Control system design and evaluation for robust autonomous rotorcraft guidance

This paper describes the design and performance analysis of a control system for rotorcraft with an emphasis on the requirements called for by autonomous guidance. To be able to track trajectories, such as those generated by a guidance system, a path following controller is used in combination with a velocity control augmentation. The path following system is motivated by nonlinear stability analysis. The velocity control augmentation follows a loop-shaping design for the inner-loop attitude control and a dynamic inversion velocity control design for the outer-loop. The identified model of the dynamics along with the uncertainties is used to determine the robustness and performance of the velocity control system. Finally, the paper presents a novel method to evaluate the overall performance of the control system in terms of the tracking error statistics. These results are then used to determine a tracking error model, which can be used to predict the tracking error for a given reference.     

Trim of rotorcraft multibody models using a neural-augmented model-predictive auto-pilot

The aeromechanical analysis of rotorcraft using comprehensive multibody vehicle models is crucially dependent on the ability to accurately compute the model trim settings. Among the various techniques proposed in the literature, the auto-pilot approach, being independent of the complexity of the model, has the potential to solve trim problems efficiently even for the highly detailed aero-servo-elastic vehicle models that are developed using modern finite-element-based multibody analysis codes. Published proportional auto-pilots show to work well in many practical instances. However, their robustness with respect to the flight condition is often poor, so that they must be accurately tuned. In this paper, an auto-pilot based on adaptive non-linear model-predictive control is proposed. The formulation uses a non-linear reference model of the rotorcraft, which is augmented with an adaptive neural element. The adaptive element identifies and corrects the mismatch between reduced and comprehensive models, thereby improving the predictive capabilities of the controller. The methodology is tested on the wind-tunnel trim of a rotor multibody model and compared to an existing implementation of a classical auto-pilot for comprehensive rotorcraft analysis applications. The proposed controller shows improved stability over the conventional approach without the need for calibration. Commemorative Contribution.     

Robust feedforward design for a two-degrees of freedom controller

In this paper we address the problem of designing the feedforward block of a two degrees of freedom controller in the case of single input single output (SISO) discrete-time linear systems with model uncertainty. In this work the design of the feedforward filter is formulated as a robust model matching problem. First, a closed form solution of the optimal noncausal filter, which minimizes the worst-case model matching error for each ω[0,2π], is obtained. Then, suitable rational stable approximations of the optimal solution are derived either by means of causal filters or, when a preview of the reference signal is available, by means of noncausal FIR filters. The efficiency of the proposed method is tested on a simulated example.     

Robust reliable control design for networked control system with sampling communication

In this article, the problem of robust exponential stability and reliable stabilisation for a class of continuous-time networked control systems (NCSs) with a sample-data controller and unknown time-varying sampling rate is considered. The analysis is based on average dwell-time, Lyapunov–Krasovskii functional and linear matrix inequality (LMI) technique. The delay-dependent criteria are developed for ensuring the robust exponential stability of the considered NCSs. The obtained conditions are formulated in terms of LMIs that can easily be solved by using standard software packages. Furthermore, the result is extended to study the robust stabilisation for NCS with parameter uncertainties. A state feedback controller is constructed in terms of the solution to a set of LMIs, which guarantee the robust exponential stabilisation of NCS and the controller. Finally, numerical examples are presented to illustrate the effectiveness of the obtained results.     

Robust pole clustering in a good ride quality region of aircraft for matrices with structured uncertainties

This paper presents a general analysis of robust pole clustering in a good ride quality region (GRQR) of aircraft for matrices with structured uncertainties. This region is an intersection of a ring and a horizontal strip, located in the left half-plane, which is a specific non-Ω-transformable region providing good ride quality of aircraft. The paper applies the Rayleigh principle along the norm theory to analyze robust pole clustering within this region since the generalized Lyapunov theory is not valid for non-Ω-transformable regions. Concerned uncertainties are structured/parametric uncertainties, including interval matrices. The results are useful for robust control analysis and design, especially, of robust good ride quality of aircraft, shuttles, vehicles and space station, as well as some industrial systems. An example of the F-16 dynamics for which GRQR is suitable is included to illustrate the results.     

Multi-input,multi-output flight control system design for the YF-16 using nonlinear QFT and pilot compensation

Nonlinear quantitative feedback theory (QFT) and pilot compensation techniques are used to design a 2 × 2 flight control system for the YF-16 aircraft over a large range of plant uncertainty. The design is based on numerical input-output time histories generated with a FORTRAN implemented nonlinear simulation of the YF-16. The first step of the design process is the generation of a set of equivalent linear time-invariant (LTI) plant models to represent the actual nonlinear plant. It has been proven that the solution to the equivalent plant problem is guaranteed to solve the original nonlinear problem. Standard QFT techniques are then used in the design synthesis based on the equivalent plant models. A detailed mathematical development of the method used to develop these equivalent LTI plant models is provided. After this inner-loop design, pilot compensation is developed to reduce the pilot's workload. This outer-loop design is also based on a set of equivalent LTI plant models. This is accomplished by modelling the pilot with parameters that result in good handling qualities ratings, and developing the necessary compensation to force the desired system responses.     

Robust output-feedback controller design via local BMI optimization

The problem of designing a globally optimal full-order output-feedback controller for polytopic uncertain systems is known to be a non-convex NP-hard optimization problem, that can be represented as a bilinear matrix inequality optimization problem for most design objectives. In this paper a new approach is proposed to the design of locally optimal controllers. It is iterative by nature, and starting from any initial feasible controller it performs local optimization over a suitably defined non-convex function at each iteration. The approach features the properties of computational efficiency, guaranteed convergence to a local optimum, and applicability to a very wide range of problems. Furthermore, a fast (but conservative) LMI-based procedure for computing an initially feasible controller is also presented. The complete approach is demonstrated on a model of one joint of a real-life space robotic manipulator.     

基于混合灵敏度设计的鲁棒迭代学习控制

提出一种鲁棒迭代学习控制的设计方法．利用混合灵敏度设计方法,控制器满足一定鲁棒性条件时就可以直接获得收敛更新规则．此外,只要学习滤波函数满足一定条件,系统跟踪误差将显著降低．仿真结果表明该方法有效性较高．     

Robust controller design for temperature tracking problems in jacketed batch reactors

There is an increasing trend to employ advanced instrumentation and control strategies for batch processes where expensive products are being manufactured. In this paper, a robust nonlinear control strategy is developed for temperature tracking problems in batch reactors in the presence of parametric uncertainty. The controller has a multi-loop feedback configuration. An inner loop is designed for approximate input–output linearization of a nominal plant. The outer loop is designed for stability and robust performance by utilizing results from structured singular values (μ-synthesis). It is shown via simulation of a temperature tracking problem in batch synthesis that the controller provides excellent tracking despite parametric uncertainty.     

Passification-based robust flight control design

A passification-based robust autopilot for attitude control of flexible aircraft under parametric uncertainty is designed. A high gain controller with forced sliding motions is used to secure good performance over a wide range of the aircraft model parameters. The shunting method is applied to ensure closed-loop system stability under lack of aircraft state information. The series reference model is used to assign the desired closed-loop system performance. An example illustrating a typical design procedure for aircraft attitude control in the horizontal plane for different flight conditions is given. The simulation results demonstrate the efficiency and high robustness of the suggested control system.     

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed. First, robust motion and force tracking is considered, where a performance criterion containing disturbance and estimated parameter attenuations is presented. Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties. After solving a simple linear matrix inequality for gain conditions, the effect from disturbance and estimated parameter errors to motion/force errors is attenuated to an arbitrary prescribed level. Moreover, if the disturbance and estimated parameter errors are square-integrable, then an asymptotic motion tracking is achieved while the force error is as small as the inversion of control gain. Finally, numerical simulation results for a constrained planar robot illustrate the expected performance.     

Robust parametric CMAC with self-generating design for uncertain nonlinear systems

In this paper, a robust parametric cerebellar model articulation controller (RP-CMAC) with self-generating design, called RPCSGD, is proposed for uncertain nonlinear systems. The proposed controller consists of two parts: one is the parametric CMAC with self-generating design (PCSGD), which is utilized to approximate the ideal controller and the other is the robust controller, which is designed to achieve a specified H^∞ robust tracking performance of the system. The corresponding memory size of the proposed controller can be suitably constructed via the self-generating design. Thus, the useless or untrained memories will not take possession of the space. Besides, the concept of sliding-mode control (SMC) is adopted so that the proposed controller has more robustness against the approximated error and uncertainties. The stability of the system can be guaranteed surely due to the derivations of the adaptive laws of the proposed RPCSGD based on the Lyapunov function. Finally, the proposed controller is applied to the second-order chaotic system and the one-link rigid robotic manipulator. The tracking performance and effectiveness of the proposed controller are verified by simulations of the computer.     

时变采样周期网络控制系统的鲁棒容错控制器设计

研究具有时变采样周期网络控制系统的执行器失效的完整性问题．假设系统任意两个连续采样间隔具有上界,利用输入时延法,将时变采样周期网络控制系统等价转化为连续时变时延网络控制系统．在此基础上,基于时延条件,应用Ｌｙａｐｕｎｏｖ稳定性理论和线性矩阵不等式（ＬＭＩｓ）方法证明了鲁棒容错控制律的存在条件,设计了鲁棒容错控制器,并给出了系统完整性条件下的最大允许时延的估计方法．仿真结果验证了所提方法的可行性和有效性．     

Robust frequency design of linear stationary systems in aeroautoelastics

Robust frequency design of linear closed-loop stationary aeroelastic systems (LCLSAES) is considered with the help of doubly coprime fractional representations, linear affine manifolds, and the matrix Corona problem as well as with or without the help of the separation principle. The numerical algorithm is produced by using the finite Lagrange-Sylvester interpolation formulas. An example is given.     

Passive range estimation for rotorcraft low-altitude flight

The automation of rotorcraft low-altitude flight presents challenging problems in control, computer vision, and image understanding. A critical element in this problem is the ability to detect and locate obstacles, using on-board sensors, and to modify the nominal trajectory. This requirement is also necessary for the safe landing of an autonomous lander on Mars. This paper examines some of the issues in the location of objects, using a sequence of images from a passive sensor, and describes a Kalman filter approach to estimate range to obstacles. The Kalman filter is also used to track features in the images leading to a significant reduction of search effort in the feature-extraction step of the algorithm. The method can compute range for both straightline and curvilinear motion of the sensor. An experiment is designed in the laboratory to acquire a sequence of images along with the sensor motion parameters under conditions similar to helicopter flight. The paper presents range estimation results using this imagery.     

Robust control design for best mean performance over an uncertain-parameters box

This paper deals with robust, polytopic, probabilistic H_∞ analysis and state-feedback synthesis of linear systems and focuses on the performance distribution over the uncertainty region (rather than on the performance bound). The proposed approach allows different disturbance attenuation levels (DALs) at the vertices of the uncertainty polytope. It is shown that the mean disturbance attenuation level (DAL) over an uncertain parameters-box is the average of the DALs at the vertices, if each parameter has an independent, symmetrical, centered probability density function. In such a (most common) case, the mean DAL over the uncertain parameters-box can be optimized by minimizing the sum of the DALs at the vertices. The standard deviation of the DAL over the uncertain parameters-box is also addressed, and a method to minimize this standard deviation is shown. A new robust H_∞ state-feedback synthesis theorem is given; it is based on a recent, most efficient analysis method and applies the proposed multiple-vertex-DALs approach. A state-feedback design example utilizing the latter theorem shows that a control design which minimizes the sum of the vertex-DALs leads to a better actual closed-loop performance than a similar design which minimizes only the bound of the DAL over the uncertainty polytope. The comparison is based on the statistics of a population of closed-loop ‘point-wise’ H_∞-norms created by a Monte-Carlo mechanism.     

Robust fault detection filter design for networked control systems with delay distribution characterisation

In this article, robust fault detection (RFD) is investigated for networked control systems with delay distribution characterisation. By utilising an observer-based fault detection filter as a residual generator, the RFD of networked control systems with non-uniformly distributed network‐induced time-varying delay is formulated as an H _∞ model-matching problem. Delay-interval-dependent and delay-interval-occurrence-rate-dependent sufficient conditions are obtained by employing the Lyapunov–Krasovskii functional approach. Especially, the robust fault detection filters guarantee strong robustness from residual signal to disturbance as well as high sensitivity to faults. A numerical example is given to illustrate the effectiveness of the proposed design techniques.     

Robust control design for long time-delay systems

In order to reduce the control design problem for input/output long-delay systems, a stable predictor is presented in this paper. The predictor is stable for stable/unstable or minimum/non-minimum phase systems. Based on the predicted undelayed output, any available control design procedure can be applied to fulfil the control requirements, the delay remaining unchanged. Disturbance rejection and robustness may be enhanced based on the estimation error. The approach is shown to work in all considered cases and comparison with other methods, when available in the literature, is reported.     

Robust optimal experiment design for system identification

This paper develops the idea of min-max robust experiment design for dynamic system identification. The idea of min-max experiment design has been explored in the statistics literature. However, the technique is virtually unknown by the engineering community and, accordingly, there has been little prior work on examining its properties when applied to dynamic system identification. This paper initiates an exploration of these ideas. The paper considers linear systems with energy (or power) bounded inputs. We assume that the parameters lie in a given compact set and optimise the worst case over this set. We also provide a detailed analysis of the solution for an illustrative one parameter example and propose a convex optimisation algorithm that can be applied more generally to a discretised approximation to the design problem. We also examine the role played by different design criteria and present a simulation example illustrating the merits of the proposed approach.