Adaptive control is an active field in the design of control systems to deal with uncertainties. The key difference between adaptive controllers and linear controllers is the adaptive controller's ability to adjust itself to handle unknown model uncertainties. Adaptive control is roughly divided into two categories: direct and indirect. Indirect methods estimate the parameters in the plant and further use the estimated model information to adjust the controller. Direct methods are ones wherein

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... he estimated parameters are those directly used in the adaptive controller. Recently, much effort has been placed in adaptive control in both theory and applications. Theory-wise, new controller design techniques are introduced to handle nonlinear and time-varying uncertainties. Broader systems with larger nonlinear uncertainties can be covered by these developments. As a result, adaptive control finds use in various real world applications. This special session generalizes some of the latest results of adaptive control in both theory and applications. After a thorough review process, 8 papers were selected. The papers in this special section include the following. The paper entitled "Adaptive control for nonlinear systems with time-varying control gain" by A. Rincon and F. Angulo, a scheme for nonlinear plants with time-varying control gains and time-varying plant coefficients is proposed and applied on a plant model consisting of a Brunovsky type model with polynomials as approximators. The methodology has been applied to the speed control of a permanent magnet synchronous motor (PMSM) and proper tracking results have been achieved. The paper entitled "Adaptive impedance control to enhance human skill on a haptic interface system" by S. Suzuki and K. Furuta, adaptive assistive control for a haptic interface system is proposed. An adaptive mechanism derived from a Lyapunov candidate function is used to tune an impedance of the virtual model for the haptic device according to the identified operator's characteristics for enhanced performance. It was verified that the operator's characteristics can be estimated and further enhanced. The paper entitled "Pilot-induced oscillation suppression by using L1 adaptive control" by C. Wang and C. Cao, where pilot-induced oscillation (PIO) is a phenomenon that occurs in both flight tests and operational aircrafts. In this paper, the L1 adaptive controller has been introduced to suppress PIO, which is caused by rate limiting and pure time delay. Due to its architecture, the L1 adaptive controller will achieve a desired response with fast adaptation. The simulation results indicate that the L1 adaptive control is efficient in solving this kind of problem. In the paper entitled "Adaptive control for a class of nonlinear system with redistributed models" by H. Ke, and J. Li, a novel multiple model adaptive controller for a class of nonlinear system in parameter-strict-feedback form is proposed. It not only improves the transient performance significantly, but also guarantees the stability of all the states of the closed-loop system. A simulation example is proposed to illustrate the effectiveness of the developed multiple model adaptive controller. The paper entitled "Adaptive Control Allocation in the Presence of Actuator Failures" by Y. Liu and L. G. Crespo proposes a control allocation framework, where a feedback adaptive signal is designed for a group of redundant actuators and is then adaptively allocated among all group members. In the adaptive control allocation structure, cooperative