, an extended state observer is designed to reduce the perturbation due to model uncertainties. By integrating the control strategies of locomotion and active deformation, we demonstrate that the soft robot possesses the capability of conducting complex tasks such as passing through narrow environment and transporting multiple objects. Various experiments are also performed to demonstrate the effectiveness of the proposed control methods.
TL;DR: The adaptive tracking control problem is investigated for a multibody high-speed train dynamic model in the presence of unknown parameters, which is an open adaptive control problem.
Abstract: In this article, the adaptive tracking control problem is investigated for a multibody high-speed train dynamic model in the presence of unknown parameters, which is an open adaptive control problem. A four-car train unit model with input signals acting on the second and third cars and output signals being the speeds of the first and third cars is chosen as a benchmark model, in which the aerodynamic resistance force is also considered. To handle the nonlinear term, the feedback linearization method is employed to decompose the system into a control dynamics subsystem and a zero dynamics subsystem. A new and detailed stability analysis is conducted to show that such a zero dynamic system is a Lyapunov stable and is also partially input-to-state stable under the condition that the speed error between the first and third cars is exponentially convergent (to be ensured by a nominal controller) or belongs to the $L^{1}$ signal space (to be achieved by a properly designed adaptive controller). The system configuration leads to a relative degree 1 subsystem and a relative degree 2 subsystem, for which different feedback linearization-based adaptive controllers and their nominal versions are developed to ensure the needed stabilization condition, the desired closed-loop system signal boundedness, and asymptotic output speed tracking. Detailed closed-loop system stability and tracking performance analysis are given for the new control schemes. Simulation results from a realistic train dynamic model are presented to verify the desired adaptive control system performance.
TL;DR: The CL passive filter is used to mitigate the high-frequency harmonics and the APF is applied to compensate the low-frequency harmonic current to suppress the second-harmonic current at the dc side of a single-stage single-phase inverter.
Abstract: In the current power system, the conversion between dc and ac is widely existing and the dc-side harmonic problem is prominent. To suppress the second-harmonic current at the dc side of a single-stage single-phase inverter, a dc hybrid active power filter (DC-HAPF) structure is presented, which composes of a bidirectional dc–dc circuit-based active power filter (APF) and a CL passive filter. In this article, the CL passive filter is used to mitigate the high-frequency harmonics and the APF is applied to compensate the low-frequency harmonic current. Meanwhile, the influence of the filter parameters on the harmonic suppression is analyzed based on the average switching model. In addition, for the control of the DC-HAPF, a nonlinear unified controller via feedback linearization is proposed, where the voltage and current dual-loop control is converted to a single-loop control of energy. By analyzing the control system stability and DC-HAPF's performance, appropriate control parameters are selected. To verify the feasibility of the proposed topology and control strategy, a 500-W single-stage single-phase inverter with the DC-HAPF is built and a good performance of the dc-side harmonic suppression has been achieved.
TL;DR: It is demonstrated that closed-loop tracking control of a bioprocess for a desired yield profile is possible only with two inputs and online learning is preferred due to lack of a priori training data for approximating complex nonlinear functions.
TL;DR: The obtained results reveal the superiority of the proposed FTC based on TDE and NFITSM, which is compared with conventional sliding mode and feedback linearization control methods.
Abstract: In this paper, a new hybrid fault-tolerant control (FTC) strategy based on nonsingular fast integral-type terminal sliding mode (NFITSM) and time delay estimation (TDE) is proposed for a Schonflies parallel manipulator. In order to detect, isolate, and accommodate actuator faults, TDE is used as an online fault estimation algorithm. Stability analysis of the closed-loop system is performed using Lyapunov theory. The proposed controller performance is compared with conventional sliding mode and feedback linearization control methods. The obtained results reveal the superiority of the proposed FTC based on TDE and NFITSM.
TL;DR: An adaptive robust combination of feedback linearization and sliding mode controller (SMC) based on fuzzy rules and gradient descent laws and a novel evolutionary algorithm termed multi-objective ant lion optimizer (MOALO) is implemented to determine the control coefficients.
Abstract: This paper develops an adaptive robust combination of feedback linearization (FL) and sliding mode controller (SMC) based on fuzzy rules and gradient descent laws. The new suggested control algorithm is tested to stabilize a fourth-order under-actuated nonlinear inverted pendulum system. More precisely, the reliable feedback linearization approach and the robust SMC controller are combined to design a stable control effort. In order to enhance the performance of the suggested controller, an adaptation technique as long as fuzzy rules are applied to update the control gains and the boundary layer parameter. Then, a novel evolutionary algorithm termed multi-objective ant lion optimizer (MOALO) is implemented to determine the control coefficients. The analysis and results conducted on the inverted pendulum system demonstrate the desired performance of the proposed control scheme by providing an optimal smooth control input, suitable tracking performance, and proper time responses.
TL;DR: It is demonstrated that performing a transformation from voltage–current to power-energy domain by feedback linearization-based nonlinear control simultaneously eliminates the unstable mode and increases control bandwidth.
Abstract: This article focuses on improving the control structure of electronic capacitor (EC), formed by a bidirectional dc–dc converter terminated with small auxiliary capacitance. Typical application of such a system is mimicking the dynamic behavior of a bulk dc-ink capacitor while actually employing a component with much lower capacitance value. It was previously revealed that EC may be treated as output-voltage regulated wide-input-range converter feeding a bidirectional power load. Dual-loop voltage-current linear cascaded regulator, typically employed for EC terminal voltage control, imposes slow unstable mode when absorbing power from the dc-link. This article demonstrates that performing a transformation from voltage–current to power-energy domain by feedback linearization-based nonlinear control simultaneously eliminates the unstable mode and increases control bandwidth. Revealed findings are well-supported by simulations and experiments.
TL;DR: The effectiveness of the proposed nonlinear hybrid controller for swinging-up and stabilizing the (under-actuated) rotary inverted pendulum system is demonstrated and the good robustness against sudden external disturbances is achieved.
Abstract: In this paper, we propose a new class nonlinear hybrid controller (NHC) for swinging-up and stabilizing the (under-actuated) rotary inverted pendulum system. First, the swing-up controller, which drives the pendulum up towards the desired upright position, is designed based on the feedback linearization and energy control methods. Then, the modified super-twisting sliding mode control is proposed based on the new sliding surface to stabilize both the fully-actuated (the rotary arm) and under-actuated (the pendulum) state variables. In the proposed NHC, around the upright position, the stabilization controller is applied, and in different circumstances aside from the upright position, the swing-up controller is used. We show that with the proposed NHC: (i) in the swing-up stage, the pendulum is able to reach the desired upright position; and (ii) in the stabilization stage, the closed-loop rotary inverted pendulum is asymptotically stable. We demonstrate the effectiveness of the proposed NHC through extensive experiments. In particular, (i) the faster swing-up under the similar control effort is obtained, compared with the existing fuzzy logic swing-up controller; (ii) the better stabilization control performance for the convergence of the angular positions of the rotary arm and pendulum is attained and the chattering is alleviated, compared with the existing sliding mode stabilization controllers; (iii) the better stabilization control accuracy with the faster convergence time and lower peak overshoot is accomplished, compared with the existing Fuzzy-LQR controller; and (iv) the good robustness against sudden external disturbances is achieved.
TL;DR: Rigorous proof shows the finite-time stability of the formation control algorithm, boundedness of the control inputs and non-existence of the unexpected Zeno behavior.
Abstract: Considering formation control of multi-UAV system subject to input saturation, the issue of achieving predefined configuration in a distributed finite-time event-triggered scheme is investigated Precise feedback linearization based on differential geometry theory is utilized to linearize the nonlinear motion model of unmanned aerial vehicles A fixed-time convergent observer is skillfully constructed to estimate the leader’s velocity information with accuracy and quickness A novel distributed event-triggered finite-time formation control protocol incorporated by saturation functions is proposed to achieve the desired formation in finite time An estimation of the finite-settling time is conducted by subtly constructing the Lyapunov function Rigorous proof shows the finite-time stability of the formation control algorithm, boundedness of the control inputs and non-existence of the unexpected Zeno behavior Numerical simulations are performed to demonstrate the effectuality of the theoretical results
TL;DR: In this paper, a feedback linearization based direct torque control (FL-DTC) scheme was designed for interior permanent magnet synchronous motors drives, which yields the exact input-output linearization decoupling of the closed-loop system.
Abstract: A feedback linearization based direct torque control (FL-DTC) scheme is designed for interior permanent magnet synchronous motors drives. Several main aspects related to FL are studied thoroughly. A proper choice of two new state variables (the torque and the squared amplitude of stator flux) yields the exact input–output linearization decoupling of the closed-loop system. The FL-DTC frame is obtained after finding a feasible diffeomorphism. For the partially linearized subsystem, i.e., the independent torque and flux loops, two FL-DTC-based controller design solutions are provided. The control problem of internal dynamics is converted to an absolute tracking problem of an equivalent Lur'e system.