Robust Adaptive Attitude Control for Non-rigid Spacecraft With Quantized Control Input

In this paper,an adaptive backstepping control scheme is proposed for attitude tracking of non-rigid spacecraft in the presence of input quantization,inertial uncertainty and external disturbance.TThe control signal for each actuator is quantized by sector-bounded quantizers,including the logarithmic quantizer and the hysteresis quantizer.By describing the impact of quantization in a new affine model and introducing a smooth function and a novel form of the control signal,the influence caused by input quantization and external disturbance is properly compensated for.Moreover,with the aid of the adaptive control technique,our approach can achieve attitude tracking without the explicit knowledge of inertial parameters.Unlike existing attitude control schemes for spacecraft,in this paper,the quantization parameters can be unknown,and the bounds of inertial parameters and disturbance are also not needed.In addition to proving the stability of the closed-loop system,the relationship between the control performance and design parameters is analyzed.Simulation results are presented to illustrate the effectiveness of the proposed scheme.

Time-optimal control of multiple unmanned aerial vehicles with human control input

Purpose–The purpose of this paper is to investigate time-optimal control problems for multiple unmanned aerial vehicle(UAV)systems to achieve predefined flying shape.Design/methodology/approach–Two time-optimal protocols are proposed for the situations with or without human control input,respectively.Then,Pontryagin’s minimum principle approach is applied to deal with the time-optimal control problems for UAV systems,where the cost function,the initial and terminal conditions are given in advance.Moreover,necessary conditions are derived to ensure that the given performance index is optimal.Findings–The effectiveness of the obtained time-optimal control protocols is verified by two contrastive numerical simulation examples.Consequently,the proposed protocolscan successfully achieve the prescribed flying shape.Originality/value–This paper proposes a solution to solve the time-optimal control problems for multiple UAV systems to achieve predefined flying shape.

New model reference adaptive control with input constraints

A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and conventional direct model reference adaptive control scheme,various modifications have been employed to achieve the goal. ＂C omposite model reference adaptive control＂of higher performance is seam-lessly combined with ＂positive μ-mod＂,which consequently results in a smooth tracking trajectory despite of the input constraints. In addition,bounded-gain forgetting is utilized to facilitate faster convergence of parameter estimates. The stability of the closed-loop systemcan be guaranteed by using Lyapunov theory.The merits and effectiveness of the proposed method are illustrated by a numerical example of the longitudinal dynamical systems of a fixed-wing airplane.

Multivariable Decoupling Predictive Control with Input Constraints and Its Application on Chemical Process

A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.

Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.

Adaptive fault-tolerant controller design for airbreathing hypersonic vehicle with input saturation

The problem of fault-tolerant control is discussed for the longitudinal model of an airbreathing hypersonic vehicle （AHV） with actuator faults and external disturbances. Firstly, a fault-tolerant control strategy is presented for the longitudinal model of an AHV, which guarantees that velocity and altitude track their reference trajectories at an exponential convergence rate. However, this method needs to know the minimum value of the actuator efficiency factor and the upper bound of the external disturbances, which makes it not easy to implement. Then an improved adaptive fault-tolerant control scheme is proposed, where two adaptive laws are employed to estimate the upper bound of the external disturbances and the minimum value of the actuator efficiency factor, respectively. Secondly, the problem of designing a control scheme with control constraints is further considered, and a new adaptive fault-tolerant control strategy with input saturation is designed to guarantee that velocity and altitude track their reference trajectories. Finally, simulation results are given to show the effectiveness of the proposed methods.

Neural Network Based Adaptive Tracking Control for a Class of Pure Feedback Nonlinear Systems With Input Saturation

In this paper, an adaptive neural networks(NNs)tracking controller is proposed for a class of single-input/singleoutput(SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach, the original input saturated nonlinear system is augmented by a low pass filter.Then, new system states are introduced to implement states transformation of the augmented model. The resulting new model in affine Brunovsky form permits direct and simpler controller design by avoiding back-stepping technique and its complexity growing as done in existing methods in the literature.In controller design of the proposed approach, a state observer,based on the strictly positive real(SPR) theory, is introduced and designed to estimate the new system states, and only two neural networks are used to approximate the uncertain nonlinearities and compensate for the saturation nonlinearity of actuator. The proposed approach can not only provide a simple and effective way for construction of the controller in adaptive neural networks control of non-affine systems with input saturation, but also guarantee the tracking performance and the boundedness of all the signals in the closed-loop system. The stability of the control system is investigated by using the Lyapunov theory. Simulation examples are presented to show the effectiveness of the proposed controller.

Control for the synchronization of Chen system via a single nonlinear input

This paper addresses control for the synchronization of Chen chaotic systems via sector nonlinear inputs. Feedback control, adaptive control, fast sliding mode and robust control approaches based on single state feedback controller are investigated. In these cases, sufficient conditions for the synchronization are obtained analytically. Numerical simulations verify the control performances.

Second-Order Consensus of Multiple Agents with Bounded Control Inputs and Preserved Network Connectivity

This paper investigates second-order consensus of multi-agent systems with a virtual leader of varying velocity while preserving network connectivity.We propose a novel second-order consensus algorithm with bounded control inputs.Under the condition that the initial network is connected,the network will be connected all the time and all agents and the virtual leader can attain the same position and move with the same velocity.A simulation example is proposed to illustrate the effective of the proposed algorithm.

Neural adaptive chaotic control with constrained input using state and output feedback

This paper presents neural adaptive control methods for a class of chaotic nonlinear systems in the presence of constrained input and unknown dynamics. To attenuate the influence of constrained input caused by actuator saturation, an effective auxiliary system is constructed to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks（RBF-NNs） are used in the online learning of the unknown dynamics, which do not require an off-line training phase. Both state and output feedback control laws are developed. In the output feedback case, high-order sliding mode（HOSM） observer is utilized to estimate the unmeasurable system states. Simulation results are presented to verify the effectiveness of proposed schemes.

Tracking Control of Uncertain Nonlinear Systems With Unknown Constant Input Delay

A robust delay compensator has been developed for a class of uncertain nonlinear systems with an unknown constant input delay.The control law consists of feedback terms based on the integral of past control values and a novel filtered tracking error,capable of compensating for input delays.Suitable Lyapunov-Krasovskii functionals are used to prove global uniformly ultimately bounded(GUUB)tracking,provided certain sufficient gain conditions,dependent on the bound of the delay,are satisfied.Simulation results illustrate the performance and robustness of the controller for different values of input delay.

An Upper Limit for Iterative Learning Control Initial Input Construction Using Singular Values

Selecting a proper initial input for Iterative Learning Control (ILC) algorithms has been shown to offer faster learning speed compared to the same theories if a system starts from blind. Iterative Learning Control is a control technique that uses previous successive projections to update the following execution/trial input such that a reference is followed to a high precision. In ILC, convergence of the error is generally highly dependent on the initial choice of input applied to the plant, thus a good choice of initial start would make learning faster and as a consequence the error tends to zero faster as well. Here in this paper, an upper limit to the initial choice construction for the input signal for trial 1 is set such that the system would not tend to respond aggressively due to the uncertainty that lies in high frequencies. The provided limit is found in term of singular values and simulation results obtained illustrate the theory behind.

Finite-time Feedback Stabilization of a Class of Input-delay Systems With Saturating Actuators via Digital Control

In this paper, the problem of making an input-delay system with saturating actuators finite-time stable by virtue of digital control is investigated. A digital state feedback controller and digital observer-controller compensator are designed for two cases: when the state of the input-delay system are available or when it is unavailable. Sufficient conditions which guarantee finite-time stability of a closed-loop input-delay system are given and the proof procedure is presented in a heuristic way by constructing appropriate comparison functions. The condition can be transformed into the intersection of two curves satisfying some constraints, which reveals the relationship between designed parameters clearly. Finally, simulation results are presented to validate the method proposed in this paper.

Finite-time control of chaotic systems with nonlinear inputs

A finite-time controller is designed for a class of nonlinear systems subject to sector nonlinear inputs. A novel and simple approach is suggested based on the finite-time control principle. The designed sliding-mode controller can drive a chaotic system to track a smooth target signal in a finite time. The chaotic Duffing-Holmes oscillator is used for verification and demonstration.

Robust Adaptive Control for Robotic Systems With Input Time-Varying Delay Using Hamiltonian Method

This paper addresses the problem of robust adaptive control for robotic systems with model uncertainty and input time-varying delay. The Hamiltonian method is applied to develop the stabilization results of the robotic systems. Firstly, with the idea of shaping potential energy and the pre-feedback skill, the n degree-of-freedom(DOF) uncertain robotic systems are realized as an augmented dissipative Hamiltonian formulation with delay.Secondly, based on the obtained Hamiltonian system formulation and by using of the Lyapunov-Krasovskii(L-K) functional method, an adaptive controller is designed to show that the robotic systems can be asymptotically stabilized depending on the input delay. Meanwhile, some sufficient conditions are spelt out to guarantee the rationality and validity of the proposed control law. Finally, study of an illustrative example with simulations shows that the controller obtained in this paper works very well in handling uncertainties and input delay in the robotic systems.

Control Design for Harmonic Disturbance Rejection for Robot Manipulators with Bounded Inputs

Currently most of control methods are of one degree of freedom（1-DOF） control structure for the robot systems which are affected by unmeasurable harmonic disturbances,at the same time in order to obtain perfect disturbance attenuation level,the controller gain must be increased.In practice,however,for robotic actuators,there are physical constraints that limit the amplitude of the available torques.This paper considers the problem of tracking control under input constraints for robot manipulators which are affected by unmeasurable harmonic disturbances.A new control scheme is proposed for the problem,which is composed of a parameter-dependent nonlinear observer and a tracking controller.The parameter-dependent nonlinear observer,designed based on the internal model principle,can achieve an estimation and compensation of a class of harmonic disturbances with unknown frequencies.The tracking controller,designed via adaptive control techniques,can make the systems asymptotically track the desired trajectories.In the control design,the continuous piecewise differentiable increasing function is used to limit control input amplitude,such that the control input saturation is avoided.The Lyapunov stability of closed loop systems is analyzed.To validate proposed control scheme,simulation results are provided for a two link horizontal robot manipulator.The simulation results show that the proposed control scheme ensures asymptotic tracking in presence of an uncertain external disturbance acting on the system.An important feature of the methodology consists of the fact that the designed controller is of 2-DOF control structure,namely,it has the ability to overcome the conflict between controller gain and robustness against external disturbances in the traditional 1 -DOF control structure framework.

Multimodel-based flight control system reconfiguration control in the presence of input constraints

In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.

An on-line data acquisition system of oscilloscope-embedded input/output controller for cavity BPM measurement

Cavity beam position monitor(BPM) is widely used in a precise electron beam position measurement. Based on high performance oscilloscope-embedded EPICS input/output controller,we developed an on-line cavity BPM signal processing system for fast data acquisition solution when designing a cavity BPM.Also,methods for extracting the position information from cavity pickup signals and calibration algorithm are included in this solution.

Robust predictive control of uncertain intergrating linear systems with input constraints

This paper presents a two-stage robust model predictive control (RMPC) aigorithm named as IRMFC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence of the resulted closed loop system is guaranteed under mild assumption. The simulation example shows its validity and better performance than conventional Min-Max RMPC strategies.