Desired Control Input Research Articles

An optimal control design for bidirectional inductive power transfer system using dynamics identification

In this article, we address the tracking control problem for the bidirectional inductive power transfer systems (BIPTS) in the view of optimal performance. To achieve the optimal destination with respect to a quadratic value function, an identified linear model for the BIPTS is first developed, and it is proved to be convergent to the true dynamic model when the persistent excitation condition is satisfied. And then, based on the identified model, the desired system states and control inputs are derived according to the reference transferring power, and be utilized as the feedback signals and feedforward compensation, respectively, for the optimal controller. A hybrid Lyapunov function is developed to guarantee the closed-loop stability and the asymptotically tracking convergence. A numerical simulation is carried out to illustrate the satisfactory control performance.

Vibration Control of a Flexible Spacecraft System With Input Backlash

In this paper, we deal with the vibration control problem of a flexible spacecraft system with unknown external disturbance and uncertain input backlash nonlinearity. The considered system is described by two partial differential equations and an ordinary differential equation as governing equations, and by ordinary differential equations as boundary conditions. The backlash nonlinearity is reformulated into the desired control input associated with an extra input nonlinear error. This input error and the external disturbance are combined into an unknown “disturbance-like” item. Two boundary control inputs are designed at the center body of the spacecraft, compensating for the unknown upper-bound of such items by applying proper online updating laws. As a result, the vibration of both solar panels of the flexible spacecraft is suppressed and their angle positions are regulated in the desired region. The numerical simulations are provided to verify the control performance of the proposed controls by the choice of proper parameters.

COMPLEX LAPLACIAN-BASED DISTRIBUTED CONTROL FOR MULTI-AGENT NETWORK

This paper, proposes a complex Laplacian-based distributed control scheme for convergence in the multi-agent network. The proposed scheme has been designated as cascade formulation. The proposed technique exploits the traditional method of organizing large scattered networks into smaller interconnected clusters to optimize information flow within the network. The complex Laplacian-based approach results in a hierarchical structure, with the formation of a meta-cluster leading other clusters in the network. The proposed formulation enables flexibility to constrain the eigenspectra of the overall closed-loop dynamics, ensuring desired convergence rate and control input intensity. The sufficient conditions ensuring globally stable formation for the proposed formulation are also asserted. Robustness of the proposed formulation to uncertainties like loss in communication links and actuator failure have also been discussed. The effectiveness of the proposed approach is illustrated by simulating a finitely large network of 30 vehicles.

Input and Output Constraints in Iterative Learning Control Design for Robotic Manipulators

Motivated by the safety requirement of rehabilitation robotic systems for after stroke patients, this paper handles position or output constraints in robotic manipulators when the patients repeat the same task with the robot. In order to handle output constraints, if all state information is available, a state feedback controller can ensure that the output constraints are satisfied while iterative learning control (ILC) is used to learn the desired control input through iterations. By incorporating the feedback control using barrier Lyapunov function with feed-forward control (ILC) carefully, the convergence of the tracking error, the boundedness of the internal state, the boundedness of input signals can be guaranteed along with the satisfaction of the output constraints over iterations. The effectiveness of the proposed controller is demonstrated using simulations from the model of EMU, a rehabilitation robotic system.

Robust H∞ fault tolerant control for quadrotor attitude regulation

This article investigates the robust H∞ fault tolerant control of quadrotor attitude regulation. The hover stability is heavily dependent on the thrust forces generated by the four rotors of the quadrotor. If faults occur in the rotors, the quadrotor becomes unstable, and possibly out of control. To this end, this article proposes a robust H∞ fault tolerant control schema, consisting of two level controllers, for regulation of the quadrotor attitude. The functions of the upper level controller and lower level controller are to generate the desired control input, and obtain the real input of the four rotors, respectively. In addition, the external disturbances are considered and attenuated by the controller. The simulation details are presented, and the effectiveness of the proposed control schema is verified.

Extended state observer-based back-stepping control for hypersonic reentry vehicle with input constraints

Abstract This paper investigates the attitude-tracking control problem of hypersonic reentry vehicle in cases of multiple uncertainties, external disturbances and input constraints. The controller design is based on synthesizing the extended state observer (ESO) into a back-stepping control technique. This control-oriented model is formulated with mismatched and matched uncertainties. They reflect the total disturbances that group different types of aerodynamic uncertainties and external moment disturbances. In order to improve the system robustness, a sigmoid function-based ESO is first proposed. This will estimate the total disturbance and is equipped with a controller. The sigmoid smooth function is also introduced for the purpose of handling the input constraints. This will approximate saturation and guarantee that the control input is bounded. Error states between the actual control input and the desired control input are integrated to compensate for the saturation effect. Following this, the stability of the closed-loop system is proved within the Lyapunov theory framework. Several simulations are then investigated to illustrate the effectiveness of the proposed constrained attitude control scheme.

Information fusion estimation–based flight control of a nonlinear jumbo jet under wind disturbance

SummaryWind disturbance has a large impact on the safety and comfort of jumbo jets. An information fusion estimation–based control method, which is based on the linearization‐based design strategy, is proposed for the flight control of a nonlinear jumbo jet to restrain wind disturbance. A complete nonlinear model of the jumbo jet is developed based on the aerodynamic data and parameters of a Boeing jumbo jet. An information fusion control (IFC) scheme based on information fusion estimation is presented for the nonzero‐state tracking problem of the linear system with disturbance. The IFC scheme fuses the information of desired future states, desired control inputs, system dynamic equations, and performance index function. IFC is applied in the automatic flight control system of the jumbo jet to design the longitudinal flight controller and lateral flight controller. Simulation results show the feasibility and effectiveness of the IFC in the flight control of the jumbo jet, as compared with the PID control and the traditional linear quadratic optimal control.

Robust control of post-stall pitching maneuver based on finite-time observer

This article presents a robust finite-time maneuver control scheme for the longitudinal attitude dynamic of the aircraft with unsteady aerodynamic disturbances and input saturation. To efficiently eliminate the influence of unsteady aerodynamic disturbances, nonlinear finite-time observers are developed. Despite the existence of the nonlinearity and the coupling between aircraft states and unsteady aerodynamic disturbances, the proposed observers can still precisely estimate the unmeasurable unsteady aerodynamic disturbances in finite time. To attenuate the effect caused by input saturation, a finite-time auxiliary system is constructed. With the error between the desired control input and saturation input as the input of the auxiliary system, the additional signals are generated to compensate for the effect of input saturation. Combined with the finite-time observers and the finite-time auxiliary system, a robust finite-time backstepping attitude control design is developed. The finite-time convergence of all closed-loop system signals is rigorously proved via Lyapunov analysis method under the developed robust attitude control schemes. Finally, simulation results are presented to illustrate the effectiveness of the proposed attitude control approaches.

Receding horizon tracking control of unicycle-type robots based on virtual structure

Summary This paper considers the receding horizon tracking control of the unicycle-type robot subject to coupled input constraint based on virtual structure. The tracking position of the follower is considered to be a virtual structure point with respect to a Frenet–Serret frame fixed on the leader, and the desired control input of the follower not only depend on the input of the leader but also the separation vector. Firstly, a sufficient input condition for the leader robot is given to enable the follower to track its desired position while satisfying its inputs constraint. Secondly, receding horizon control scheme is designed for the follower robot, in which the recursive feasibility is guaranteed by developing a diamond-shaped positively invariant terminal-state region and its corresponding controller. Finally, simulation results are provided to verify the effectiveness of the scheme proposed. Copyright © 2016 John Wiley & Sons, Ltd.

Vibration control of a Timoshenko beam system with input backlash

In this study, a novel boundary control algorithm is designed for a class of Timoshenko beams with unknown external disturbance and uncertain input backlash non-linearity. The Timoshenko beam is described by partial differential equations as governing equations and ordinary differential equations as boundary conditions. The representation of input backlash is modified as a desired control input associated with input non-linear error, which will be transformed into the external disturbance. The unknown ‘disturbance-like’ item is defined based on input error and disturbance, and the boundary of this unknown ‘disturbance-like’ item will be estimated by a disturbance observer. The unknown backlash can be handled and the ultimately uniform stability of the closed-loop system is ensured. Numerical simulations are provided to verify the performance of the author's proposed control.

Attitude Control and Vibration Suppression for Flexible Spacecraft Using Control Moment Gyroscopes

AbstractThis paper presents a novel control strategy for attitude control and vibration suppression of flexible spacecraft. A set of control moment gyroscopes are distributed on the flexible structure of the spacecraft to provide control torques. The interactions between the control moment gyroscopes and the flexibilities of the structure are incorporated in the equations of motion. A nonlinear controller is first formulated to determine the desired control input for large angle three-axis attitude maneuvers and vibration suppression. Then, a steering law is designed to obtain the gimbal commands for the control moment gyroscopes. For the small attitude error stabilization, a simple adaptive controller is developed based on the linearized dynamic model. It can avoid the singularity issue of the control moment gyroscopes, while simultaneously achieving attitude stabilization and vibration suppression. Numerical examples demonstrate the efficacy of the proposed methods.

Decentralized Adaptive Fuzzy Tracking Control for Robot Finger Dynamics

In this paper, a novel design method for adaptive tracking control is proposed for robot finger dynamics. First, the dynamics are described by considering the robot finger as a large-scale system since it has many joints and multi-degrees of freedoms (DOFs). Second, by employing the direct adaptive fuzzy approximation method to approximate the unknown and desired control input signals instead of the unknown nonlinear functions, the number of adaptive design parameters obtained in the control design process is greatly reduced. Moreover, it is shown that the designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded. Finally, simulations are conducted on the Puma 560 robot manipulator, and the results show the effectiveness of the developed control design approach.

Adaptive dynamic surface control of NSVs with input saturation using a disturbance observer

An adaptive dynamic surface control (DSC) scheme is proposed for the multi-input and multi-output (MIMO) attitude motion of near-space vehicles (NSVs) in the presence of external disturbance, system uncertainty and input saturation. The external disturbance and the system uncertainty are efficiently tackled using a Nussbaum disturbance observer (NDO), and the adaptive controller is constructed by combining the dynamic surface control technique to handle the problem of “explosion of complexity” inherent in the conventional backstepping method. For handling the input saturation, an auxiliary system is designed with the same order as that of the studied MIMO attitude system. Using the error between the saturation input and the desired control input as the input of the designed auxiliary system, a series of signals are generated to compensate for the effect of the saturation in the dynamic surface control design. It is proved that the developed control scheme can guarantee that all signals of the closed-loop control system are semi-globally uniformly bounded. Finally, simulation results illustrate that the proposed control scheme can achieve satisfactory tracking performance under the composite effects of the input saturation and the external disturbance.

Fault-Tolerant Control of FWIA Electric Ground Vehicles with Differential Drive Assisted Steering

This paper investigates a novel fault-tolerant control approach against active steering system failure based on differential drive assisted steering (DDAS) on four-wheel independently actuated (FWIA) electric ground vehicles. The front-wheel steering angle can be generated with the front-wheel differential driving torque when the steering motor fails. DDAS being the only steering power source is a potential alternative to replace the regular steering system in defective situation. On this issue, this paper deploys a hierarchical control method to utilize DDAS for automatic steering. The higher-level controller is dedicated to generate the desired control input including the yaw moments based on a robust LQR approach, and the lower-level controller is responsible for control allocation and giving out the respective desired tire forces and the desired steering angle. Simulation based on a high-fidelity and full-car model indicates that DDAS-based fault-tolerant control design for FWIA electric vehicle is effective to achieve equal handling and maneuverability performance in case of the steering failure.

Determination of Space and Time Dependent Function of Internal Heat Source in Heat Conductivity Equation

In the present work we propose the method for the solution inverse heat conduction problem that consists of the identification of unknown internal heat source function depending simultaneously on space and time variables. The source function can be represented as a separable variable. We consider an inverse problem of determining the heat source function based on temperature measurements.Inverse problem is formulated as an optimization problem, a variational formulation for solving the optimization problem is given. The estimation of internal heat source is investigated with the analytical method combined with the method for approximate modal definition of temperature state and source function. The temperature state and desired control input are represented in the form of finite expansion in terms of orthogonal system of eigenfunctions. The required eigencoefficients of temperature state can be determined using temperature measurements. Then eigencoefficients of heat source function can be defined sequentially.Some numerical examples are provided to show the efficiency of the proposed method.

Neural-network-based adaptive tracking control for a class of pure-feedback stochastic nonlinear systems with backlash-like hysteresis

In this paper, we are concerned with the problem of adaptive neural network tracking control for a class of pure-feedback stochastic nonlinear systems with backlash-like hysteresis. Unlike some existing control schemes, an affine variable at each step is constructed without using the mean value theorem, and neural networks are used to approximate the unknown and desired control input signals. By introducing the additional first-order low-pass filter for the actual control input signal, the algebraic loop problem arising in pure-feedback stochastic nonlinear systems with backlash-like hysteresis is addressed. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in probability while the tracking error converges to a small neighborhood of the origin in the sense of four-moment. Finally, a simulation example is given to verify the effectiveness of the proposed scheme.

Dynamic learning from adaptive neural network control of a class of nonaffine nonlinear systems.

This paper studies the problem of learning from adaptive neural network (NN) control of a class of nonaffine nonlinear systems in uncertain dynamic environments. In the control design process, a stable adaptive NN tracking control design technique is proposed for the nonaffine nonlinear systems with a mild assumption by combining a filtered tracking error with the implicit function theorem, input-to-state stability, and the small-gain theorem. The proposed stable control design technique not only overcomes the difficulty in controlling nonaffine nonlinear systems but also relaxes constraint conditions of the considered systems. In the learning process, the partial persistent excitation (PE) condition of radial basis function NNs is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition and an appropriate state transformation, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the implicit desired control input dynamics in the stable control process and of storing the learned knowledge in memory. Subsequently, an NN learning control design technique that effectively exploits the learned knowledge without re-adapting to the controller parameters is proposed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed design techniques.

출력 제약된 Pure-Feedback 시스템의 적응 신경망 제어

This paper investigates an adaptive approximation design problem for the tracking control of output-constrained non-affine pure-feedback systems. To satisfy the desired performance without constraint violation, we employ a barrier Lyapunov function which grows to infinity whenever its argument approaches some limits. The main difficulty in dealing with pure-feedback systems considering output constraints is that the system has a non-affine appearance of the constrained variable to be used as a virtual control. To overcome this difficulty, the implicit function theorem and mean value theorem are exploited to assert the existence of the desired virtual and actual controls. The function approximation technique based on adaptive neural networks is used to estimate the desired control inputs. It is shown that all signals in the closed-loop system are uniformly ultimately bounded.

Adaptive Neural Control of a Hypersonic Vehicle in Discrete Time

The article investigates the discrete-time controller for the longitudinal dynamics of the hypersonic flight vehicle with throttle setting constraint. Based on functional decomposition, the dynamics can be decomposed into the altitude subsystem and the velocity subsystem. Furthermore, the discrete model could be derived using the Euler expansion. For the velocity subsystem, the controller is proposed by estimating the system uncertainty and unknown control gain separately with neural networks. The auxiliary error signal is designed to compensate the effect of throttle setting constraint. For the altitude subsystem, the desired control input is approximated by neural network while the error feedback is synthesized for the design. The singularity problem is avoided. Stability analysis proves that the errors of all the signals in the system are uniformly ultimately bounded. Simulation results show the effectiveness of the proposed controller.

A modified Smith predictive scheme based back-propagation neural network approach for FOPDT processes control

In this paper, a novel control scheme to deal with process uncertainties in the form of disturbance loads and modelling errors, as well as time-varying process parameters is proposed by applying the back-propagation neural network (BPNN) approach. A BPNN predictive controller that replaces the entire Smith predictor structure is initially trained offline. Lyapunov direct method is used to prove that the convergence of this BPNN is guaranteed by selecting a suitable learning rate during the learning process. However, the Smith predictor based BPNN control is an off-line training based algorithm, which is a time consuming method and requires a known process plant input from the controller. A desired control input to the process is difficult to obtain for the training of the network. As a result a group of proper training data (target control inputs and outputs) can hardly be provided. In order to overcome this problem, a BPNN with an on-line training algorithm is introduced for the control of a First Order plus Dead Time (FOPDT) process. The stability analysis is carried out using the Lyapunov criterion to demonstrate the network convergence ability. Simulation results show that this proposed online trained neural Smith predictor based controller provides excellent robustness to process modelling errors and disturbance loads, and high adaptability to time varying processes parameters.