Input-output Feedback Research Articles

Automatic control of Hypothalamus-Pituitary-Adrenal axis dynamics

Background and ObjectiveIn this study, a presentation is made for the automatic control of the hypothalamus-pituitary-adrenal axis which plays an important role in the immune stress responses and the circadian rhythms of mammalian organisms. MethodsControl approaches are implemented on a novel second order nonlinear system which accepts adrenocorticotropin hormone as an input and models the variation of plasma concentrations of adrenocorticotropin and cortisol respectively. The control methods are based on back-stepping and input-output feedback linearization techniques. The controllers adjust the adrenocorticotropin injection to maintain the daily rhythm of the cortisol concentration. In accordance with the periodicity of biological clock mechanism, we provide a sinusoidally varying cortisol reference to the controllers. ResultsNumerical simulations are performed (on MATLAB) to demonstrate the closed loop performance of the controllers. Major concerns in the selection of the control gains are chattering and negative concentration in responses. The simulation results showed that one can successfully find gain levels which do not lead to those issues. However, the gains lie in different ranges for back-stepping and feedback linearization based controllers. ConclusionThe results showed that, both back-stepping and feedback linearization based controllers fulfilled their duty of synchronization of the cortisol concentration to a reference daily periodic rhythm. In addition to that, the risk of negative valued adrenocorticotropin injection can be eliminated by properly choosing the controller gains.

On Nonlinear Control of Single-Phase Converters With Active Power Decoupling Function

In this paper, a nonlinear controller is presented for regulating an emerging class of high power density, high power-conversion efficiency, and high reliability (H3) single-phase power converters featuring small buffering capacitors. The proposed controller combines the best features of input–output feedback linearization and an automatic-power-decoupling (APD) control strategy, and achieves enhanced dynamic performances as compared to existing solutions based on linear control techniques. By feedback linearization, the plant models of H3 single-phase converters are fully linearized and decoupled, and thus fast dynamics, large-signal asymptotic tracking, and global stability can be achieved with simple linear controllers. Additionally, the APD strategy further strengthens the robustness of the closed-loop system as active pulsating power buffering (of the instantaneous power difference between the ac-port and the dc-port of the converter) of basically any form can be achieved. With the proposed nonlinear controller, we are able to further exploit the potential and unlock several new features intrinsic in H3 single-phase converters, e.g., active voltage holdup function, allowing extended holdup time (comparable to existing products with large dc-link capacitors) with only a small energy storage. The general theory, design procedures, and practical considerations of the nonlinear controller are detailed. A 100-W hardware prototype is also built to demonstrate the advantages of the proposed solution. New opportunities of H3 single-phase power converters with the aid of the proposed nonlinear control are also suggested for future research.

Model predictive maneuvering control and energy management for all-electric autonomous ships

Over the last few years, autonomous shipping has been under extensive investigation by the scientific community where the main focus has been on ship maneuvering control and not on the optimal use of energy sources. In this paper, the purpose is to bridge the gap between maneuvering control, energy management, and the control of the Power and Propulsion System (PPS) to improve fuel efficiency and the performance of the vessel. Maneuvering control, energy management, and the control of the PPS are in the literature typically studied independently from one another, while they are closely connected. A generic control methodology based on receding horizon control techniques is proposed for the ship maneuvering control as well as energy management. In the context of this research, Direct Current (DC) all-electric architectures are considered for the PPS where the relationship between the produced power by energy sources and vessel propellers is established by a DC microgrid. The objective of the proposed approach is to ensure the ship mission objectives by guaranteeing efficient power availability, decreasing the trajectory tracking error, and increasing the fuel efficiency. In this regard, for the ship motion control, a Model Predictive Control (MPC) algorithm is proposed which is based on Input–Output Feedback Linearization (IOFL). Through this algorithm, the required power for the ship mission is predicted and then, transferred to the proposed Predictive Energy Management (PEM) algorithm which decides on the optimal split between different on-board energy sources during the mission. As a result, the fuel efficiency and the power system stability can be increased. Several simulations are carried out for the evaluation of the proposed approach. The results suggest that by adopting the proposed approach, the trajectory tracking error decreases and the Specific Fuel Consumption (SFC) efficiency is significantly improved.

Robust adaptive fuzzy sliding mode control of nonlinear uncertain MIMO fluttering FGP plate based on feedback linearization

In this study using an adaptive fuzzy sliding mode control (AFSMC) scheme, the robust stabilization of multi-input–multi-output (MIMO) nonlinear aero-elastic fluttering of the Functionally Graded Piezoelectric (FGP) plate in the presence of mismatched time-varying uncertainties have been investigated. It is assumed that the aerodynamic load is modeled by the first order piston theory and the piezoelectric patches are assumed to be bonded to the top and bottom surfaces of the plate in order to produce the controlling bending moment excitations. Using the airy stress function and applying the Hamilton's principle the governing coupled partial differential equations of motion are derived. Then considering the immovable simply supported edges boundary conditions and employing the aero-elastic multi-mode interactions and applying the Galerkin's method, the nonlinear coupled partial differential equations of motion are reduced to nonlinear ordinary differential equations in time. Then using the full state input-output feedback linearization technique, the nonlinear dynamics of the model is linearized and transformed into the multiple decupled single input-single output uncertain subsystems. In order to overcome the chattering phenomenon arises due to the sliding mode control (SMC) discontinuous inputs, a hybrid adaptive fuzzy sliding mode control technique is utilized to approximate the discontinuous synthetic control inputs. It is showed that considering the physical input limitations, the designed AFSMC control system, effectively suppress the fluttering motions in presence of the bounded external inaccuracies and it prevents the unwanted chattering of the subsystems inputs.

A fault monitoring approach using model-based and neural network techniques applied to input–output feedback linearization control induction motor

This paper presents a contribution to the fault monitoring approach and input–output feedback linearization control of the induction motor (IM) in the closed-loop drive. Two kinds of faults are considered in the machine, particularly the broken rotor bars and stator inter-turn short circuit faults. This approach has been employed to detect and identify simple and mixed defects during motor operation by utilizing advanced techniques. To achieve it, two procedures are applied for the fault monitoring: The model-based strategy, which used to generate a residual speed signal to indicate the presence of possible failures, by means the high gain observer in the closed-loop drive. However, this strategy is not able to recognise the type of faults but it can be affected by the disturbances. Therefore, the neural network (NN) technique is applied in order to identify the faults and distinguish them. However, the NN required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform and fast Fourier transform is applied to extract the amplitude of the harmonics and used them as an input data set for NN. The obtained results show the efficiency of the fault monitoring system and its ability to detect and diagnosis any minor faults in a closed loop of the IM.

Sensorless Non-linear Control Applied to a PMSM Machine Based on New Extended MVT Observer

The focus of this paper is on the notion of an extended observer based on Input–output feedback linearization control. The mean value theorem (MVT) and sector non-linearity approaches were implemented for a class of the Lipchitz model of Permanent Magnet Synchronous Machine (PMSM). Based on these mathematical approaches, the proposed design allows the expression of nonlinear error dynamics for the extended observer become a convex combination of known matrices that have time varying coefficients. This is similar to a linear parameter varying (LPV) systems after the non-linear system of the PMSM is represented in the Lipchitz form. Through the Lyapunov theory, one can obtain and express the stability conditions in a term of linear matrix inequalities (LMI). The extended observer gain is separately determined based on the separation principles by the exploitation of YALMIP software. Furthermore, this gain have complete independence from the PMSM states. As a proof of the efficacy of the proposed approach, one applies an illustrative simulation to the sensorless Field Oriented Control (FOC) of the PMSM drive by utilising the MATLAB/SIMULINK environment.

Adaptive Input-Output Feedback Linearization Control of Doubly-Fed Induction Machine in Wind Power Generation

The present paper aims to present an adaptive, nonlinear control of wind energy conversion system using doubly fed induction generator (DFIG). The aim is to track the optimal torque-speed characteristic of the wind turbine in order to extract the maximum power, as well as to control the reactive power transmitted to the electrical grid. The adaptive nonlinear controller is designed on the basis of input-output feedback linearization control (FLC) with the full order model of the DFIG in the fixed stator d, q axis reference frame. In order to ensure the system stability under uncertain parameters such as rotor and stator windings resistances and the aerodynamic torque which comes to compromise the robustness and the efficiency of the controller, update laws in real time of the uncertain parameters are established. The global stability of the system is proven using Lyapunov's stability theory. Simulation results have been carried out in Matlab/Simulink in presence of uncertain parameters also, the comparison between the control with and without adaptation confirm the validity and robustness of proposed controller.

CONDITIONS FOR COMPLETE FEEDBACK LINEARISATION ON ACTIVE SUSPENSION SYSTEMS FOR MINING TRUCKS

Abstract This paper presents the design and implementation of a mining truck half-car active vehicle suspension system with a pre-disturbance detection algorithm that aims at reducing driver experienced vibrations. It addresses the complexities and limitations of input output feedback linearisation by addressing challenges of hidden state analysis involving jacobian invertibility and zero dynamics analysis on suspension models. The novelty of this paper entails adopting input state linearisation conditions to develop a set of states that result in complete linearisation having no hidden states. Negative state feedback controller is applied to the linearised system and the results demonstrate improved attenuated displacement and smoother accelerations hence cushioning the sprung load. A test bed is developed and integrated with the controller through the use of Matlab, Simulink and DSpace to demonstrate the active suspension system prototype results. Respectively 81.82 % and 97.5 % displacement and acceleration attenuation are achieved by the implemented linear controller.

Robust Control for High Performance Induction Motor Drives Based on Partial State-Feedback Linearization

This paper deals with a robust input–output feedback linearization control technique for induction motors. Indeed, classic feedback linearization presents two main disadvantages: 1) the accuracy of the dynamic model; and 2) the corresponding correct knowledge of the model parameters. To address this issue, the linear controller has been substituted with a suitably controller designed to be robust to the variations of the main parameters of the induction motor, like stator and rotor resistances, and the three-phase magnetizing inductance. The proposed controller has been tested both in numerical simulation and experimentally on a suitably designed test setup. Moreover, it has been compared with the classical feedback linearization based on linear controllers, highlighting the improvements in terms of dynamic performance, when parameter variations occur. Results confirm a significant increase of the robustness of the controller against parameter variations.

Dynamic Virtual Fixtures Based on Path Following Control

This paper presents a path following control (PFC) approach to generate guidance virtual fixtures with dynamic constraints (time-varying constraints) for physical human-robot interaction tasks. Using input-output feedback linearization and a parallel transport frame, the system dynamics are linearized and decomposed into tangential and orthogonal direction with respect to a time-varying path, which is parametrized as a regular threefold continuously differentiable curve. The presented PFC approach is combined with compliance control, which enables the systematic generation of virtual fixtures. An experiment on a six-axis industrial robot reveals the potential of this approach to generate virtual fixtures with dynamic constraints.

Adaptive Torque and Flux Control of Sensorless IPMSM Drive in the Stator Flux Field Oriented Reference Frame

In this paper, direct torque and flux control of interior permanent magnet synchronous motor has been described in the stator flux field-oriented reference frame based on adaptive input-output feedback linearization (AIOFL) control method. The proposed control method does not need to know the motor two-axis inductances, the rotor permanent magnetic flux linkages, and the rotor position. However, the knowledge of the stator resistance is mandatory and that is estimated by AIOFL method. In practice, the rotor speed is approximately estimated by using the derivation of the stator flux vector angle in the stator stationary reference frame. The experimental and simulation results presented in this paper show the effectiveness and capability of the proposed control approach.

Model Reference Adaptive Control Design for Nonlinear Plants

In this paper, the basic theory of the model reference adaptive control design and issues of particular relevance to control nonlinear dynamic plants with a relative degree greater than or equal to one with unknown parameters are detailed. The studied analysis was motivated through its application to a robot manipulator with six degrees of freedom. After linearization using the input-output feedback linearization and decoupling algorithm, the nonlinear Multi-input Multi-output system was transformed into six independent single-input single-output linear subsystems each one has a relative degree equal to two, the obtained results in different simulations shows that the augmented reference model adaptive controller has been successfully implemented.

Input-Output Feedback Linearization for the Control of a 4 Cable-Driven Parallel Robot

Abstract This paper presents the control of an under-constrained 4 Cable-Driven Parallel Robot (CDPR) using input-output feedback linearization technique. The dynamic model of the CDPR is first formulated by taking into account the Euler angle rates. Following this, the input-output feedback linearization method is implemented to decouple the output and input. A linear feedback controller is then designed using pole placement method to control the CDPR. The control law is then verified by simulation using MATLAB software. Simple trajectories are then tested with and without the presence of noise to analyze the behavior of the control law.

Real-time Neural Input–Output Feedback Linearization control of DFIG based wind turbines in presence of grid disturbances

This paper proposes a Neural Input–Output Feedback Linearization (N-IOFL) controller for a Doubly Fed Induction Generator (DFIG) prototype connected to the grid. Under unbalanced grid voltages, the existing control strategies need to be modified, becoming very complicated. By using an identifier based on Recurrent High Order Neural Network (RHONN), trained on-line with an Extended Kalman Filter (EKF), an adequate model of the DFIG and of the DC-link can be obtained, which helps the control law based on feedback linearization to reject grid disturbances appearing under non-ideal grid conditions without decomposition process. Based on such identification, the proposed controller is used to track a desired Direct Current (DC) voltage reference at the output of DC-link, to maintain constant the electric power factor controlled by the Grid Side Converter (GSC), and to force independently the rotor currents to track a specified reference defined from the required stator powers, controlled by the Rotor Side Converter (RSC), under both balanced and unbalanced grid conditions. Real-time results illustrate the effectiveness of the proposed controller even in presence of non-ideal grid conditions.

Input-output tracking control of a 2-DOF laboratory helicopter with improved algebraic differential estimation

This paper presents a study on input-output feedback linearization control (OFLC) of a laboratory twin rotor helicopter system based on an improved algebraic differential estimation approach. In the previous algebraic differential estimation studies, the reset time Tr is always selected cautiously in a narrow range because the estimation accuracy is very sensitive to Tr. Too small or too large reset time Tr will lead to an inaccurate estimation of signal derivatives. In this paper, we first propose a new resetting and overlapping strategy for improving the robustness of the estimation algorithm. Then it is used to estimate the velocities of pitch and yaw motions. The OFLC is designed and verified by letting the output signals track different periodic reference signals. Both simulation and experimental results show better control performance as compared with the LQR design.

Trajectory Tracking and Path Following for Underactuated Marine Vehicles

In this paper, we present a control strategy for trajectory tracking and path following of generic paths for underactuated marine vehicles. Our work is inspired and motivated by previous works on ground vehicles. In particular, we extend the definition of the hand position point, introduced for ground vehicles, to autonomous surface vehicles and autonomous underwater vehicles, and then use the hand position point as output for a control strategy based on the input–output feedback linearization method. The presented strategy is able to deal with external disturbances affecting the vehicle, e.g., constant and irrotational ocean currents. Using the Lyapunov analysis, we are able to prove that the closed-loop system has an external dynamics that is globally exponentially stable and an internal dynamics that has ultimately bounded states, both for the trajectory tracking and the path following control problems. Finally, we present a simulation case study and experimental results in order to validate the theoretical results.

A Load Transportation Nonlinear Control Strategy Using a Tilt-Rotor UAV

This paper proposes a nonlinear control strategy to solve the trajectory tracking problem of a tilt-rotor Unmanned Aerial Vehicle (UAV) when transporting a suspended load. For the present study, the aim of the control system is to track a desired trajectory of the aircraft with load’s swing-free, even in the presence of external disturbances, parametric uncertainties, unmodeled dynamics, and noisy position measurements with lower sampling frequency than the controller. The whole system modeling is obtained through the Euler-Lagrange formulation considering the dynamics of the tilt-rotor UAV coupled to the suspended load. As for the nonlinear control strategy, an inner-loop control is designed based on input-output feedback linearization combined with the dynamic extension approach to stabilize the attitude and altitude of the UAV assuming nonlinearities, while an outer-loop control law is designed for guiding the aircraft with reduced load swing. The linearized dynamics are controlled using linear mixed H2/H∞ controllers with pole placement constraints. The solution is compared to two simpler control systems: the first one considers the load as a disturbance to the system but does not avoid its swing; the second one is a previous academic result with a three-level cascade strategy. Finally, in order to deal with the problem of position estimation in presence of unknown disturbances and noisy measurements with low sampling frequency, a Linear Kalman Filter with Unknown Inputs is designed for estimating both the aircraft’s translational position and translational disturbances. Simulation results are carried out to corroborate the proposed control strategy.

Dynamic neural network-based feedback linearization control of full-car suspensions using PSO

This paper proposes a nonlinear control approach using dynamic neural network-based input–output feedback linearization to resolve the inherent conflicting performance criteria for a full-car nonlinear electrohydraulic active vehicle suspension system. Particle swarm optimization is applied both for the dynamic neural network models’ trainings and the computation of the controllers’ parameters. The intelligent control scheme outperformed the passive vehicle suspension system and the benchmark particle swarm-optimized proportional+integral+derivative controller. Effectiveness and robustness of the proposed controller are demonstrated through simulations both in time- and frequency-domains.

Robust Trajectory‐Tracking Control of a PVTOL under Crosswind

AbstractThis work proposes a robust controller to solve the trajectory‐tracking control problem of planar vertical take‐off and landing (PVTOL) aircraft under crosswind. The controller combines input–output feedback linearization and active disturbance rejection control techniques. The former linearizes the PVTOL dynamics and the latter actively estimates and compensates for the crosswind effects. Numerical simulations assess the effectiveness of the proposed approach.

Fault-Tolerant Cooperative Control Design of Multiple Wheeled Mobile Robots

This brief investigates fault-tolerant cooperative control (FTCC) strategies for multiple differentially driven autonomous wheeled mobile robots (WMRs) in the presence of actuator faults during formation operation. First, for normal/fault-free cases and for preparation to the faults occurrence cases, an integrated approach combining input-output feedback linearization and distributed linear model predictive control techniques is designed and implemented on a team of WMRs to accomplish a formation task. Second, when actuator faults occur in one of the robots of the team, two cases are explicitly considered: 1) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are reassigned to the remaining healthy robots to complete the mission with graceful performance degradation and 2) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure their controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, a fault detection and diagnosis unit using a two-stage Kalman filter to detect and diagnose actuator faults is presented. Then, the FTCC problem is formulated as an optimal assignment problem, where a Hungarian algorithm is applied. Moreover, a collision avoidance algorithm based on mechanical impedance principle is proposed to avoid the potential collision between the healthy robots and the faulty ones. Formation operation of the robot team is based on a leader-follower approach, while the control algorithm is implemented in a distributed manner. The results of real experiments demonstrate the effectiveness of the proposed FTCC scheme in different fault scenarios.