Robust tracking control of autonomous underwater vehicles in the presence of disturbance inputs

An autonomous underwater vehicle (AUV) is expected to operate in an ocean in the presence of poorly known disturbance forces and moments. The uncertainties of the environment makes it difficult to apply open-loop control scheme for the motion planning of the vehicle. The objective of this paper is to develop a robust feedback trajectory tracking control scheme for an AUV that can track a prescribed trajectory amidst such disturbances. We solve a general problem of feedback trajectory tracking of an AUV in SE(3). The feedback control scheme is derived using Lyapunov-type analysis. The results obtained from numerical simulations confirm the asymptotic tracking properties of the feedback control law. We apply the feedback control scheme to different mission scenarios, with the disturbances being initial errors in the state of the AUV.

State space formulation of ammonia reactor dynamics

The specific objective of this paper is to develop a state space model of a tubular ammonia reactor which is the heart of an ammonia plant in a fertiliser complex. A ninth order model with three control inputs and two disturbance inputs is generated from the nonlinear distributed model using linearization and lumping approximations. The lumped model is chosen such that the steady state temperature at the exit of the catalyst bed computed from the simplified state space model is close enough to the one computed from the nonlinear steady state model. The model developed in this paper is very useful for the design of continuous/discrete versions of single variable/multivariable control algorithms.

Design of Multiloop Controllers for an Ammonia Reactor

The specific objective of this paper is to develop multiloop controllers that would achieve asymptotic regulation in the presence of parameter variations and disturbance inputs for a tubular reactor used in ammonia synthesis. The dynamic model considered here has nine state variables, two control inputs, and two outputs. A systematic procedure for pairing the two inputs with the corresponding two outputs is presented. The two multiloop proportional controllers so configured are designed via the parameter plane method. This economic configuration of controllers maintains the temperature profile almost at the optimal value whereas the point controllers fail to do so.

Robust multivariable controllers for a tublar ammonia reactor

The specific objective of this paper is to develop multivariable controllers that would achieve asymptotic regulation in the presence of parameter variations and disturbance inputs for a tubular reactor used in ammonia synthesis. A ninth order state space model with three control inputs and two disturbance inputs is generated from the nonlinear distributed model using linearization and lumping approximations. Using this model, an approach for control system design is developed keeping in view the imperfections of the model and the measurability of the state variables. Specifically, the design of feedforward and robust integral controllers using state and output feedback is considered. Also, the design of robust multiloop proportional integral controllers is presented. Finally the performance of these controllers is evaluated through simulation.

H∞ sampled-data synthesis and related numerical issues

In this paper we present a new, compact derivation of state-space formulae for the so-called discretisation-based solution of the H∞ sampled-data control problem. Our approach is based on the established technique of continuous time-lifting, which is used to isometrically map the continuous-time, linear, periodically time-varying, sampled-data problem to a discretetime, linear, time-invariant problem. State-space formulae are derived for the equivalent, discrete-time problem by solving a set of two-point, boundary-value problems. The formulae accommodate a direct feed-through term from the disturbance inputs to the controlled outputs of the original plant and are simple, requiring the computation of only a single matrix exponential. It is also shown that the resultant formulae can be easily re-structured to give a numerically robust algorithm for computing the state-space matrices. © 1997 Elsevier Science Ltd. All rights reserved.

类Lyapunov理论在类人形机器人任务空间内跟踪的应用

考虑了类人形机器人的各种不确定因素,提出了其手臂控制的新方法.基于类Lyapunov方法,设计出具有鲁棒性功能的任务空间控制器.该方法得到的控制器不但在有限确定时间内达到稳定跟踪,而且不需要雅可比矩阵求逆,对一定类型的外部干扰具有鲁棒性功能.最后通过数值仿真显示了所得结果的有效性及其应用方法.

水下机器人的神经网络自适应控制

研究了水下机器人神经网络直接自适应控制方法,采用Lyapunov稳定性理论,证明了存在有界外界干扰和有界神经网络逼近误差条件下,水下机器人控制系统的跟踪误差一致稳定有界.为了进一步验证该水控制方法的正确性和稳定性,利用水下机器人实验平台进行了动力定位实验、单自由度跟踪实验和水平面跟踪实验等验证实验.

Stabilization of Uncertain Nonholonomic Dynamic Systems with Bounded Inputs

The sliding mode approach and the multi-step control strategy are exploited to propose a stabilizing controller for uncertain nonholonomic dynamic systems with bounded inputs. This controller can stabilize the system to an arbitrarily small neighborhood about its equilibrium in a finite time .Its application to a nonholonomic wheeled mobile robot is described. Simulation result shows that the proposed controller is effective

Disturbance decoupled observers for systems with unknown inputs

This note deals with the design of reduced-order disturbance decoupled scalar functional observers for linear systems with unknown inputs. Based on a parametric approach, existence conditions are derived and a design procedure for finding reduced-order scalar functional observers is given. The derived existence conditions are relaxed and the procedure can find first-order disturbance decoupled scalar functional observers for some cases where the number of unknown inputs is more than the number of outputs. Also, the observer matching condition, which is the necessary requirement for the design of state observers for linear systems with unknown inputs, is not required. Numerical examples are given to illustrate the attractiveness of the proposed design method.

A common disturbance decoupled observer for linear systems with unknown inputs

This paper presents a solution to the problem of designing a common linear functional observer that can observe a partial set of the state vector of two linear systems with unknown inputs. A new structure of a decoupled linear functional observer is proposed for systems subject unknown disturbances, using only the available output information. Existence conditions as well as a design procedure are given for constructing the proposed observer. A numerical example is given to illustrate the effectiveness of the new observer structure.

A Syntactic Approach to Revising Epistemic States with Uncertain Inputs

Revising its beliefs when receiving new information is an important ability of any intelligent system. However, in realistic settings the new input is not always certain. A compelling way of dealing with uncertain input in an agent-based setting is to treat it as unreliable input, which may strengthen or weaken the beliefs of the agent. Recent work focused on the postulates associated with this form of belief change and on finding semantical operators that satisfy these postulates. In this paper we propose a new syntactic approach for this form of belief change and show that it agrees with the semantical definition. This makes it feasible to develop complex agent systems capable of efficiently dealing with unreliable input in a semantically meaningful way. Additionally, we show that imposing restrictions on the input and the beliefs that are entailed allows us to devise a tractable approach suitable for resource-bounded agents or agents where reactiveness is of paramount importance.

Eccentricity and hardness control in cold rolling mills with a dynamically constructed neural controller

The main objective of a steel strip rolling process is to produce high quality steel at a desired thickness.  Thickness reduction is the result of the speed difference between the incoming and the outgoing steel strip and the application of the large normal forces via the backup and the work rolls.  Gauge control of a cold rolled steel strip is achieved using the gaugemeter principle that works adequately for the input gauge changes and the strip hardness changes.  However, the compensation of some factors is problematic, for example, eccentricity of the backup rolls.  This cyclic eccentricity effect causes a gauge deviation, but more importantly, a signal is passed to the gap position control so to increase the eccentricity deviation.  Consequently, the required high product tolerances are severely limited by the presence of the roll eccentricity effects.
In this paper a direct model reference adaptive control (MRAC) scheme with dynamically constructed neural controller was used.  The aim here is to find the simplest controller structure capable of achieving an optimal performance.  The stability of the adaptive neural control scheme (i.e. the requirement of persistency of excitation and bounded learning rates) is addressed by using as the inputs to the reference model the plant's state variables.  In such a case, excitation is due to actual plant signals (states) affected by plant disturbances and noise.  In addition, a reference model in the form of a filter with a desired transfer function using Modulus Optimum design was used to ensure variance in the desired dynamic characteristics of the system.  The gradually decreasing learning rate employed by the neural controller in this paper is aimed at eliminating controller instability resulting from over-aggressive control.  The moving target problem (i.e. the difficulty of global neural networks to perfrom several separate computational tasks in closed -loop control) is addressed by the localized architecture of the controller.  The above control scheme and learning algorithm offers a method for automatic discovery of an efficient controller.
The resulting neural controller produces an excellent disturbance rejection in both cases of eccentricity and hardness disturbances, reducing the gauge deviation due to eccentricity disturbance from 33.36% to 4.57% on average, and the gauge deviation due to hardness disturbance from 12.59% to 2.08%.

ε-bounded state estimation for time-delay systems with bounded disturbances

A new problem on ε-bounded functional state estimation for time-delay systems with unknown bounded disturbances is studied in this paper. In the presence of unknown bounded disturbances, the common assumption regarding the observers matching condition is no longer required. In this regard, instead of achieving asymptotic convergence for the observer error, the error is now required to converge exponentially within a ball with a small radius ε > 0. This means that the estimate converges exponentially within an ε-bound of the true value. A general observer that utilises multiple-delayed output and input information is proposed. Sufficient conditions for the existence of the proposed observer are first given. We then employ an extended Lyapunov-Krasovskii functional which combines the delay-decomposition technique with a triple-integral term to study the ε-convergence problem of the observer error system. Moreover, the obtained results are shown to be more effective than the existing results for the cases with no disturbances and/or no time delay. Three numerical examples are given to illustrate the obtained results.

Determining the role of environmental factors and disturbance in the distribution of reed canary grass within wetlands

Reed canary grass (Phalaris arundinacea L.) is an invasive species originally from Europe that has now expanded to a large range within the United States. Reed canary grass possesses a number of traits that allow it to thrive in a wide range of environmental factors, including high rates of sedimentation, bouts of flooding, and high levels of nutrient inputs. Therefore, the goals of our study were to determine if 1) certain types of wetland were more susceptible to Reed canary grass invasion, and 2) disturbances facilitated Reed canary grass invasion. This study was conducted within the Keweenaw Bay Indian Community reservation in the Upper Peninsula of Michigan, in Baraga County. We selected 28 wetlands for analysis. At each wetland, we identified and sampled distinct vegetative communities and their corresponding environmental attributes, which included water table depth, pH, conductivity, calcium and magnesium concentrations, and percent organic matter. Disturbances at each site were catalogued and their severity estimated with the aid of aerial photos. A GIS dataset containing information about the location of Reed canary grass within the study wetlands, the surrounding roads and the level of roadside Reed canary grass invasion was also developed. In all, 287 plant species were identified and classified into 16 communities, which were then further grouped into three broad groupings of wetlands: nonforested graminoid, Sphagnum peatlands, and forested wetlands. The two most common disturbances identified were roads and off-road recreation trails, both occurring at 23 of the 28 sites. Logging activity surrounding the wetlands was the next most common disturbance and was found at 18 of the sites. Occurrence of Reed canary grass was most common in the non-forested graminoid communities. Reed canary grass was very infrequent in forested wetlands, and almost never occurred in the Sphagnum peatlands. Disturbance intensity was the most significant environmental factor in explaining Reed canary grass occurrence within wetlands. Statistically significant relationships were identified at distances of 1000 m, 500 m, and 250 m from studied wetlands, between the level of road development and the severity of Reed canary grass invasion along roadsides. Further analysis revealed a significant relationship between roadside Reed canary grass populations and the level of road development (e.g. paved, graded, and ungraded).