Closed-loop Robotic System Research Articles

Passive Model-Predictive Impedance Control for Safe Physical Human–Robot Interaction

Various cognitive systems have been designed to model the position and stiffness profiles of human behavior and then to drive robots by mimicking the human’s behavior to accomplish physical human-robot interaction tasks through a properly designed impedance controller. However, some studies have shown that variable stiffness parameters of the impedance controller can cause the violation of the passivity constraint of the robot states, and make the robot’s stored energy exceed the external energy injected from the human user, thus leading to the unsafe human-robot interaction. To solve this problem, this paper proposes a novel passive model predictive impedance control method including two control loops. In the bottom-loop of the proposed controller, the robot is driven by a variable impedance controller to achieve the desired compliant interaction behavior. In the top-loop of the proposed controller, the model predictive control (MPC) is used to ensure that the robot states satisfy the passivity constraint by calculating a complementary torque to limit the stored energy of the robot. The passivity of the closed-loop robot system and the feasibility of MPC are guaranteed by theoretical analysis, ensuring the safety of the robotic movement in the human-robot interaction. The effectiveness of the proposed method is demonstrated by the simulation and experiment on the Franka Emika Panda robot.

Skin-Inspired Multi-Modal Mechanoreceptors for Dynamic Haptic Exploration.

Active sensing is a fundamental aspect of human and animal interactions with the environment, providing essential information about the hardness, texture, and tackiness of objects. This ability stems from the presence of diverse mechanoreceptors in the skin, capable of detecting a wide range of stimuli and from the sensorimotor control of biological mechanisms. In contrast, existing tactile sensors for robotic applications typically excel in identifying only limited types of information, lacking the versatility of biological mechanoreceptors and the requisite sensing strategies to extract tactile information proactively. Here, inspired by human haptic perception, a skin-inspired artificial 3D mechanoreceptor (SENS) capable of detecting multiple mechanical stimuli is developed to bridge sensing and action in a closed-loop sensorimotor system for dynamic haptic exploration. A tensor-based non-linear theoretical model is established to characterize the 3D deformation (e.g., tensile, compressive, and shear deformation) of SENS, providing guidance for the design and optimization of multimode sensing properties with high fidelity. Based on SENS, a closed-loop robotic system capable of recognizing objects with improved accuracy (≈96%) is further demonstrated. This dynamic haptic exploration approach shows promise for a wide range of applications such as autonomous learning, healthcare, and space and deep-sea exploration.

Decentralized fault-tolerant control of modular robot manipulators with actuator saturation: neural adaptive integral terminal sliding mode-based control approach

A novel neural adaptive integral terminal sliding mode control for decentralized fault-tolerant control strategy, including the integral terminal sliding mode surface, the nonlinear disturbance observer, the radial basis neural network and robust controller, is presented in this paper to achieve fault-tolerant control of modular robot manipulators. First, the integral terminal sliding mode is designed for the fault-tolerant controller. Then, to boost the performance of the controlled system, the radial basis neural network and disturbance observer are introduced to approximate the nonlinear terms and disturbances. The reconstructed approximate uncertainty term is applied as compensation. Next, the super-twisting technique is employed to compensate for estimation errors to ensure stability. In addition, for the actuator saturation problem, the radial basis function neural network-based compensation control is proposed. Finally, the stability of the closed-loop robotic system is demonstrated based on Lyapunov theory. Computer simulations verified the efficiency and advantages of the presented approach.

Tansig type NLESO-based integral terminal sliding mode control for modular robot manipulators with uncertain disturbance: Theory and experimental verification

To overcome the mutual constraints between the system chattering and the arrival time in conventional sliding mode control in this paper, a tangent excitation function (tansig) type nonlinear extended state observer (NLESO)-based integral terminal sliding mode control is proposed to solve the trajectory tracking control task of the modular robot manipulators (MRMs) possessing strong coupling and complex time-varying properties. Through the joint torque feedback technology, the MRM system dynamic model is formulated; on this basis, the proposed NLESO based on tansig is utilized to estimate and compensate the system uncertain information, which are consisted of the friction, interconnected dynamic coupling, and external disturbance. The proposed NLESO can simplify the traditional extended state observer design parameters for theoretical analysis and practical application. The proposed tansig-type NLESO-based integral terminal sliding mode control integrated adaptive term exponential reaching law for MRMs is designed to diminish the arrival time when the system state converges to the equilibrium point. The presented algorithm improves the robotic system antidisturbance ability. Furthermore, the Lyapunov theory is utilized to verify stability of the proposed NLESO and closed-loop robotic system. Finally, the availability of the proposed algorithm is demonstrated by experiments.

Closed-loop sound source localization in neuromorphic systems

Sound source localization (SSL) is used in various applications such as industrial noise-control, speech detection in mobile phones, speech enhancement in hearing aids and many more. Newest video conferencing setups use SSL. The position of a speaker is detected from the difference in the audio waves received by a microphone array. After detection the camera focuses onto the location of the speaker. The human brain is also able to detect the location of a speaker from auditory signals. It uses, among other cues, the difference in amplitude and arrival time of the sound wave at the two ears, called interaural level and time difference. However, the substrate and computational primitives of our brain are different from classical digital computing. Due to its low power consumption of around 20 W and its performance in real time the human brain has become a great source of inspiration for emerging technologies. One of these technologies is neuromorphic hardware which implements the fundamental principles of brain computing identified until today using complementary metal-oxide-semiconductor technologies and new devices. In this work we propose the first neuromorphic closed-loop robotic system that uses the interaural time difference for SSL in real time. Our system can successfully locate sound sources such as human speech. In a closed-loop experiment, the robotic platform turned immediately into the direction of the sound source with a turning velocity linearly proportional to the angle difference between sound source and binaural microphones. After this initial turn, the robotic platform remains at the direction of the sound source. Even though the system only uses very few resources of the available hardware, consumes around 1 W, and was only tuned by hand, meaning it does not contain any learning at all, it already reaches performances comparable to other neuromorphic approaches. The SSL system presented in this article brings us one step closer towards neuromorphic event-based systems for robotics and embodied computing.

Initial-Rectification Neuro-Adaptive Iterative Learning Control for Robot Manipulators With Input Deadzone and Nonzero Initial Errors

An initial-rectification adaptive iterative learning control scheme is proposed to solve the angle tracking problem of robot manipulators with input deadzone under nonzero initial errors. Lyapunov approach is utilized to design the controller. First, the initial-rectification auxiliary reference signal is constructed to overcome the obstacle caused by nonzero initial errors during ILC design. Second, adaptive ILC strategy and robust control strategy are adopted for dealing with deadzone nonlinearity. In addition, adaptive learning neural network is applied to approximate for uncertainties. The stability of closed-loop robotic system is rigorously proven by theoretical analysis. In the end, numerical simulation results are provided to verify the effectiveness of the proposed adaptive iterative learning control scheme.

An End-to-End Spiking Neural Network Platform for Edge Robotics: From Event-Cameras to Central Pattern Generation

Learning to adapt one’s gait with environmental changes plays an essential role in the locomotion of legged robots which remains challenging for constrained computing resources and energy budget, as in the case of edge-robots. Recent advances in bio-inspired vision with dynamic vision sensors (DVSs) and associated neuromorphic processing can provide promising solutions for end-to-end sensing, cognition, and control tasks. However, such bio-mimetic closed-loop robotic systems based on event-based visual sensing and actuation in the form of spiking neural networks (SNNs) have not been well explored. In this work, we program the weights of a bio-mimetic multigait central pattern generator (CPG) and couple it with DVS-based visual data processing to show a spike-only closed-loop robotic system for a prey-tracking scenario. We first propose a supervised learning rule based on stochastic weight updates to produce a multigait producing spiking-CPG (SCPG) for hexapod robot locomotion. We then actuate the SCPG to seamlessly transition between the gaits for a nearest prey tracking task by incorporating SNN-based visual processing for input event-data generated by the DVS. This for the first time, demonstrates the natural coupling of event data flow from event-camera through SNN and neuromorphic locomotion. Thus, we exploit bio-mimetic dynamics and energy advantages of spike-based processing for autonomous edge-robotics.

A New Fuzzy Controlled Antenna Network Proposal for Small Satellite Applications

This research contributes to small satellite system development based on electromagnetic modeling and an integrated meta-materials antenna networks design for multimedia transmission contents. It includes an adaptive nonsingular mode tracking control design for small satellites systems using fuzzy waveless antenna networks. By analyzing and modeling based on electromagnetic methods, propagation properties of guided waves from metallic structures with simple or complex forms charge partially or entirely by anisotropic materials such as metamaterials. We propose a system control rule to omit uncertainties, including the inevitable approximation errors resulting from the finite number of fuzzy signal power value basis functions in antenna networks. Moreover, both the stability and the tracking performance of the closed-loop robotic system are experimentally validated. The research lies within the scope of the improvement of speed, effectiveness, and precision of numerical methods applied to electro-magnetic modeling with complex structures, essentially rectangular metallic waveguides filled with isotropic or anisotropic metamaterials. Three axes of our research are presented: waveguides, filters, and antennas. The proposed controller does not require prior knowledge about the dynamics of the fuzzy system controller for antenna networks or the offline learning phase. In addition, this work contributes to solving the problem of non-visibility stations to ensure data transmission in wireless networks. The proposed solution maximizes inter-connection by using a fuzzy controlled antenna network, and the novelty guarantees non-limited interconnection in wireless networks compared to traditional methods.

Learning-Based Balance Control of Wheel-Legged Robots

This letter studies the adaptive optimal control problem for a wheel-legged robot in the absence of an accurate dynamic model. A crucial strategy is to exploit recent advances in reinforcement learning (RL) and adaptive dynamic programming (ADP) to derive a learning-based solution to adaptive optimal control. It is shown that suboptimal controllers can be learned directly from input-state data collected along the trajectories of the robot. Rigorous proofs for the convergence of the novel data-driven value iteration (VI) algorithm and the stability of the closed-loop robot system are provided. Experiments are conducted to demonstrate the efficiency of the novel adaptive suboptimal controller derived from the data-driven VI algorithm in balancing the wheel-legged robot to the equilibrium.

Dynamic modeling of closed-chain robotic manipulators in the presence of frictional dynamic forces: A planar case

In this paper, the phenomenon of impact-contact in a closed-loop robotic chain has been dynamically modeled. The motion of this system, which comprises rigid links and ideal joints, has two phases. In the flight phase, the system is suspended in the air and moves only under the effect of Earth’s gravity. And in the impact phase, the mentioned robotic mechanism collides with a rough surface. We first present the direct form of dynamic equations for an open chain robotic system in the suspension phase. Then by applying the constraints that originate from the closed topology of the examined kinematic chain, the differential equations of the closed-loop system are obtained for the flight phase. In the impact phase, the contact force model is employed for modeling the impact-contact phenomenon. Although this method of impact modeling is more realistic, it leads to the stiffening of motion equations. In order to solve these equations, a special computational algorithm has been developed in this paper. To achieve more realistic results, the force of friction has also been considered in the collision of system joints with the ground. The dynamic formulations of friction have been used to obtain the friction force. In these models, an additional state variable is added to system variables in order to simulate some of the existing friction phenomena. Another objective of this paper is to present a criterion for selecting the most suitable model for simulating the motion of closed-loop robotic systems in the presence of friction. In this regard, the four dynamic friction force models of Dahl, reset integrator, LuGre, and elasto-plastic have been compared with the two static models of Coulomb and Bengisu & Akay. Although the developed algorithm is able to simulate any closed-loop robotic system that comes into contact with an uneven surface, the simulation of a 5-rigid-link closed-loop mechanism in collision with a rough surface of sinusoidal profile has been presented in this paper.

Optimal grasp force for robotic grasping and in-hand manipulation with impedance control

PurposeThe purpose of this paper is to achieve stable grasping and dexterous in-hand manipulation, the control of the multi-fingered robotic hand is a difficult problem as the hand has many degrees of freedom with various grasp configurations.Design/methodology/approachTo achieve this goal, a novel object-level impedance control framework with optimized grasp force and grasp quality is proposed for multi-fingered robotic hand grasping and in-hand manipulation. The minimal grasp force optimization aims to achieve stable grasping satisfying friction cone constraint while keeping appropriate contact forces without damage to the object. With the optimized grasp quality function, optimal grasp quality can be obtained by dynamically sliding on the object from initial grasp configuration to final grasp configuration. By the proposed controller, the in-hand manipulation of the grasped object can be achieved with compliance to the environment force. The control performance of the closed-loop robotic system is guaranteed by appropriately choosing the design parameters as proved by a Lyapunove function.FindingsSimulations are conducted to validate the efficiency and performance of the proposed controller with a three-fingered robotic hand.Originality/valueThis paper presents a method for robotic optimal grasping and in-hand manipulation with a compliant controller. It may inspire other related researchers and has great potential for practical usage in a widespread of robot applications.

Compensator-critic structure-based neuro-optimal control of modular robot manipulators with uncertain environmental contacts using non-zero-sum games

In this paper, a novel compensator-critic structure-based neuro-optimal control approach is presented for modular robot manipulators (MRMs) in contact with uncertain environments. Based on joint torque feedback (JTF) technique, the dynamic model of the manipulator systems is described as an integration of joint subsystems associated with the effect of interconnected dynamic coupling (IDC). A local dynamic information-based robust compensator is designed to engage the model uncertainty compensation, and then, the optimal tracking control problem of an MRM system is transformed into an n-player non-zero-sum game issue of multiple joint subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a cost function approximator, which is constructed by using only critic neural networks (NNs), is developed for solving the Hamilton–Jacobi (HJ) equation, thus facilitating the feasible derivation of the neuro-optimal control policy. The tracking error of the closed-loop robotic system is proved to be uniformly ultimately bounded (UUB) on the basis of the Lyapunov theory. Finally, experiment results illustrated the advantage and effectiveness of the developed control method.

Self-Triggered Model Predictive Control for Perturbed Underwater Robot Systems

Aiming at solving the control problem of a constrained and perturbed underwater robot, a control method was proposed by combining the self-triggered mechanism and the nonlinear model predictive control (NMPC). The theoretical properties of the kinematic model of the underwater robot, as well as the corresponding MPC controller, are first studied. Then, a novel technique for determining the next update moment of both the optimal control problem and the system state is developed. It is further rigorously proved that the proposed algorithm can (1) stabilize the closed-loop underwater robot system, (2) reduce the time of solving the optimal control problem and (3) save the information transfer resources. Finally, a case study is provided to show the effectiveness of the developed researched scheme.

Decentralized robust zero-sum neuro-optimal control for modular robot manipulators in contact with uncertain environments: theory and experimental verification

This paper presents a decentralized robust zero-sum optimal control approach for modular robot manipulators (MRMs) in contact with uncertain environments based on the adaptive dynamic programming (ADP) algorithm. The dynamic model of MRMs is formulated via joint torque feedback technique that is deployed for each joint module to design the model compensation controller. An uncertainty decomposition-based robust control is developed to compensate the model uncertainties, and then, the robust optimal control problem of the MRM system is transformed into a two-player zero-sum optimal control one. According to the ADP algorithm, the Hamilton–Jacobi–Isaacs equation can be solved by establishing action and critic neural networks, thus making the derivation of the approximate optimal control policy feasible. Based on the Lyapunov theory, the closed-loop robotic system is proved to be asymptotic stable under the developed decentralized control method. Finally, experiments are conducted to verify the effectiveness and advantages of the proposed method.

Actor-Critic-Identifier Structure-Based Decentralized Neuro-Optimal Control of Modular Robot Manipulators With Environmental Collisions

This paper presents a decentralized zero-sum optimal control method for MRMs with environmental collisions via an actor-critic-identifier (ACI) structure-based adaptive dynamic programming (ADP) algorithm. The dynamic model of the MRMs is formulated via a novel collision identification method that is deployed for each joint module, in which the local position and torque information are used to design the model compensation controller. A neural network (NN) identifier is developed to compensate the model uncertainties and then, the optimal control problem of the MRMs with environmental collisions can be transformed into a two-player zero-sum optimal control one. Based on the ADP algorithm, the Hamilton-Jacobi-Isaacs (HJI) equation is solved by constructing the actor-critic NNs, thus making the derivation of the approximate optimal control policy feasible. Based on the Lyapunov theory, the closed-loop robotic system is proved to be asymptotically stable. Finally, the experiments are conducted to verify the effectiveness and advantages of the proposed method.

Finite-Time Adaptive Fuzzy Tracking Control Design for Parallel Manipulators with Unbounded Uncertainties

In this paper, a finite-time adaptive fuzzy tracking control scheme is proposed for parallel manipulators with unbounded uncertainties. The proposed approach is based on fuzzy logic systems (FLS), nonsingular fast terminal sliding mode control (NFTSMC) and adaptive sliding compensation from nonlinearities in output feedback. FLS are adopted to handle system uncertainties and external disturbances. This learning approach does not require knowledge of parallel manipulator dynamics. The proposed control scheme can possess several advantages over other state-of-the-art approaches such as rapid response, robustness, higher precision, nonsingular, and finite-time converge. Besides, since the proposed approach is a hybrid scheme of NFTSMC and FLS, the proposed controller can have much better tracking control performance. The finite-time convergence and stability of the closed-loop robotic system are ensured by the Lyapunov theory. Finally, from the simulation results, it is clearly evident that the proposed approach can indeed outperform previous existing methods.

Switching Motion Control of Aircraft Skin Inspection Robot Using Backstepping Scheme and Nussbaum Disturbance Observer

In this paper, a backstepping control using switching strategy is proposed for the motion control of the aircraft skin inspection robot with double frame in the presence of external disturbances. The inspection robot equipped with a CCD camera and an ultrasonic sensor can alternately adsorb and move on the aircraft surface. The influence of the external disturbances is obvious in the switching motion. The disturbances are efficiently estimated using a Nussbaum disturbance observer (NDO), and the disturbance observer errors are uniformly ultimately bounded. Then, the backstepping control method is used to design the motion controller. The stability of the closed-loop robot system is proved by Lyapunov analysis through the average dwell time method.The tracking errors and the disturbance observer errors are semi-globally uniformly bounded using the proposed control scheme with NDO,and NDO is compared with Super-twisting disturbance observer. Finally, simulation results illustrate that the proposed control scheme with NDO can achieve satisfactory tracking performance under the external disturbance.

Decentralized robust optimal control for modular robot manipulators via critic-identifier structure-based adaptive dynamic programming

This paper presents a decentralized robust optimal control method for modular robot manipulators (MRMs) via a critic-identifier structure-based adaptive dynamic programming (ADP) scheme. The robust control problem of MRMs is transformed into an optimal compensation control issue, which consists of model-based compensation control, identifier-based learning control and ADP-based optimal control. The dynamic model of MRMs is deployed for each joint module where the local dynamic information is utilized to design the model compensation controller. A neural network (NN) identifier is established to approximate the interconnected dynamic coupling. Based on ADP and local online policy iteration algorithm, the Hamiltonian–Jacobi–Bellman equation is solved by constructing a critic NN, and then the approximate optimal control policy derivation is possible. The closed-loop robotic system is asymptotic stable by the implementation of a set of developed decentralized control policies. Simulations are presented to demonstrate the effectiveness of the proposed method.

Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems

This paper addresses the trajectory-tracking control problem of mobile robot systems with nonholonomic constraints, in the presence of time-varying parametric uncertainties and external disturbances. This necessitates an accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence control technique to design a robust controller to meet the control objectives. The design of the intelligent controller is based on the optimal control theory, the adaptive neural network system, and the robust control technique. The trajectory-tracking problem is solved using the optimal control methodology. Since the nonholonomic wheeled mobile robot is strongly nonlinear, the neural network system is applied to approximate the nonlinear function in the optimal control law. The robust controller, for his part, is then applied to adaptively estimate an unknown upper bound of the time-varying parametric uncertainties, external disturbances and approximation error of the neural network system. The stability of the closed-loop robot system is proven using the optimal control theory and Lyapunov stability analysis. The results of the simulation studies on three typical nonholonomic mobile robots are provided to demonstrate the effectiveness of the proposed controller. In addition, a comparative study with a recent robust adaptive controller shows that our proposed intelligent controller gives better results, in the sense that the output trajectory converges to the steady state faster with smaller tracking error.

Adaptive fuzzy sliding mode control for redundant manipulators with varying payload

Purpose This paper aims to present an adaptive fuzzy sliding mode controller with nonlinear observer (AFSMCO) for the redundant robotic manipulator handling a varying payload to achieve a precise trajectory tracking in the task space. This approach could be applied to solve the problems caused by the dynamic effect of the varying payload to robotic system caused by model uncertainties. Design/methodology/approach First, a suitable observer using the recursive algorithm is presented for an accurate estimation of external disturbances caused by a variable payload. Second, the adaptive fuzzy logic is designed to approximate the parameters of the sliding mode controller combined with nonlinear observer (SMCO) to avoid chattering in real time. Moreover, Lyapunov theory is applied to guarantee the stability of the proposed closed-loop robotic system. Finally, the effectiveness of the proposed control approach and theoretical discussion are proved by simulation results on a seven-link robot and demonstrated by a humanoid robot platform. Findings The varying payload leads to large variations in the dynamics of the manipulator and the tracking error. To achieve high-precision position tracking, nonlinear observer was introduced to feed into the sliding mode control (SMC) which had improved the ability to resist the external disturbance. In addition, the chattering caused by the SMC was eliminated by recursively approximating the switching gain with the usage of adaptive fuzzy logic. Therefore, a distributed control strategy solves the problems of an SMC implementation in improving its tracking performance and eliminating the chattering of the system control. Originality/value The AFSMCO is proposed for the first time and used to control the redundant robotic manipulator that handles the varying payload. The proposed control algorithm possesses better robustness and higher precision for the trajectory tracking than classical SMC.