Adaptive Output Feedback Apparatuses And Methods Capable Of Controlling A Non-minimum Phase System

The invention comprises apparatuses and methods for providing the capability to stabilize and control a non-minimum phase, nonlinear plant with unmodeled dynamics and/or parametric uncertainty through the use of adaptive output feedback. A disclosed apparatus can comprise a reference model unit for generating a reference model output signal ym. The apparatus can comprise a combining unit that combines and differences a plant output signal y of a non-minimum phase plant for which not all of the states can be sensed, and a plant output signal y, to generate an output error signal ỹ. The apparatus can further comprise an adaptive control unit for generating an adaptive control signal uad used to control the plant.Georgia Tech Research Corporatio

Stability and Performance Metrics for Adaptive Flight Control

This paper addresses the problem of verifying adaptive control techniques for enabling safe flight in the presence of adverse conditions. Since the adaptive systems are non-linear by design, the existing control verification metrics are not applicable to adaptive controllers. Moreover, these systems are in general highly uncertain. Hence, the system's characteristics cannot be evaluated by relying on the available dynamical models. This necessitates the development of control verification metrics based on the system's input-output information. For this point of view, a set of metrics is introduced that compares the uncertain aircraft's input-output behavior under the action of an adaptive controller to that of a closed-loop linear reference model to be followed by the aircraft. This reference model is constructed for each specific maneuver using the exact aerodynamic and mass properties of the aircraft to meet the stability and performance requirements commonly accepted in flight control. The proposed metrics are unified in the sense that they are model independent and not restricted to any specific adaptive control methods. As an example, we present simulation results for a wing damaged generic transport aircraft with several existing adaptive controllers

Adaptive and Reconfigurable Flight Control

An indirect adaptive and reconfigurable flight control system is developed. The three-module controller consists of: (1) a system identification module, (2) a parameter estimate smoother, and (3) a proportional and integral compensator for tracking control. Specifically: (1) The identification of a linear discrete-time control system\u27s open-loop gain is addressed. The classical Kalman filter theory for linear control systems is extended and the control system\u27s state and loop gain are jointly estimated on-line. Explicit formulae for the loop gain\u27s estimate and estimation error covariance are derived. The estimate is unbiased and the predicted covariance is reliable. (2) An adaptive smoother is developed to reduce the fluctuations automatically in the gain estimate, and bursting, caused by instances of poor excitation. (3) Special attention is given to the design of a proportional and integral tracking controller. The outputs of the system identification and gain smoother modules are used to adjust the tracking controller\u27s gain continuously in order to compensate for a possible reduction in the loop gain due to control surface area loss, thus achieving the benefits of adaptive and reconfigurable control. The performance of the adaptive and reconfigurable controller in the face of a simulated control surface failure is examined in carefully designed experiments. The adaptive controller developed in this dissertation and illustrated in a flight control Context is applicable to a wide range of control problems

Dynamic Re-Optimization of a MEMS Controller in Presence of Unmodeled Uncertainties

Online trained neural networks have become popular in recent years in designing robust and adaptive controllers for dynamic systems with uncertainties in their system equations because of their universal function approximation property. This paper discusses a technique that dynamically reoptimizes a Single Network Adaptive Critic (SNAC) based optimal controller in the presence of unmodeled uncertainties. The controller design is carried out in two steps: (i) synthesis of a set of online neural networks that capture the uncertainties in the plant equations on-line (ii) re-optimization of the existing optimal controller to drive the states of the plant to a desired reference by minimizing a predefined cost function. The neural network weight update rule for the online networks has been derived using Lyapunov theory that guarantees stability of the error dynamics as well as boundedness of the weights. This approach has been applied in the online reoptimization of a micro-electromechanical device controller and numerical results from simulation studies are presented here

Experimental comparison of parameter estimation methods in adaptive robot control

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

Improving Transient Performance of Adaptive Control Architectures using Frequency-Limited System Error Dynamics

We develop an adaptive control architecture to achieve stabilization and command following of uncertain dynamical systems with improved transient performance. Our framework consists of a new reference system and an adaptive controller. The proposed reference system captures a desired closed-loop dynamical system behavior modified by a mismatch term representing the high-frequency content between the uncertain dynamical system and this reference system, i.e., the system error. In particular, this mismatch term allows to limit the frequency content of the system error dynamics, which is used to drive the adaptive controller. It is shown that this key feature of our framework yields fast adaptation with- out incurring high-frequency oscillations in the transient performance. We further show the effects of design parameters on the system performance, analyze closeness of the uncertain dynamical system to the unmodified (ideal) reference system, discuss robustness of the proposed approach with respect to time-varying uncertainties and disturbances, and make connections to gradient minimization and classical control theory.Comment: 27 pages, 7 figure

Robust high-performance control for robotic manipulators

Model-based and performance-based control techniques are combined for an electrical robotic control system. Thus, two distinct and separate design philosophies have been merged into a single control system having a control law formulation including two distinct and separate components, each of which yields a respective signal component that is combined into a total command signal for the system. Those two separate system components include a feedforward controller and a feedback controller. The feedforward controller is model-based and contains any known part of the manipulator dynamics that can be used for on-line control to produce a nominal feedforward component of the system's control signal. The feedback controller is performance-based and consists of a simple adaptive PID controller which generates an adaptive control signal to complement the nominal feedforward signal

Direct Adaptive Control of Systems with Actuator Failures: State of the Art and Continuing Challenges

In this paper, the problem of controlling systems with failures and faults is introduced, and an overview of recent work on direct adaptive control for compensation of uncertain actuator failures is presented. Actuator failures may be characterized by some unknown system inputs being stuck at some unknown (fixed or varying) values at unknown time instants, that cannot be influenced by the control signals. The key task of adaptive compensation is to design the control signals in such a manner that the remaining actuators can automatically and seamlessly take over for the failed ones, and achieve desired stability and asymptotic tracking. A certain degree of redundancy is necessary to accomplish failure compensation. The objective of adaptive control design is to effectively use the available actuation redundancy to handle failures without the knowledge of the failure patterns, parameters, and time of occurrence. This is a challenging problem because failures introduce large uncertainties in the dynamic structure of the system, in addition to parametric uncertainties and unknown disturbances. The paper addresses some theoretical issues in adaptive actuator failure compensation: actuator failure modeling, redundant actuation requirements, plant-model matching, error system dynamics, adaptation laws, and stability, tracking, and performance analysis. Adaptive control designs can be shown to effectively handle uncertain actuator failures without explicit failure detection. Some open technical challenges and research problems in this important research area are discussed

Robust adaptive control of conjugated polymer actuators

Conjugated polymers are promising actuation materials for bio- and micromanipulation systems, biomimetic robots, and biomedical devices. Sophisticated electrochemomechanical dynamics in these materials, however, poses significant challenges in ensuring their consistent, robust performance in applications. In this paper, an effective adaptive control strategy is proposed for conjugated polymer actuators. A self-tuning regulator is designed based on a simple actuator model, which is obtained through reduction of an infinite-dimensional physical model and captures the essential actuation dynamics. The control scheme is made robust against unmodeled dynamics and measurement noises with parameter projection, which forces the parameter estimates to stay within physically meaningful regions. The robust adaptive control method is applied to a trilayer polypyrrole (PPy) actuator that demonstrates significant time-varying actuation behavior in air due to the solvent evaporation. Experimental results show that, during 4-h continuous operation, the proposed scheme delivers consistent tracking performance with the normalized tracking error decreasing from 11% to 7%, while the error increases from 7% to 28% and to 50% under a proportional-integral-derivative (PID) controller and a fixed model-following controller, respectively. In the meantime, the control effort under the robust adaptive control scheme is much less than that under PID, which is important for prolonging the lifetime of the actuator

Robust adaptive control of conjugated polymer actuators

Conjugated polymers are promising actuation materials for bio and micromanipulation systems, biomimeticrobots, and biomedical devices. Sophisticated electrochemomechanical dynamics in these materials, however,poses significant challenges in ensuring their consistent, robust performance in applications. In this paper aneffective adaptive control strategy is proposed for conjugated polymer actuators. A self-tuning regulator isdesigned based on a simple actuator model, which is obtained through reduction of an infinite-dimensionalphysical model and captures the essential actuation dynamics. The control scheme is made robust againstunmodeled dynamics and measurement noises with parameter projection, which forces the parameter estimates tostay within physically-meaningful regions. The robust adaptive control method is applied to a trilayer polypyrroleactuator that demonstrates significant time-varying actuation behavior in air due to the solvent evaporation.Experimental results show that, during four-hour continuous operation, the proposed scheme delivers consistenttracking performance with the normalized tracking error decreasing from 11% to 7%, while the error increasesfrom 7% to 28% and to 50% under a PID controller and a fixed model-following controller, respectively. In themean time the control effort under the robust adaptive control scheme is much less than that under PID, whichis important for prolonging the lifetime of the actuator