This paper develops a robust adaptive model reference impedance controller for an n-link robotic manipulator with parameter uncertainties, actuator saturation, and imprecise force sensor measurement. In order to handle the system uncertainties, the adaptive technique and prediction error are used. To reduce calculation complexity, the adaptation law for robot parameters estimation is designed in the joint space. Actuator saturation is also overcome by an auxiliary system, and imprecise force sensor measurement is compensated with utilization of adaptive bounding technique. Moreover, the joint acceleration vector problem, which is nonmeasurable, is covered by the filtered regressor matrix. Using Lyapunov's direct method, as long as two conditions are satisfied the proposed controller ensures the exponential stability of all the signals of the closed loop system; otherwise, the closed loop system is globally stable. Finally, simulations are performed on prototyped robots to analyze and evaluate the established controller as well as to demonstrate the effectiveness of the proposed scheme.

This paper presents a novel light-weight back-drivable inherently-safe robotic mechanism for delivering ankle rehabilitation therapies. The robot is designed to be used as the ankle module of a multi-purpose lower-limb rehabilitation robot. A novel friction-based safety feature has been introduced that enables mechanical adjustment of the maximum amount of allowable transfer forces and torques to the patient's limb. The design procedure, mathematical modeling and experimental validations are provided to demonstrate the performance of the proposed system.

This paper presents a novel multimodal training platform integrated with hand-over-hand (HOH) haptic guidance for dual-console surgical robotic systems such as the da Vinci Si system. The expert-in-the-loop (EIL) framework incorporates a fuzzy interface system in order to provide a trainee with adaptive authority over the procedure as well as hand-over-hand haptic guidance adjusted in real time based on the proficiency level of the trainee. The EIL expertise-oriented framework enables performance of a surgical procedure by an expert surgeon on a patient, while simultaneously providing a trainee at any stage of the motor-skills development with multimodal training without jeopardizing patient safety. Closed-loop stability of the system is investigated using the circle criterion and it is shown that the proposed architecture is unconditionally stable. Experimental evaluations are presented in support of the proposed platform through the implementation of a dual-console surgical setup consisting of the classic da Vinci surgical system (Intuitive Surgical, Inc., Sunnyvale, CA, USA) and the dV-Trainer master console (Mimic Technology, Inc., Seattle, WA, USA). To the best of our knowledge, the implemented setup is the first research platform for dual-console studies involving the classic da Vinci surgical system.

Control of the exerted force on objects as well as the position of the actuator during a cell characterization operation has an essential role in achieving an efficient performance. In such operations, any undesired applied force could degrade the efficiency or make damages to the object. A challenging problem associated with application to cell characterization using a piezoelectric actuator is to design a suitable force-position switching control law and also to deal with nonlinear hysteretic behavior of such actuators. The main concern in this operation is to provide a smooth switching transition between position and force control modes. A modified Prandtl-Ishlinskii (PI) operator and its inverse are utilized for both identification and real time compensation of the hysteresis nonlinear behavior. By proposing an appropriate force-position switching control approach, the smooth transition between these two modes is guaranteed. The stability of each controller as well as the switching system is demonstrated analytically. Experimental results depict that the proposed approach achieves precise force-position control within each modes and also the switching remains smooth.

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.

This paper proposes an expertise-oriented training platform for robotics-assisted minimally invasive surgery. The framework builds on previous work of the authors and makes use of dual-user teleoperation scenario, allowing the presence of an expert in the training loop. A Fuzzy-Logic (FL) methodology is proposed, which specifies the level/mode of the training required for the trainee according to his/her level of proficiency over the task. A major advantage of the proposed FL approach is that, having the expert in the loop, it can specify the trainee's proficiency level relative to that of the expert in real-time. Moreover, based on the relative skills assessment, the proposed FL approach decides if or to what extent the trainee should receive a haptic guidance force based on Virtual Fixtures or the environment force from the interaction between the surgical instrument and tissue at the slave side. In addition to the level/mode of the haptics-enabled training required for the trainee, the proposed FL framework specifies the authority level of the trainees over the operation in real-time, according to their proficiency levels over the task. Stability of the overall closed-loop teleoperated system is also investigated using the small-gain theorem, resulting in a sufficient condition to guarantee stability in the presence of constant communication delays. Finally, experimental results are given to evaluate the design and feasibility of the proposed framework.

This paper presents a novel scale-dependent method for grasp evaluation based on so-called size ratio (introduced in this paper) and the ability of the grasp to reject disturbance forces. Manipulating large objects, comparable to the robot size is considered. This is the case of a whole arm manipulation (WAM) in which the robot arm can manipulate larger objects as the whole surface is used. In this way task-oriented information is incorporated in proposed grasp evaluation criterion. Examples are presented to demonstrate the validity of the proposed approach.

A model predictive method is proposed to control the power ramp rate of a wind turbine system integrated with ultracapacitors as its high power, short-term, energy storage. In this method, it is assumed that the wind turbine output power can be predicted within a predefined horizon time interval. The proposed method involves two steps. First, the total stored energy required to maintain a preset power ramp rate during the prediction horizon time is calculated. Should the value of the calculated energy exceed the storage capacity, the power ramp rate is incremented gradually until the storage system can supply the required energy. Next, the prediction horizon is shifted forward by a desirable time interval and the previous step is repeated. It is shown that the expansion of the prediction time interval decreases the average power ramp, enhancing the efficiency of the proposed method.

This paper focuses on control challenges caused by the slave-arm flexibility in master-slave telerobotic systems. Apart from time delay, the nonlinear non-minimum phase deflection of flexible-link slave manipulators creates extra challenges for control of telerobotic systems. These challenges have forced most of the prior research to use basic simplifications. In this paper, we try to remove most of the simplifications using a more realistic model for slave deflections. The degrading effects of flexibility on stability and performance of conventional telerobotics architectures are analyzed. Then, the standard Extended Lawrence Four-Channel (ELFC) control architecture is modified to obtain the stability condition and to enhance performance. Finally, the input-to-output stability (IOS) Small-Gain Theorem is used to generalize the stability criteria for varying delay and to remove the restrictive assumption of deflection-linearity. The proposed architecture consists of a local Partial Feedback Linearization scheme embedded into a modified ELFC architecture. This study is motivated by the use of light-weight cable driven tools in telerobotic surgery.

In this paper, a novel decentralized multilateral structure is proposed for the dual user systems in the presence of communication delay. The proposed structure utilizes adaptive impedance control approach in order to overcome the destructive effect of the time-delay on system desired-objectives, which is a disregarded issue in the previous studies on dual user system. The proposed control strategy, which utilizes three desired impedance surfaces defined in the paper, satisfactorily brings the system hybrid matrix close to the ideal one that guarantees the system stability and transparency. The controller is designed in a way that eliminates the necessity of the delay estimation as one of its outstanding characteristics; consequently, the unknown communication time-delay can be handled via this structure while previous studies have disregarded the issue of time delay in dual user system. Furthermore, the adaptive structure of the controller promises to overcome the uncertainties on robot's dynamics. In addition, the efficiency of the controller in guaranteeing the system stability in the presence of unknown communication delay is investigated through passivity theory and the presented analysis illustrates complete independency of the closed-loop system stability on time delay value applying the proposed controller. Experimental results performed on a delayed dual user system demonstrate validity of the proposed scheme.

In this paper, a novel function approximation approach based on a combination of conventional Neural Networks and tile coding approximators is proposed. The proposed approach can maintain the desired features of both approaches whiles eliminates the deficiencies of each method. The combination will reduce the sharpness of tile coding. It will also provide an easy way to adjust the accuracy/complexity of the approximation according to the function being approximated (adaptive tiling) and the subspace used on. In this algorithm, it is possible to construct the approximator with specified and various approximation accuracies in different subspaces. This feature enables us to allocate an arbitrary accuracy/complexity wherever a more accurate approximation is needed. Finally simulation studies are presented to show the efficiency of and applicability of the proposed approach.

This paper presents a novel dynamic formation control strategy for multiple nonholonomic mobile robots. The main idea is converting the leader-follower formation control problem to an equivalent tracking control problem for follower robot based on the states of the relative motion between the robots. Then, the dynamic model of robots is used in formation. A formation controller, consisting of a feedback linearization part and a sliding mode compensator, is designed to stabilize the overall system. The proposed controller generates the commanded torques for the follower robot and makes the formation control system robust to the effect of unknown bounded disturbances in dynamic modeling. Furthermore, on active obstacle avoidance is presented by considering the obstacle as a virtual leader in our proposed model. Simulation results are presented to verify the performance of the proposed scheme.

This paper presents an online learning scheme to train a Cellular Neural Network (CNN) which can be used to model multidimensional systems whose dynamics are governed by Partial Differential Equations (PDE). Most of the previous work on CNN, employed fixed parameters or learning methods which need many iterations of an algorithm. There is a lack of fast, online and robust training method in the field of cellular neural networks. The learning method presented in this paper is a modified online backpropagation (BP) algorithm. The modification is concerned with adding a damping term which enhances the robustness of the training scheme. Another modification is decrease the learning rate to avoid unwanted oscillations. To evaluate the performance of the training scheme a set of simulations are performed on two-dimensional heat conduction problem. The results obtained by using CNN are compared to those of analytic solutions.

The observer-based tracking control problem for a class of nonlinear affine systems using neural networks is proposed in this paper. The controller is based on feedback linearization where the observer system and control signal are directly estimated by a nonlinear in parameter neural networks (NLPNN). A Hebbian-like algorithm with e-modification is used to update the weights of the network. The uniformly ultimately boundedness of the tracking error and all signals in the overall closed-loop system is proved using Lyapunov's direct method. To evaluate the performance of the proposed observer-based controller, a set of simulations is performed on a nonlinear cart-pole system. Simulation results show the effectiveness of the proposed control methodology.

A distributed coverage algorithm for a team of robots based on a hexagonal formation is presented in this paper for complete coverage of free space. The main property of hexagonal formation is the high fault tolerance due to the overlap coverage of the three agents in each direction. Moreover, a coverage degree of 3 is also guaranteed for almost all points of the area. Another important feature of the proposed approach is that it does not require the absolute positions of the agents. The formed cluster is then considered as a single robot; hence, the coverage algorithm developed for a single robot can be extended to solve the resulting coverage problem. A common objective, especially in a deterministic node deployment, is to use a minimum number of nodes (sensors). In this approach, however, the maximum coverage is guaranteed for a fixed number of agents.

This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.

This paper presents a novel hybrid fault diagnosis approach to detect and estimate component faults in general nonlinear systems with full-state measurement. Unlike most existing fault diagnosis techniques, the proposed solution provides an integrated framework to simultaneously detect, isolate, and estimate the severity of faults in system components. The proposed solution consists of a bank of adaptive Neural Parameter Estimators (NPE) where each NPE in the bank is designed based on a separate parameterized fault model. Each NPE in the bank estimates its corresponding unknown Fault Parameter (FP) that is further used for fault detection and estimation purposes. Fast convergence and simple isolation policy are among the characteristic features of our proposed solution. Static neural network architecture and simple weight adaptation laws also make the proposed technique appropriate for real-time implementations. Simulation results reveal the effectiveness of the developed scheme in detecting, isolating and estimating faults in components of reaction wheel actuators of a 3-axis stabilized satellite even in presence of satellite disturbances.

This paper presents a robust Fault Detection and Isolation (FDI) scheme for a general nonlinear system using a neural network based observer. The nonlinear system is subject to state and sensor uncertainties and disturbances. A recurrent nonlinear-in-parameters neural network (NLPNN) is employed to identify the general unknown fault. The neural network weights are updated based on a modified backpropagation scheme. Unlike many previous methods, the proposed fault detection and isolation scheme does not rely on the availability of all state measurements. The stability of the overall fault detection approach in the presence of unknown faults as well as plant and sensor uncertainties is shown using Lyapunov's direct method. Stability analysis presented here imposes no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer type actuators that are commonly utilized in the satellite attitude control system is considered as a case study. The effectiveness of the proposed fault diagnosis strategy is demonstrated via simulations.

This paper presents an actuator Fault Detection and Isolation (FDI) scheme for nonlinear systems. A state space approach is used and a nonlinear-in-parameters neural network (NLPNN) is employed to identify the additive unknown fault. The FDI scheme is based on a hybrid model (composed of an analytical nominal model and a neural network model) of the nonlinear system. The nominal performance of the system in fault free operation is governed by analytical model whereas the uncertainties and unmodeled dynamics are accounted for by intelligent (neural network based) model. The neural network weights are updated based on a modified backpropagation scheme. The stability of the overall fault detection scheme is shown using Lyapunov's direct method. To evaluate the performance of the proposed fault detection scheme, FDI in a spacecraft attitude control systems with reaction wheel type of actuators is considered as a case study. Simulation results are presented to show the effectiveness of the proposed fault detection scheme.

In this paper, the stability problem of neural network based observers/identifiers for nonlinear systems is revisited when nonlinear-in-parameter neural networks (NLPNN) are employed. The proposed approach is based on decomposing the neural network into two subsystems. The first subsystem (Subsystem 1) consists of the estimation error and output-layer weight error and the second subsystem (Subsystem 2) consists of the hidden-layer weight error. The key to this decomposition is that the hidden-layer weights appear in Subsystem 1, only as an argument of a sigmoidal function and its derivative which are both known to be bounded. This allows us to regard the Subsystem 1 as a linear-in-parameter neural network (LPNN) whose stability proof is more straightforward. Having shown the stability of the first subsystem, the stability of the second subsystem is also shown subsequently without the requirement of having the limiting assumptions of previous work. The bound on estimation error can be made arbitrarily small by proper selection of design parameters. The estimation scheme is then employed to estimate the state of flexible joint manipulators.

A stable neural network (NN)-based observer for general multivariable nonlinear systems is presented in this paper. Unlike most previous neural network observers, the proposed observer uses a nonlinear-in-parameters neural network (NLPNN). Therefore, it can be applied to systems with higher degrees of nonlinearity without any a priori knowledge about system dynamics. The learning rule for the neural network is a novel approach based on the modified backpropagation (BP) algorithm. An e-modification term is added to guarantee robustness of the observer. No strictly positive real (SPR) or any other strong assumption is imposed on the proposed approach. The stability of the recurrent neural network observer is shown by Lyapunov's direct method. Simulation results for a flexible-joint manipulator are presented to demonstrate the enhanced performance achieved by utilizing the proposed neural network observer.

In this paper, a new approach for sensor and actuator fault detection and estimation in unknown nonlinear systems is proposed. Model-free structure and no a priori knowledge about the faults are two main properties of the proposed method that make it a viable candidate for real-time applications. First, a neuro-fuzzy technique is used to obtain a nominal models of the system based on input-output data in normal system operation. Actuator and sensor faults are then estimated such that the error between the output of the model and the actual output is minimized. The gradient descent method is used to update the fault estimated values. The estimated values are subsequently used for fault accommodation. Simulation results for a two link planar robot manipulator are presented to demonstrate the effectiveness of the proposed approach.

The problem of controlling a discrete-time nonlinear non-minimum phase system is considered. An output re-definition strategy is developed which is applicable to a class of open-loop stable nonlinear systems whose input-output maps contain nonlinear terms from the output and linear terms from the input. No a priori knowledge about the nonlinearities of the system is required. The output re-definition scheme is based on first identifying the nonlinearities of the system using neural networks and then modifying the system zero dynamics. A stable/anti-stable factorization is performed on the zero dynamics of the system. The new output is re-defined using, the neural identifier and the stable part of the zero dynamics. A controller is then designed based on the new output whose zero dynamics are stable and can be inverted. Simulation results are resented to show the effectiveness of the proposed control scheme as compared to both linear and nonlinear conventional controllers.

The problem of controlling the tip position of a flexible-link manipulator is considered. The control strategy is based on an output re-definition approach which is applicable to a class of non-minimum phase nonlinear systems whose nonlinearities appear in output terms in their input-ouput mappings and are open-loop stable. The controller is composed of a stabilizing joint PD controller and an output re-definition tracking controller. The output re-definition scheme is based on modifying the zero dynamics of the system using a stable/anti-stable factorization. The controller is then designed based on the new output whose zero dynamics are stable and can be inverted. Experimental results are also presented to show the effectiveness of the proposed control scheme as compared to a PD controller.

The problem of controlling a nonminimum phase nonlinear system with application to tip position control of a flexible-link manipulator is considered. An output re-definition strategy is developed which is applicable to a class of open-loop stable nonlinear systems whose input-output maps contain nonlinear terms from output and linear terms from input. No a priori knowledge about the nonlinearities of the system is required. The output re-definition scheme is based on first identifying the nonlinearities of the system using neural networks and then modifying the system zero dynamics. A stable/anti-stable factorization is performed on the zero dynamics of the system. The new output is re-defined using the neural identifier and the stable part of the zero dynamics. A controller is then designed based on the new output whose zero dynamics are stable and can be inverted. For the flexible-link manipulator case, the controller is composed of a stabilizing joint PD controller and an output re-definition tracking controller. Experimental and simulation results are presented to show the effectiveness of the proposed control scheme as compared to both linear and nonlinear conventional controllers.

Experimental evaluation of the performance of neural network-based controllers for tip position tracking of flexible-link manipulators is presented. A modified output re-definition approach is utilized to overcome the problem caused by the non-minimum phase characteristic of the flexible-link system. This modification is based on using minimum a priori knowledge about the system dynamics. The modified output redefinition approach requires a priori knowledge about the linear model of the system and no a priori knowledge about the payload mass. Four different neural network schemes are proposed. The neural networks are trained and employed as online controllers. The four proposed neural network controllers are implemented on a single flexible-link experimental test-bed. Experimental and simulation results are presented to illustrate the advantages and improved performance of the proposed tip position tracking controllers over conventional PD-type controllers in the presence of unmodeled dynamics.

This paper presents dynamic modeling of flexible link manipulators using artificial neural networks. A state-space representation is considered for a neural identifier. The recurrent network configuration is obtained by a combination of feedforward network architectures with dynamical elements in the form of stable filters. To guarantee the boundedness of the states, joint PD control is introduced in the system. The method can be considered both as an online identifier that can be used as a basis for designing neural network controllers as well as an off-line learning scheme to compute deflections due to link flexibility for evaluating forward dynamics. The performance of the proposed neural identifier is evaluated by identifying the dynamics of different flexible-link manipulators. To demonstrate the effectiveness of the algorithm, simulation results for a single-link manipulator, a two-link planar manipulator and the Space Station Remote Manipulator System (SSRMS) are presented.

This paper presents neural network based adaptive controllers for a single flexible-link manipulator. The control is designed by using the output re-definition approach. Three different neural network schemes are examined. None of the schemes requires full state measurements. The neural network controllers are implemented on a single flexible-link experimental test-bed. Experimental and simulation results are presented to illustrate the advantages and improved performance of the proposed tip position tracking controllers over the conventional PD-type controller.