Direct self-repairing control for a helicopter via quantum multi-model and disturbance observer

In this paper, a new direct self-repairing control scheme is developed for a helicopter flight control system with unknown actuator faults and external disturbance. The design of multi-model-based adaptive control is used to accommodate the faulty system under different fault conditions. By appropriate switching based on quantum information technique, the system can be converted to the best model and the corresponding controller. Asymptotic model following performance and system stability is guaranteed. A disturbance observer is introduced to observe the disturbance of the system, which can produce corresponding control signals according to the disturbance. The results including a numerical simulation and a semi-physical verification demonstrate the effectiveness of the proposed self-repairing control approach for the helicopter flight control system.     

Min-max model predictive control for constrained nonlinear systems via multiple LPV embeddings

A min-max model predictive control strategy is proposed for a class of constrained nonlinear system whose trajectories can be embedded within those of a bank of linear parameter varying (LPV) models. The embedding LPV models can yield much better approximation of the nonlinear system dynamics than a single LTV model. For each LPV model, a parameter-dependent Lyapunov function is introduced to obtain poly-quadratically stable control law and to guarantee the feasibility and stability of the original nonlinear system. This approach can greatly reduce computational burden in traditional nonlinear predictive control strategy. Finally a simulation example illustrating the strategy is presented. Supported by the National Natural Science Foundation of China (Grant Nos. 60774015, 60825302, 60674018), the National High-Tech Research & Development Program of China (Grant No. 2007AA041403), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20060248001), and partly by Shanghai Natural Science Foundation (Grant No. 07JC14016)     

System identification and improved internal model control for yaw of unmanned helicopter

The sea breeze is a low-frequency disturbance that severely damages the stability of small unmanned helicopters operating over the sea, especially for the yaw control, which is highly sensitive to disturbance. General internal model control is an appropriate method for dealing with this kind of operation conditions, whereas conventional internal model control cannot eliminate the tracking errors between a nominal model and a real model. In coping with unknown dynamics and low-frequency gust disturbances for small helicopters, this paper proposes a novel robust controller constructed with system identification and integrator-based improved general internal model. As a refinement of the conventional frame, the proposed control scheme extends the applicable scope of a controlled plant from a priori known dynamic to an unknown dynamic. Furthermore, under the proposed controller, it is guaranteed that the tracking error between the actual model and the nominal model converges to zero asymptotically. Finally, the effectiveness and advantage of the proposed control scheme are verified through comparative practical flight tests.     

On an improved model reduction technique for nonlinear systems

An algorithm is presented for reducing the number of terms in a nonlinear static system which can be modeled by a linear combination of nonlinear functions. The method is an improvement over a previously presented algorithm (Desrochers and Saridis, 1980a). The improvements now make it possible to perform all calculations from a single set of input and output data while in the original algorithm n sets of data were required where n is the number of terms retained. In addition, it is shown how the model error can be calculated at each iteration which relieves the arbitrariness of stopping the algorithm at a preselected value of n as was done originally. Then the insight gained from this improved technique is used to develop an optimal solution to the model reduction problem, a major improvement over the original technique. It is then conjectured that some structural concepts for such systems may exist in a matrix formed from the input and output data.     

An optimized quantum information splitting scheme with multiple controllers

We propose an efficient scheme for splitting multi-qudit information with cooperative control of multiple agents. Each controller is assigned one controlling qudit, and he can monitor the state sharing of all multi-qudit information. Compared with the existing schemes, our scheme requires less resource consumption and approaches higher communication efficiency. In addition, our proposal involves only generalized Bell-state measurement, single-qudit measurement, one-qudit gates and a unitary-reduction operation, which makes it flexible and achievable for physical implementation.     

An improved control algorithm for high-order nonlinear systems with unmodelled dynamics

In this paper, we consider a class of high-order nonlinear systems with unmodelled dynamics from the viewpoint of maintaining the desired control performance （e,g., asymptotical stability） and reducing the control effort. By introducing a new reseating transformation, adopting an effective reduced-order observer, and choosing an ingenious Lyapunov function and appropriate design parameters, this paper designs all improved output-feedback controller. The output-feedback controller guarantees the globally asymptotieal stability of the closed-loop system. Subsequently, taking a concrete system for an example, the smaller critical values for gain parameter and resealing transformation parameter are obtained to effectively reduce the control effort.     

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method.     

An improved nonlinear control design for series DC motors

A series DC motor must be represented by a nonlinear model when nonlinearities such as magnetic saturation are considered. To provide effective control, nonlinearities and uncertainties in the model must be taken into account in the control design. In this paper, the recursive design method is applied to generate nonlinear control, nonlinear PI control, and robust control, and these controls are shown to be efficient and robust in the simulation study compared to existing results.     

Fault tolerant control for a class of nonlinear system with actuator faults

In this work, a fault tolerant control scheme is proposed for a class of nonlinear system with actuator faults. In this fault tolerant control strategy, an estimator is designed to estimate both the system states and the fault signal simultaneously. Based on these estimations, the control law is constructed to achieve the fault tolerant control for the nonlinear system considered. It is shown that the estimation error and the system state can be guaranteed to be bounded. The obtained theoretic results have been verified through the simulation examples on the three‐tank system.     

Tracking control of nonlinear systems with improved performance via transformational approach

In this work, we present a transformation‐based adaptive control design, for uncertain strict‐feedback nonlinear systems, to achieve given performance specifications in terms of convergence rate/time, overshoot, steady‐state (zero‐error) precision, in addition to the primary stability requirement. For the case with no uncertainty and known control coefficient, by introducing a time‐varying scaling function and an error‐dependent transformation, we develop a control strategy that is able to achieve exponential and uniform convergence of the tracking error and at the same time maintain the output tracking overshoot to be as small as desired without the need for trajectory initialization resetting. For the case with nonparametric uncertainties and unknown control directions, by employing an additional time‐varying scaling function together with a self‐tuning Nussbaum function, we develop a control scheme that not only secures asymptotic tracking but also guarantees finite time transient process in that the tracking error, prior to converging to zero, is regulated into a prespecified residual set within a prescribed time. Both theoretical analysis and numerical simulations verify the effectiveness and benefits of the proposed method.     

一类非线性大滞后系统的改进无模型自适应控制

针对工业过程中一类常见的非线性大滞后对象,基于伪偏导数(pseudo-partial-derivative,PPD)的概念动态线性化非线性系统,并利用跟踪.微分器预测系统未来时刻的输出,提出了一种改进的无模型自适应控制(improvedmodel-free adaptive control,IMFAC)算法.通过严格的理论推导,证明了新算法的BIBO稳定性和收敛性.仿真结果验证了该算法的有效性.     

一类非线性直升机模型的滑模降阶控制器设计

针对带有交叉耦合的多输入CE150直升机模型,研究了一类多输入仿射非线性系统的控制设计问题,基于滑模变结构控制理论,采用了一种新的控制器设计方法:滑模降阶方法,即反复运用变结构控制理论,对一类高阶的仿射非线性系统,构造了合适的微分同胚变换函数,把初始高阶系统降至低阶系统,并构造了变结构控制律,再利用当前级和上一级控制输入的映射关系反推出初始系统的控制输入.通过CE150直升机模型仿真结果表明,该方法有效可行.     

An acceleration measurements-based approach for helicopter nonlinear flight control using Incremental Nonlinear Dynamic Inversion

Due to the inherent instabilities and nonlinearities of rotorcraft dynamics, its changing properties during flight and the engineering difficulties to predict its aerodynamics with high levels of fidelity, helicopter flight control requires the application of special strategies. These strategies must allow to cope with the nonlinearities of the system and assure robustness in the presence of inaccuracies and changes in configuration.In this paper, a novel approach based on an Incremental Nonlinear Dynamic Inversion is applied to simplify the design of helicopter flight controllers. With this strategy, by employing the feedback of acceleration measurements to avoid the need for information relative to any aerodynamic change, the control system does not need any model data that depends exclusively on its states, thus enhancing its robustness to model uncertainties.The overall control system is tested by simulating two tasks with distinct agility levels as described in the ADS-33 helicopter handling qualities standard. The analysis shows that the controller provides an efficient tracking of the commanded references. Furthermore, with the robustness properties verified within the range of inaccuracies expected to be found in reality, this novel method seems to be eligible for a potential practical implementation to helicopter vehicles.     

Quasi-finite-time control for high-order nonlinear systems with mismatched disturbances via mapping filtered forwarding technique

In this study, a quasi-finite-time control method for designing stabilising control laws is developed for high-order strict-feedback nonlinear systems with mismatched disturbances. By using mapping filtered forwarding technique, a virtual control is designed to force the off-the-manifold coordinate to converge to zero in quasi-finite time at each step of the design; at the same time, the manifold is rendered insensitive to time-varying, bounded and unknown disturbances. In terms of standard forwarding methodology, the algorithm proposed here not only does not require the Lyapunov function for controller design, but also avoids to calculate the derivative of sign function. As far as the dynamic performance of closed-loop systems is concerned, we essentially obtain the finite-time performances, which is typically reflected in the following aspects: fast and accurate responses, high tracking precision, and robust disturbance rejection. Spring, mass, and damper system and flexible joints robot are tested to demonstrate the proposed controller performance.     

Reliable LQ fuzzy control for continuous-time nonlinear systems with actuator faults

This paper deals with the reliable linear quadratic (LQ) fuzzy control problem for continuous-time nonlinear systems with actuator faults. The Takagi-Sugeno (T-S) fuzzy model is employed to represent a nonlinear system. By using multiple Lyapunov functions, an improved linear matrix inequality (LMI) method for the design of reliable LQ fuzzy controllers is investigated, which reduces the conservatism of using a single Lyapunov function. The different upper bounds on the LQ performance cost function for the normal and different actuator fault cases are provided. A suboptimal reliable LQ fuzzy controller is given by means of an LMI optimization procedure, which can not only guarantee the stability of the closed-loop overall fuzzy system for all cases, but also provide an optimized upper bound on a weighted average LQ performance cost function. Finally, numerical simulations on the chaotic Lorenz system are given to illustrate the application of the proposed design method.     

直升机横侧向通道模糊自适应控制律设计

根据飞行包线中典型状态的小扰动线性化方程,建立了直升机横侧向系统的T-S模型。采用参数映射设计方法,利用系统的部分状态信息,直接针对规定的系统性能指标,在满足要求的可用参数集中设计控制器参数。根据平行分布补偿原理,设计模糊神经网络控制器,利用各典型状态下选定的参数作为样本,训练模糊神经网络,实现所设计的控制律,使飞机能够在全包线范围内达到要求的性能指标。仿真结果表明,应用所设计的控制律,可以在飞行包线内将系统极点较准确地配置在希望极点附近,系统动态性能指标完全满足规定的要求。表明提出的设计方法可行而且有效。     

Observer-based fault tolerant control for a class of nonlinear multi-agent systems with sensor faults

This article focuses on the robust fault tolerant control (FTC) problem for a class of Lipschitz nonlinear multi-agent systems(MASs) subject to sensor faults. Firstly, sensor faults are transformed into actuator faults via introducing a new intermediate auxiliary state variable, and a distributed adaptive fault estimation observer is designed to estimate the state information and the concerned faults by using the relative output estimation error. Then, the sufficient existence conditions for the observer to satisfy the robust performance index are given. Thirdly, based on the results of observer design, a new design method of dynamic output feedback controller is proposed to implement consensus of MASs and ensure the desired disturbance rejection performance. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.     

Nonlinear adaptive model following control for a 3-DOF tandem-rotor model helicopter

This paper considers two-input, two-output nonlinear adaptive model following control of a 3-DOF (degree-of-freedom) tandem rotor model helicopter. The control performance is studied by real time implementation of the control algorithms in an actual helicopter testbed. Since the decoupling matrix of the model helicopter is singular, the system is not decouplable by static state feedback, and it is challenging to design a feedback control system. Dynamic state feedback is applied. The controller is designed using a nonlinear structure algorithm. Furthermore, a parameter identification scheme is introduced in the closed-loop system to improve the control performance. Three identification methods are discussed.     

Adaptive fuzzy backstepping control for a class of switched nonlinear systems with actuator faults

This paper investigates the problem of fault-tolerant control (FTC) for a class of switched nonlinear systems. These systems are under arbitrary switchings and are subject to both lock-in-place and loss-of-effectiveness actuator faults. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. Under the framework of the backstepping control design, FTC, fuzzy adaptive control and common Lyapunov function stability theory, an adaptive fuzzy control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop switched system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error remains an adjustable neighbourhood of the origin. Two simulation examples are provided to illustrate the effectiveness of the proposed approach.     

Localization of multiple faults with group control on a discrete device

We consider the set of control points in a discrete device where testing is organized in groups with a signature analyzer. We solve the localization problem for a fault of any multiplicity located in one or different groups. For this purpose, in addition we construct various partitions of the set of control points where we organize similar group testing. We show an algorithm for constructing additional partitions. We solve the minimization problem for the testing hardware needed to implement it.