Disturbance rejection tube model predictive levitation control of maglev trains

Magnetic levitation control technology plays a significant role in maglev trains.Designing a controller for the levitation system is challenging due to the strong nonlinearity,open-loop instability,and the need for fast response and security.In this paper,we propose a Disturbance-Observe-based Tube Model Predictive Levitation Control(DO-TMPLC)scheme combined with a feedback linearization strategy for the levitation system.The proposed strategy incorporates state constraints and control input constraints,i.e.,the air gap,the vertical velocity,and the current applied to the coil.A feedback linearization strategy is used to cancel the nonlinearity of the tracking error system.Then,a disturbance observer is implemented to actively compensate for disturbances while a TMPLC controller is employed to alleviate the remaining disturbances.Furthermore,we analyze the recursive feasibility and input-to-state stability of the closed-loop system.The simulation results indicate the efficacy of the proposed control strategy.

MULTI-LAYER PIEZOELECTRIC ACTUATOR AND ITS APPLICATION IN CONTROLLABLE CONSTRAINED DAMPING TREATMENT

A kind of novel multi-layer piezoelectric actuator is proposed and integrated with controllable constrained damping treatment to perform hybrid vibration control. The governing equation of the system is derived based on the constitutive equations of elastic, viscoelastic and piezoelectric materials, which shows that the magnitude of control force exerted by multi-layer piezoelectric actuator is the quadratic function of the number of piezoelectric laminates used but in direct proportion to control voltage. This means that the multi-layer actuator can produce greater actuating force than that by piezoelectric laminate actuator with the same area under the identical control voltage. The optimal location placement of the multi-layer piezoelectric actuator is also discussed. As an example, the hybrid vibration control of a cantilever rectangular thin-plate is numerically simulated and carried out experimentally. The simulated and experimental results validate the power of multi-layer piezoelectric actuator and indicate that the present hybrid damping technique can effectively suppress the low frequency modal vibration of the experimental thin-plate structure.

A STA Current-Constrained Control for PMSM Speed Regulation System with Function Disturbance Observer

The non-cascade permanent magnet synchronous motor control system has the advantages of simple structure and less adjustable parameters,but the non-cascade structure needs to solve the problem of over-current protection.In this paper,a current constrained control method is used to limit the starting current to a safe range.At the same time,to ensure the robustness and rapidity of the system,a super twist current constraint controller(CCSTA)is generated by combining super twist algorithm(STA)with current constraint control;Considering the diversity of internal and external disturbances,a functional disturbance observer(FDOB)is proposed to compensate the matched and unmatched disturbances,which further improves the robustness of the system.

Dual-mode Nonlinear MPC via Terminal Control Laws With Free-parameters

The paper presents a new dual-mode nonlinear model predictive control(NMPC) scheme for continuous-time nonlinear systems subject to constraints on the state and control.The idea of control Lyapunov functions for nonlinear systems is used to compute the terminal regions and terminal control laws with some free-parameters in the dual-mode NMPC framework.The parameters of the terminal controller are selected offline to estimate the terminal region as large as possible;and the parameters are optimized online to gain optimality of the terminal controller with respect to given cost functions.Then a dual-mode NMPC algorithm with varying time-horizon is formulated for the constrained system.Recursive feasibility and closed-loop stability of this NMPC are established.The example of a spring-cart is used to demonstrate the advantages of the presented scheme by comparing to the dual-mode NMPC via the linear quadratic regulator(LQR) method.

Time-Varying Asymmetrical BLFs Based Adaptive Finite-Time Neural Control of Nonlinear Systems With Full State Constraints

This paper concentrates on asymmetric barrier Lyapunov functions(ABLFs)based on finite-time adaptive neural network(NN)control methods for a class of nonlinear strict feedback systems with time-varying full state constraints.During the process of backstepping recursion,the approximation properties of NNs are exploited to address the problem of unknown internal dynamics.The ABLFs are constructed to make sure that the time-varying asymmetrical full state constraints are always satisfied.According to the Lyapunov stability and finitetime stability theory,it is proven that all the signals in the closedloop systems are uniformly ultimately bounded(UUB)and the system output is driven to track the desired signal as quickly as possible near the origin.In the meantime,in the scope of finitetime,all states are guaranteed to stay in the pre-given range.Finally,a simulation example is proposed to verify the feasibility of the developed finite time control algorithm.

Stabilizing model predictive control scheme for piecewise affine systems with maximal positively invariant terminal set

An efficient algorithm is proposed for computing the solution to the constrained finite time optimal control （CFTOC） problem for discrete-time piecewise affine （PWA） systems with a quadratic performance index. The maximal positively invariant terminal set, which is feasible and invariant with respect to a feedback control law, is computed as terminal target set and an associated Lyapunov function is chosen as terminal cost. The combination of these two components guarantees constraint satisfaction and closed-loop stability for all time. The proposed algorithm combines a dynamic programming strategy with a multi-parametric quadratic programming solver and basic polyhedral manipulation. A numerical example shows that a larger stabilizable set of states can be obtained by the proposed algorithm than precious work.

Constrained adaptive neural network control of an MIMO aeroelastic system with input nonlinearities

A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output（MIMO） aeroelastic system in the presence of wind gust,system uncertainties,and input nonlinearities consisting of input saturation and dead-zone.In regard to the input nonlinearities,the right inverse function block of the dead-zone is added before the input nonlinearities,which simplifies the input nonlinearities into an equivalent input saturation.To deal with the equivalent input saturation,an auxiliary error system is designed to compensate for the impact of the input saturation.Meanwhile,uncertainties in pitch stiffness,plunge stiffness,and pitch damping are all considered,and radial basis function neural networks（RBFNNs） are applied to approximate the system uncertainties.In combination with the designed auxiliary error system and the backstepping control technique,a constrained adaptive neural network controller is designed,and it is proven that all the signals in the closed-loop system are semi-globally uniformly bounded via the Lyapunov stability analysis method.Finally,extensive digital simulation results demonstrate the effectiveness of the proposed control scheme towards flutter suppression in spite of the integrated effects of wind gust,system uncertainties,and input nonlinearities.

Output constrained IMC controllers in control systems of electromechanical actuators

Electromechanical actuators are widely used in many industrial applications. There are usually some constraints existing in a designed system. This paper proposes a simple method to design constrained controllers for electromechanical actuators. The controllers merge the ideas exploited in internal model control and model predictive control. They are designed using the standard control system structure with unity negative feedback. The structure of the controllers is relatively simple as well as the design process. The output constraint handling mechanism is based on prediction of the control plant behavior many time steps ahead. The mechanism increases control performance and safety of the control plant. The benefits offered by the proposed controllers have been demonstrated in real-life experiments carried out in control systems of two electromechanical actuators： a DC motor and an electrohydraulic actuator.

A FAST STOCHASTIC GALERKIN METHOD FOR A CONSTRAINED OPTIMAL CONTROL PROBLEM GOVERNED BY A RANDOM FRACTIONAL DIFFUSION EQUATION

We develop a fast stochastic Galerkin method for an optimal control problem governed by a random space-fractional diffusion equation with deterministic constrained control. Optimal control problems governed by a fractional diffusion equation tends to provide a better description for transport or conduction processes in heterogeneous media. Howev- er, the fractional control problem introduces significant computation complexity due to the nonlocal nature of fractional differential operators, and this is further worsen by the large number of random space dimensions to discretize the probability space. We ap- proximate the optimality system by a gradient algorithm combined with the stochastic Galerkin method through the discretization with respect to both the spatial space and the probability space. The resulting linear system can be decoupled for the random and spatial variable, and thus solved separately. A fast preconditioned Bi-Conjugate Gradient Stabilized method is developed to efficiently solve the decoupled systems derived from the fractional diffusion operators in the spatial space. Numerical experiments show the utility of the method.

A CHARACTERISTIC FINITE ELEMENT METHOD FOR CONSTRAINED CONVECTION-DIFFUSION-REACTION OPTIMAL CONTROL PROBLEMS

In this paper, we develop a priori error estimates for the solution of constrained convection-diffusion-reaction optimal control problems using a characteristic finite element method. The cost functional of the optimal control problems consists of three parts： The first part is about integration of the state over the whole time interval, the second part refers to final-time state, and the third part is a regularization term about the control. We discretize the state and co-state by piecewise linear continuous functions, while the control is approximated by piecewise constant functions. Pointwise inequality function constraints on the control are considered, and optimal a L2-norm priori error estimates are obtained. Finally, we give two numerical examples to validate the theoretical analysis.

Multi-mesh Adaptive Finite Element Algorithms for Constrained Optimal Control Problems Governed By Semi-Linear Parabolic Equations

In this paper, we derive a posteriori error estimators for the constrained optimal control problems governed by semi-linear parabolic equations under some assumptions. Then we use them to construct reliable and efficient multi-mesh adaptive finite element algorithms for the optimal control problems. Some numerical experiments are presented to illustrate the theoretical results.

Constrained controllability of nonlinear neutral fractional integrodifferential systems

In this paper,we study the complete controllability for a class of nonlinear neutral fractional integro-differential systems in a finite interval of time by means of controls whose initial and final values can be assigned in advance.The result is achieved using the Banach fixedpoint theorem and Schauder’s fixed-point theorem.Some numerical examples are provided to demonstrate the effectiveness of the main result.

Superconvergence of Rectangular Mixed Finite Element Methods for Constrained Optimal Control Problem

We investigate the superconvergence properties of the constrained quadratic elliptic optimal control problem which is solved by using rectangular mixed finite element methods.We use the lowest order Raviart-Thomas mixed finite element spaces to approximate the state and co-state variables and use piecewise constant functions to approximate the control variable.We obtain the superconvergence of O(h^(1+s))(0<s≤1)for the control variable.Finally,we present two numerical examples to confirm our superconvergence results.

Control of sound radiation from vibrating structure using active constrained layer: Ⅱ. Numerical calculation

The solution to the state response of active constrained layer and the expression of the sound field radiated from the vibrating structure are given, and the damping mechanisms of active constrained layer (ACL) are discussed. Compaxisons are made with the passive constrained layer (PCL) and the traditional active control method in reducing the structural vibration. The numerical results indicate that: using the active constrained layer damping (ACLD) treatment to dissipate energyt not only the vibration amplitude of structtire is reduced, but also the sound radiated from vibrating structure is suppressed

Time-varying linear control for tiltrotor aircraft

Tiltrotor aircraft have three flight modes： helicopter mode, airplane mode, and transition mode. A tiltrotor has characteristics of highly nonlinear, time-varying flight dynamics and inertial/-control couplings in its transition mode. It can transit from the helicopter mode to the airplane mode by tilting its nacelles, and an effective controller is crucial to accomplish tilting transition missions. Longitudinal dynamic characteristics of the tiltrotor are described by a nonlinear Lagrangeform model, which takes into account inertial/control couplings and aerodynamic interferences.Reference commands for airspeed velocity and attitude in the transition mode are calculated dynamically by visiting a command library which is founded in advance by analyzing the flight envelope of the tiltrotor. A Time-Varying Linear（TVL） model is obtained using a Taylorexpansion based online linearization technique from the nonlinear model. Subsequently, based on an optimal control concept, an online optimization based control method with input constraints considered is proposed. To validate the proposed control method, three typical tilting transition missions are simulated using the nonlinear model of XV-15 tiltrotor aircraft. Simulation results show that the controller can be used to control the tiltrotor throughout its operating envelop which includes a transition flight, and can also deal with vertical gust disturbances.

Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks

Accurate acceleration acquisition is a critical issue in the robotic exoskeleton system,but it is difficult to directly obtain the acceleration via the existing sensing systems.The existing algorithm-based acceleration acquisition methods put more attention on finite-time convergence and disturbance suppression but ignore the error constraint and initial state irrelevant techniques.To this end,a novel radical bias function neural network(RBFNN)based fixed-time reconstruction scheme with error constraints is designed to realize high-performance acceleration estimation.In this scheme,a novel exponential-type barrier Lyapunov function is proposed to handle the error constraints.It also provides a unified and concise Lyapunov stability-proof template for constrained and non-constrained systems.Moreover,a fractional power sliding mode control law is designed to realize fixed-time convergence,where the convergence time is irrelevant to initial states or external disturbance,and depends only on the chosen parameters.To further enhance observer robustness,an RBFNN with the adaptive weight matrix is proposed to approximate and attenuate the completely unknown disturbances.Numerical simulation and human sub ject experimental results validate the unique properties and practical robustness.

VARIATIONAL DISCRETIZATION FOR OPTIMAL CONTROL GOVERNED BY CONVECTION DOMINATED DIFFUSION EQUATIONS

In this paper, we study variational discretization for the constrained optimal control problem governed by convection dominated diffusion equations, where the state equation is approximated by the edge stabilization Galerkin method. A priori error estimates are derived for the state, the adjoint state and the control. Moreover, residual type a posteriori error estimates in the L^2-norm are obtained. Finally, two numerical experiments are presented to illustrate the theoretical results.

Limit protection design in turbofan engine acceleration control based on scheduling command governor

A new limit protection method based on Scheduling Command Governor(SCG) is proposed for imposing multiple constraints on a turbofan engine during acceleration process. A Gain Scheduling Controller(GSC) is designed for the transient state control and its stability proof is developed using Linear Matrix Inequalities(LMIs). The SCG is an add-on control scheme which manages engine limits effectively based on reference trajectory optimization. Unlike the traditional min–max architecture with switching logic, the SCG method utilizes the Linear Parameter Varying(LPV) closed-loop model to form a prediction of future constraint violation and per instant solves a constraint-admissible reference within an approximate Maximal Output Admissible Set(MOAS).The influence of the variation of engine dynamic characteristics and equilibrium points during transient state control is handled by the design of contractive sets. Simulation results on a turbofan engine component-level model show the applicability and effectiveness of the SCG method. Compared to the traditional min–max method, the SCG method has less conservativeness. In addition,the design of contractive sets makes conservativeness tunable.