Adaptive Feedback Control Law Research Articles

Fault Tolerant Control of Internal Combustion Engine subject to Intake Manifold Leakage

In this paper, a new FTC strategy of the diesel engine air path subject to intake manifold leakage described by Takagi-Sugeno model is presented. The proposed FTC design scheme integrates the state estimation, the leakage identification and the state feedback control law, to guaranty the stabilization of the faulty plant. State vector and leakage are estimated by an adaptive observer. The gains of the adaptive observer and feedback control law are obtained by solving a linear matrix inequality derived from the Lyapunov theory. The effectiveness and performances of the proposed approach are illustrated using the professional advanced diesel engine simulator AMEsim(LMS) platform in co-simulation with SIMULINK software.

Adaptive control of a class of nonlinear systems with its application to a synchronization problem

In this paper, we first consider an adaptive stabilization problem for a class of uncertain nonlinear systems with both dynamic and static uncertainties. A sufficient condition on the convergence of the estimated parameter to the true parameter is also given. Then we show that the synchronization problem for the Chua's circuit and an uncertain harmonic system can be converted into the adaptive stabilization problem under consideration. Finally, an adaptive feedback control law is designed such that, the output of the Chua's circuit can asymptotically synchronize, in the presence of large parameter variations, with the output of the uncertain harmonic system. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

A Nonlinear Control Design for Tracked Robots with Longitudinal Slip

AbstractThis paper presents an adaptive control strategy for a tracked mobile robot, in which the longitudinal slip of the left and right tracks are described by two unknown parameters. It is assumed that the kinematic model of the tracked robot is approximated by the one of a differential wheeled robot. An adaptive nonlinear feedback control law that compensates for the longitudinal slip is proposed to achieve a given trajectory tracking objective. Asymptotic stability of the close-loop system is ensured using an appropriate Lyapunov function. Numerical results show the benefits of the proposed approach.

Non-Certainty-Equivalent Adaptive Control of a Nonlinear Aeroelastic System

Non-Certainty-Equivalent Adaptive Control of a Nonlinear Aeroelastic SystemThe development of a non-certainty-equivalent adaptive control system for the control of a nonlinear aeroelastic system is the subject of this paper. The prototypical aeroelastic wing section considered here includes structural nonlinearity and a single control surface for the purpose of control. Its dynamical model has two-degree-of-freedom and describes the plunge and pitch motion. It is assumed that the model parameters (except the sign of one of the control input coefficients) are not known. The uncontrolled aeroelastic model exhibits limit cycle oscillation beyond a critical free-stream velocity. Based on the attractive manifold, and the immersion and invariance methodologies, a non-certainty-equivalent adaptive state variable feedback control law for the trajectory tracking of the pitch angle is derived. Using the Lyapunov analysis, asymptotic convergence of the state variables to the origin is established. It is shown that the trajectory of the system converges to a manifold. The special feature of the designed control system is that the closed-loop system asymptotically recovers the performance of a deterministic controller. This cannot happen if certainty-equivalent adaptive controllers are used. Simulation results are presented which show that the control system suppresses the oscillatory responses of the system in the presence of large parameter uncertainties.

비정합 불확실성을 갖는 시스템을 위한 적응 슬라이딩 모드 제어기 설계

초록·키워드 목차 오류제보하기 본 논문에서는 불확실한 다변수 시스템을 위한 적응 슬라이딩 모드 제어기의 LMI 기반 설계방법이 제시된다. LMI를 사용하여 슬라이딩 평면의 존재조건을 구한다. 그리고 외란의 놈 경계치를 추정하고 안정성을 보장할 적응 스위칭 궤환 제어기 알고리즘을 제시한다. 마지막으로 제안된 방법의 유효성을 보이기 위해 크레인 모델을 제어하기 위한 설계 예를 제시한다. #Linear Matrix Inequality(Lmi) #Adaptive Control #Sliding Mode #Polytopic Model 요약Abstract1. 서론2. 문제 설정3. 주요 결과4. 수치적인 예5. 결론References저자소개

Adaptive Control for a Mobile Robot Under Slip Conditions Using an LMI-Based Approach

This paper presents a control law for the trajectory tracking of mobile robots under slip conditions and subject to both system constraints and varying dynamics. First, a control law is obtained based on a Lyapunov function to guarantee closed-loop asymptotic stability, resulting in a set of feedback gains, one for each extreme model realization. On-line computation is devoted to determine an adaptive feedback control law for the current realization of the state as a convex combination of the gains previously obtained. Simulations comparing the proposed control law with other strategies under slip conditions are provided. They show the satisfactory behavior of the proposed control strategy.

Robust chaos synchronization of four-dimensional energy resource system via adaptive feedback control

This paper investigates the robust chaos synchronization problem for the four-dimensional energy resource systems with mismatched parameters. Based on the Lyapunov stability theory, the sufficient conditions for the synchronization are obtained analytically and an adaptive feedback control law is derived to make the states of two slightly mismatched chaotic systems asymptotically synchronized. Finally, some numerical simulations are performed to verify the proposed results.

Pneumatic Control of Robots for Rehabilitation

Pneumatic actuators are attractive for robotic rehabilitation applications because they are lightweight, powerful, and compliant, but their control has historically been difficult, limiting their use. In this paper we present the pneumatic control system developed for Pneu-WREX: a pneumatically actuated, upper extremity orthosis for rehabilitation after stroke. The developed pneumatic control system combines several novel components to make the entire system stable, reliable, and backdrivable. These components, which are described in this paper, include: (1) a unique two-valve force control subsystem that keeps chamber pressure low (to reduce friction and energy consumption) and adaptively compensates for leakage; (2) a new servovalve characterization approach that uses experimentally measured data in a combined non-linear and least-squares regression to obtain a linear relationship between mass flow and valve voltage; and (3) a new approach to state estimation using accelerometers and a Kalman filter to obtain clean signals for use in a non-linear adaptive feedback control law. Experimental testing of the device demonstrates the efficacy of the developed pneumatic control system.

Coordinated Path Following of Multiple UAVs for Time-Critical Missions in the Presence of Time-Varying Communication Topologies

We address the problem of steering multiple unmanned air vehicles (UAVs) along given paths (path-following) under strict temporal coordination constraints requiring, for example, that the vehicles arrive at their final destinations at exactly the same time. Path-following relies on a nonlinear Lyapunov based control strategy derived at the kinematic level with the augmentation of existing autopilots with ℒ1 adaptive output feedback control laws to obtain inner-outer loop control structures with guaranteed performance. Multiple vehicle time-critical coordination is achieved by enforcing temporal constraints on the speed profiles of the vehicles along their paths in response to information exchanged over a dynamic communication network. We consider that each vehicle transmits its coordination state to only a subset of the other vehicles, as determined by the communications topology adopted. We address explicitly the case where the communication graph that captures the underlying communication network topology may be disconnected during some interval of time (or may even fail to be connected at any instant of time) and provide conditions under which the closed-loop system is stable. Flight test results obtained at Camp Roberts, CA in 2008 and hardware-in-the-loop (HITL) simulations demonstrate the benefits of the algorithms developed.

DISTURBANCE ESTIMATION AND CANCELLATION FOR LINEAR UNCERTAIN SYSTEMS

This paper considers a class of linear uncertain systems in which the uncertainty is an additive perturbation of a known (nominal) linear model. It is supposed that the uncertainty and/or disturbance is known to be bounded, but its bound is unkown. A novel, easy to implement, adaptive feedback control law is designed to estimate the bounded disturbance on-line. This information is then used to cancel the effect of the disturbance in the system. The main advantage is that, if further design objectives are to be realized (for example, with respect to a tracking problem), the controls can be designed on the information from the nominal model only and not on the uncertain model.

Adaptive control of a nonminimum phase linear system

For a weakly nonminimum phase linear system, an adaptive feedback control law is designed that ensures the system output to track a desired trajectory to an arbitrarily high degree of precision. The key to this is the use of a low gain feedback design technique.

On the Adaptive Control of Jump Parameter Systems via Nonlinear Filtering

In this paper we first present an error analysis for the process of estimates generated by the Wonham filter when it is used for the estimation of the (finite set-valued) jump-Markov parameters of a random parameter linear stochastic system and further give bounds on certain functions of these estimates. We then consider a certainty equivalence adaptive linear-quadratic Gaussian feedback control law using the estimates generated by the nonlinear filter and demonstrate the global existence of solutions to the resulting closed-loop system. A stochastic Lyapunov analysis establishes that the certainty equivalence law stabilizes the Markov jump parameter linear system in the mean square average sense. The conditions for this result are that certain products of (i) the parameter process jump rate and (ii) the solution of the control Riccati equation and its second derivatives should be less than certain given bounds. An example is given where the controlled linear system has state dimension 2. Finally, the stabilizing properties of certainty equivalence laws which depend on (i) the maximum likelihood estimate of the parameter value and (ii) a modified version of this estimate are established under certain conditions.

The adaptive control of an airlift tower loop fermenter: an application of model-based control employing a sophisticated process model

Abstract The contribution focuses on the application of model-based control techniques to a biotechnological process described by a complicated reactor model. Treated is the control of the aeration rate during the batch production of single cellular protein in an airlift tower loop reactor. To obtain an accurate description of this process, a spatially distributed model consisting of coupled algebraic and ordinary differential equations is employed. The first technique discussed is an adaptive strategy known as the open-loop-feedback-optimal (OLFO) control. To further the practical applicability of this strategy, methods are presented for simulation and optimization of the process in a computationally very efficient manner. At the same time, the spatially distributed nature of the process model is retained in order to ensure sufficient accuracy in performing the tasks of the control algorithm. In a second technique, a time-variant, adaptive feedback control law is suggested as an alternative means to optimally control the treated process. The feedback law is base on a criterion formulated from the necessary conditions of optimality according to variational principles and the results of a sensitivity analysis with respect to the model parameters.

Optimal policies for passive learning controllers

This paper deals with the optimal control of unknown parameter, partially observed linear discrete time systems. The objective of the paper is to find a suitable cost functional for which passive learning controllers are optimal. It is shown that if there are parameter uncertainties in the observation equation, then a quadratic functional can be found such that a passively adaptive closed loop control law is optimal with respect to this functional. If there are also parameter uncertainties in the system equation, then a quadratic functional can be found such that a passively adaptive open loop feedback control law is optimal with respect to this functional.

Synthesis of adaptive sub-optimal feedback control laws†

Abstract In this paper a method for synthesizing a sub-optimal feedback controller which is sensitive to variations in plant parameters is presented. The structure of the sub-optimal control is a linear combination of suitably chosen basis functions multiplied by coefficients which are functions of the varying plant parameters. The coefficient multipliers are determined by the minimization of a mean-square error using data obtained from numerically computed optimal trajectories. The controller may be called adaptive since it uses identification of the varying plant parameters to modify the coefficient multipliers. Examples are included which show that with relative sensitivity as the criterion the method is superior to other methods of suboptimal control design.