The author describes a generalization of the theory of hysteresis to cover asymmetric or minor hysteresis loop excursions The original description of the model was found to be particularly useful because it enabled hysteresis loops of magnetic materials to be successfully modeled with a limited set of only five physically based parameters The extension of the model presented is completely self-consistent with the original formulation and requires no additional parameters to describe the minor loops Results of the model calculations are shown with the objective of demonstrating the morphology of the solutions These display the expected behavior and also include the phenomena of minor loop crossing the partial coincidence of minor loops under certain conditions >

A self consistent generalized model for the calculation of minor loop excursions in the theory of hysteresis

This paper proposes a decentralized neural adaptive dynamic surface approximated inverse control (DNADSAIC) scheme for a class of large-scale time-delay systems with hysteresis nonlinearities as input. The decentralized control problem under the case only the outputs are measurable is solved by utilizing the radial basis function neural networks approximator and the hysteresis approximated inverse compensator. Also, with the help of finite covering lemma, the traditional Krasovskii functionals are dropped when coping with the delays, leading to the removal of the assumptions on the functions with time-delay states and the acquisition of the arbitrarily small ${L}_{{\infty }}$ tracking performance of each hysteretic subsystem with time delays. The analysis of stabilities guarantees all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. Simulation results illustrate the efficiency of the proposed DNADSAIC scheme.

Decentralized Adaptive Neural Approximated Inverse Control for a Class of Large-Scale Nonlinear Hysteretic Systems With Time Delays

This paper studies bipartite tracking problem of nonlinear multiagent systems over signed directed graphs Each following agent is modeled by a higher-order nonlinear system in strict-feedback form with unknown dynamics and hysteresis input uncertainty Both distributed state feedback and output feedback control laws are proposed to achieve bipartite tracking confined by the prescribed performance bounds The proposed approximation-free distributed controllers only utilize error variables incorporating with performance bound functions, which lead to a low-complexity control algorithm Moreover, the proposed control laws guarantee that all signals of the closed-loop system are uniformly ultimately bounded

Prescribed Performance for Bipartite Tracking Control of Nonlinear Multiagent Systems With Hysteresis Input Uncertainties

A novel actuating material, which is lightweight, soft, and capable of generating large flapping motion under electrical stimuli, is highly desirable to build energy-efficient and maneuverable bio-inspired underwater robots. Ionic polymer–metal composites are important category of electroactive polymers, since they can generate large bending motions under low actuation voltages. IPMCs are ideal artificial muscles for small-scale and bio-inspired robots. This paper takes a system perspective to review the recent work on IPMC-enabled underwater robots, from modeling, fabrication, and bio-inspired design perspectives. First, a physics-based and control-oriented model of IPMC actuator will be reviewed. Second, a bio-inspired robotic fish propelled by IPMC caudal fin will be presented and a steady-state speed model of the fish will be demonstrated. Third, a novel fabrication process for 3D actuating membrane will be introduced and a bio-inspired robotic manta ray propelled by two IPMC pectoral fins will be demonstrated. Fourth, a 2D maneuverable robotic fish propelled by multiple IPMC fin will be presented. Last, advantages and challenges of using IPMC artificial muscles in bio-inspired robots will be concluded.

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A review on robotic fish enabled by ionic polymer-metal composite artificial muscles.

In this paper, an observer-based fuzzy adaptive controller for a class of uncertain nonstrict nonlinear systems with unknown control direction and unknown dead-zone is presented. First, by using equivalence dead-zone inverse and a linear state transformation, the original system is converted to a new one. Then, by using fuzzy logic systems, the unknown nonlinearities are approximated based on an adaptive mechanism, and a nonlinear fuzzy state observer is designed to estimate immeasurable states. The dynamic surface control technique is employed to solve the problem of explosion of complexity in the traditional backstepping approach, and then, this method is combined with Nussbaum gain function to address the problem of unknown control direction. Besides, barrier Lyapunov function is employed to overcome the violation of system output. The proposed controller guarantees that the closed-loop system is stable; all the system states are bounded, and tracking errors converge to a neighborhood of the origin. A numerical simulation is provided to confirm the usefulness of the proposed control design.

Observer-Based Fuzzy Adaptive Dynamic Surface Control of Uncertain Nonstrict Feedback Systems With Unknown Control Direction and Unknown Dead-Zone

Magnetostrictive actuators featuring high energy densities, large strokes, and fast responses are playing an increasingly important role in micro/nano-positioning applications. However, such actuators with different input frequencies and mechanical loads exhibit complex dynamics and hysteretic behaviors, posing a great challenge on applications of the actuators. Therefore, it is important to develop a dynamic model that can characterize dynamic behaviors of the actuators, including current-magnetic flux nonlinear hysteresis, frequency responses, and loading effects, simultaneously. To this end, a comprehensive model, which thoroughly considers the electric, magnetic, and mechanical domain, as well as the interactions among them, is developed in this paper. To validate the developed model, the parameters of the model are identified where the hysteresis of the magnetostrictive actuator is described, as an illustration, by the asymmetric shifted Prandtl–Ishlinskii model. The experimental results demonstrate that the comprehensive model presents an excellent agreement with dynamic behaviors of the magnetostrictive actuator.

A Comprehensive Dynamic Model for Magnetostrictive Actuators Considering Different Input Frequencies With Mechanical Loads

This paper discusses the modeling and control of the ionic polymer-metal composite (IPMC) actuators which have many promising applications in biomechatronics. A novel mathematical model in continuous-time domain of the IPMC actuator, being a stable second-order dynamical system preceded by a nonlinear hysteresis representation, is proposed. An adaptive controller is formulated for the IPMC actuator based on the proposed model. The proposed adaptive control law ensures the global stability of the controlled IPMC system, and the position error of the IPMC actuator can be theoretically guaranteed to converge to zero. The effectiveness of the proposed model and the superiority of the proposed control to the traditional proportional-integral-derivative control are verified by experimental results.

Adaptive Control for Ionic Polymer-Metal Composite Actuators

In this paper, a fuzzy approximator based adaptive dynamic surface control scheme is proposed for a class of unknown time delay nonlinear systems preceded by asymmetric hysteresis nonlinearities. The features of the proposed method are: 1) by combining the approximated property of the fuzzy logic systems (FLSs) with the finite covering lemma, the Krasovskii functionals are disposed of, achieving the ${L}_{{\infty }}$ norm of the tracking error by using the initializing technique; 2) the assumptions on the time-delay functions are removed due to the use of the finite covering lemma and the FLSs; and 3) the proposed adaptive fuzzy dynamic surface control scheme can also compensate the asymmetric shifted Prandtl-Ishlinskii (ASPI) hysteresis without constructing the inverse of the ASPI model with the density function of ASPI model being unknown and estimated online to compensate the hysteresis. It is proved that all the signals in the closed-loop system are ultimately uniformly bounded and can be made arbitrarily small. Simulation results show the validity of the proposed method.

Chun-Yi Su

Papers

A Comprehensive Dynamic Model for Magnetostrictive Actuators Considering Different Input Frequencies With Mechanical Loads

Adaptive Control for Ionic Polymer-Metal Composite Actuators

Fuzzy Approximator Based Adaptive Dynamic Surface Control for Unknown Time Delay Nonlinear Systems With Input Asymmetric Hysteresis Nonlinearities