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This paper studies the robust bipartite consensus problems for heterogeneous nonlinear nonaffine discrete-time multi-agent systems (MASs) with fixed and switching topologies against data dropout and unknown disturbances. At first, the controlled system's virtual linear data model is developed by employing the pseudo partial derivative technique, and a distributed combined measurement error function is established utilizing a signed graph theory. Then, an input gain compensation scheme is formulated to mitigate the effects of data dropout in both feedback and forward channels. Moreover, a data-driven learning-based robust bipartite consensus control (LRBCC) scheme based on a radial basis function neural network observer is proposed to estimate the unknown disturbance, using the online input/output data without requiring any information on the mathematical dynamics. The stability analysis of the proposed LRBCC approach is given. Simulation and hardware testing also illustrate the correctness and effectiveness of the designed method.

The feedforward inverse compensation is a typical approach to compensate for the hysteresis nonlinearity in smart materials based actuators, which often requires to develop a hysteresis model first and to compute the parameters of the inverse compensator based on the hysteresis model. Different from the above method, a direct inverse model (DIM) constructed by newly proposed clockwise relay operators is developed in this article. The direct inverse modeling approach is free from constructing the inverse compensator, and can be directly used as the inverse compensator. The parameters of DIM can be obtained via the model identification using the experimental data. In order to facilitate the numerical implementation and reduce the model complexity of DIM, a QR factorization based selection mechanism is employed to select the dominant clockwise relay operators. The reduced DIM is capable of preserving the accuracy of the full DIM. The simulation study and experiments are conducted to validate the effectiveness of the developed compensation approach.

Multiple flexible manipulators can be used to complete some repeatable missions. Each flexible manipulator can be described as an underactuated Lagrangian system based on the assumed modes method. Also, the actuator nonlinearity may deteriorate the system performance. Hence, this article aims to develop a distributed controller to solve the leader–follower consensus of multiple flexible manipulators with uncertain parameters, unknown disturbances, and actuator dead zones. The disturbances are classified as repeatable and nonrepeatable ones. The adaptive, iterative learning, and sliding-mode control techniques are used to handle uncertain parameters, repeatable, and nonrepeatable disturbances, respectively. Based on a dead-zone inverse and a finite-time observer, a distributed controller is developed to drive the flexible manipulators to track a moving leader and keep the flexible vibrations bounded simultaneously. Experimental results are presented to verify the effectiveness of the proposed controller.

A distributed adaptive controller is proposed in this paper for a class of underactuated Lagrangian systems to control the actuated variables to track a dynamic leader and keep unactuated ones bounded under a directed communication graph. A finite-time observer is introduced to estimate the leader's velocity. Based on two sliding variables defined for the actuated and unactuated channels, adaptive controllers are designed for the underactuated Lagrangian systems subject to uncertain parameters and external disturbances without or with actuator faults. The convergences of the proposed controllers are proven based on the separation principle between the observer and the controller. Finally, simulations and experiments are conducted to verify the effectiveness of the proposed controllers.

This paper presents the development of novel fixed-time consensus controllers of simple form for the second-order multiagent systems based on input shaping under an undirected communication graph. To satisfy the requirement of multiagent systems, the input shaping technique is extended to solve the control of an oscillation system with nonzero initial position first. Then, three cases are considered under the connected undirected graph. Theoretical analyses are presented to solve the exact convergence time based on the responses of the closed-loop multiagent systems. Finally, numerical simulations and experiments are conducted to verify the effectiveness of the proposed controllers.

The classical Preisach model, which is built by the superposition of a great number of relay operators, is one of the most popular models to represent the hysteretic behaviors in various applications, such as the smart materials-based actuators. However, the construction of the inverse compensator for the classical Preisach model is very challenging for some reasons, first, the analytical inverse of the classical Preisach model is not available, and, second, due to a huge amount of the relay operators the implementation of the inverse compensator is troublesome and causes heavy computational burden. To overcome these drawbacks, a simplified discrete Preisach model is developed in this paper. The simplified model has an explicit expression with respect to the input of the model, thus it is simple to construct its inverse compensator using the inverse multiplicative structure approach. To reduce the computational effort in implementing the inverse compensator, the model-order reduction method is employed to reduce the complexity of the inverse compensator. Experimental tests are carried out to validate the effectiveness of the proposed approach.

The Preisach model, which is constructed by the superposition of relay operators, is one of the most popular hysteresis models to describe the hysteresis nonlinearities in smart-materials-based actuators. The application of the Preisach model suffers from the tradeoff between the model accuracy and the number of the relay operators. With a large number of relay operators, the Preisach model can predict the hysteretic effect very precisely; however, a large number of relay operators may lead to a heavy computation burden. To deal with this tradeoff, in this paper, a model order reduction method, namely discrete empirical interpolation method, is applied to reduce the number of the relay operators and meanwhile to preserve the model accuracy of the original Preisach model. Simulations under different conditions (different input signals and different density functions) and experimental tests on a magnetostrictive-actuated platform are conducted to validate the effectiveness of the proposed reduced Preisach model.