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This paper proposes some methods for estimating the order of nonlinear systems having non-integer order. First, the stability of time-varying order systems is studied. Afterward, the multivariable systems with time-varying incommensurate order are studied and an estimation scheme is proposed to approximate the order. In the next step, considering the pseudo-states of a system to be unknown, an order/pseudo-state estimator is designed for a category of nonlinear systems. It is shown that the method is extendible to form order/pseudo-state estimators for other classes of nonlinear systems using other traditional nonlinear observers. One of the advantages of the proposed methods is that a compact time interval is enough to guarantee bounded estimation error.

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BACKGROUND: Soft pneumatic robots have shown great promises in hand rehabilitation systems as alternatives to conventional rigid systems. However, their application is limited to clinical rehabilitation programs due to their dependency on large-sized compressors as air suppliers. This disadvantage triggered the search for compact portable pneumatic sources. METHOD: A compact valveless pneumatic source to control the bending angle of a soft actuator is proposed in this paper. The source incorporates two series of serially connected commercially available microcompressors to provide additional pressure and flowrate in two directions. In the proposed design, an inner-loop controller, controls the output characteristics of the source while an outer-loop controller handles the trajectory tracking of the angular position. RESULTS: Experimental results show that the source is capable of providing up to 160 kPa of output. The controller is able to track up to 2 rad/sec sinusoidal trajectory with a maximum 0.066 rad root-mean-square error. CONCLUSION: Experimental measurements showed satisfactory results in the maximum ratings and tracking errors whilst relatively low average power was consumed by eliminating control valves.

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In this paper, a combination of robust active controller and disturbance observer for active engine mounts is proposed as an effective way to improve driver comfort for automotive vehicles. First, a robust controller based on µ-synthesis is designed for the engine mounting system in the presence of parametric uncertainty. The effectiveness of the µ-controller in satisfying robust stability and performance is demonstrated. To further improve the performance of vibration control, an observer is designed where all system states and unknown disturbances are estimated. The estimated disturbance forces are used as control signals to cancel the unwanted effects of these disturbances. The results of the system behavior at different engine speeds clearly demonstrate a significant improvement of the system performance as a result of applying the system observer.

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In this paper, a new scheme for multi-lateral remote rehabilitation is proposed. There exist one therapist, one patient, and several trainees, who are participating in the process of telerehabilitation (TR) in this scheme. This kind of strategy helps the therapist to facilitate the neurorehabilitation remotely. Thus, the patients can stay in their homes, resulting in safer and less expensive costs. Meanwhile, several trainees in medical education centers can be trained by participating partially in the rehabilitation process. The trainees participate in a "hands-on" manner; so, they feel like they are rehabilitating the patient directly. For implementing such a scheme, a novel theoretical method is proposed using the power of multi-agent systems (MAS) theory into the multi-lateral teleoperation, based on the self-intelligence in the MAS. In the previous related works, changing the number of participants in the multi-lateral teleoperation tasks required redesigning the controllers; while, in this paper using both of the decentralized control and the self-intelligence of the MAS, avoids the need for redesigning the controller in the proposed structure. Moreover, in this research, uncertainties in the operators' dynamics, as well as time-varying delays in the communication channels, are taken into account. It is shown that the proposed structure has two tuning matrices (L and D) that can be used for different scenarios of multi-lateral teleoperation. By choosing proper tuning matrices, many related works about the multi-lateral teleoperation/telerehabilitation process can be implemented. In the final section of the paper, several scenarios were introduced to achieve "Simultaneous Training and Therapy" in TR and are implemented with the proposed structure. The results confirmed the stability and performance of the proposed framework.

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In this paper, an identifier-critic structure is introduced to find an online near-optimal controller for continuous-time nonaffine nonlinear systems having saturated control signal. By employing two Neural Networks (NNs), the solution of Hamilton-Jacobi-Bellman (HJB) equation associated with the cost function is derived without requiring a priori knowledge about system dynamics. Weights of the identifier and critic NNs are tuned online and simultaneously such that unknown terms are approximated accurately and the control signal is kept between the saturation bounds. The convergence of NNs' weights, identification error, and system states is guaranteed using Lyapunov's direct method. Finally, simulation results are performed on two nonlinear systems to confirm the effectiveness of the proposed control strategy.

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BACKGROUND: Recently, a great number of studies have been carried out to model soft tissue deformation in contact with surgical instruments to aid the development of surgical simulators. Precise methods to model the soft tissue such as the Finite Element Method (FEM) lack accuracy in large deformations. METHODS: An innovative meshless method is used, which has high precision and is applicable to large deformations. The meshless simulation method is implemented for a 2D beam and a 3D cube. Experiments are conducted for two silicone-gel samples to verify the correctness of the method. RESULTS: The meshless results in 2D and 3D show better accuracy for large deformations in comparison with the FEM. This method is used to model human organs such as liver and gallbladder. CONCLUSION: It is concluded that the proposed model exhibits good accuracy as well as speed. Thus, it seems promising to be employed in surgical simulators. Copyright © 2015 John Wiley & Sons, Ltd.

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The electro-hydraulic servo system (EHSS) demonstrates numerous advantages in size and performance compared to other actuation methods. Oftentimes, its utilization in industrial and machinery settings is limited by its inferior efficiency. In this paper, a nonlinear backstepping control algorithm with an energy-saving approach is proposed for position control in the EHSS. To achieve improved efficiency, two control valves including a proportional directional valve (PDV) and a proportional relief valve (PRV) are used to achieve the control objectives. To design the control algorithm, the state space model equations of the system are transformed to their normal form and the control law through the PDV is designed using a backstepping approach for position tracking. Then, another nonlinear set of laws is derived to achieve energy-saving through the PRV input. This control design method, based on the normal form representation, imposes internal dynamics on the closed-loop system. The stability of the internal dynamics is analyzed in special cases of operation. Experimental results verify that both tracking and energy-saving objectives are satisfied for the closed-loop system.

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This paper presents a tracking control methodology for a class of uncertain nonlinear systems subject to input saturation constraint and external disturbances. Unlike most previous approaches on saturated systems, which assumed affine nonlinear systems, in this paper, tracking control problem is solved for uncertain nonaffine nonlinear systems with input saturation. To deal with the saturation constraint, an auxiliary system is constructed and a modified tracking error is defined. Then, by employing implicit function theorem, mean value theorem, and modified tracking error, updating rules are derived based on the well-known back-propagation (BP) algorithm, which has been proven to be the most relevant updating rule to control problems. However, most of the previous approaches on BP algorithm suffer from lack of stability analysis. By injecting a damping term to the standard BP algorithm, uniformly ultimately boundedness of all the signals of the closed-loop system is ensured via Lyapunov's direct method. Furthermore, the presented approach employs nonlinear in parameter neural networks. Hence, the proposed scheme is applicable to systems with higher degrees of nonlinearity. Using a high-gain observer to reconstruct the states of the system, an output feedback controller is also presented. Finally, the simulation results performed on a Duffing-Holmes chaotic system, a generalized pendulum-type system, and a numerical system are presented to demonstrate the effectiveness of the suggested state and output feedback control schemes.

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