Robust Adaptive Control

Several concepts and results in robust adaptive control are are discussed and is organized in three parts. The first part surveys existing algorithms. Different formulations of the problem and theoretical solutions that have been suggested are reviewed here. The second part contains new results related to the role of persistent excitation in robust adaptive systems and the use of hybrid control to improve robustness. In the third part promising new areas for future research are suggested which combine different approaches currently known

Robust adaptive control: legacies and horizons

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96316/1/acs2352.pd

Robust Adaptive Control in H(infinity).

This dissertation addresses the problem of unifying identification and control in the paradigm of {\cal H}\sb\infty to achieve robust adaptive control. To achieve robust adaptive control, we employ the same approach used for identification in {\cal H}\sb\infty and robust control in {\cal H}\sb\infty. In the modeling part, we aim not only to identify the nominal plant, but also to quantify the modeling error in {\cal H}\sb\infty norm. The linear algorithm based on least-squares is used, and the upper bounds for the corresponding modeling error are derived. In the control part, we aim to achieve the performance specification in frequency domain using innovative model reference control. New algorithms are derived that minimize an {\cal H}\sb\infty index function associated with the deviation between the performance of the feedback system to be designed, and that of the reference model. The results for the modeling and control part are then combined and applied to adaptive control. It is shown that with mild assumption on persistent excitation, the least squares algorithm in frequency domain is equivalent to the recursive least squares algorithm in time domain. Moreover, finite horizon {\cal H}\sb\infty is employed to design feedback controller recursively using the identified model that is time varying in nature. The robust stability of the adaptive feedback system is then established

Robust Adaptive Control of Switched Systems

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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

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 an Uninhabited Surface Vehicle

In this paper, we develop a novel and robust adaptive autopilot for uninhabited surface vehicles (USV). In practice, usually asudden change in dynamics results in aborted missions and the USV has to be rescued to avoid possible damage to other marine crafts inthe vicinity. This problem has been investigated in our innovative design, which enables the autopilot to cope well with significant changes in the system dynamics and empowers USVs to accomplish their desired missions. The model predictivecontrol technique is employed which adopts an online adaptive nature by utilising three algorithms. Even with random initialisation,significant improvements over the gradient descent and least squares approaches have been achieved by the modified weightedleast squares (WLS) method, which periodically reinitialising the covariance matrix. Extensive simulation studies have been performed to test and verify the advantages of the proposed method