Optimal Robot-Environment Interaction Using Inverse Differential Riccati Equation

An optimal robot-environment interaction is designed by transforming an environment model into an optimal control problem. In the optimal control, the inverse differential Riccati equation is introduced as a fixed-end-point closed-loop optimal control over a specific time interval. Then, the environment model, including interaction force is formulated in a state equation, and the optimal trajectory is determined by minimizing a cost function. Position control is proposed, and the stability of the closed-loop system is investigated using the Lyapunov direct method. Finally, theoretical developments are verified through numerical simulation

An Analysis of the Finite Element Method Applied on Dynamic Motion and Maximum Payload Planning of Flexible Manipulators

This paper is concerned with the dynamic motion analysis and the planning of maximum payload path of flexible manipulators. The finite element method was employed for dynamic modelling of the system and the motion of the model was considered as a combination of the rigid displacement and the elastic deformation of each link. Each manipulator link was treated as a finite number of elements and total displacement was derived by means of the shape functions of flexible elements. The problem of maximum payload trajectory planning was formulated as an optimal control problem. An indirect optimal control solution was employed. This method converts an optimality problem to a two-point boundary value problem. The effect of the number of elements on the dynamic motion, optimal trajectory and maximum allowable dynamic payload of the system was studied. Finally, a number of simulations were performed to verify the applicability and capability of the method for the nonlinear dynamic modelling and the control of flexible manipulators

Fault-tolerant neuro adaptive constrained control of wind turbines for power regulation with uncertain wind speed variation

This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with an unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation, and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation, or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust, adaptive, and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme

A Lightweight Universal Gripper with Low Activation Force for Aerial Grasping

Soft robotic grippers have numerous advantages that address challenges in dynamic aerial grasping. Typical multi-fingered soft grippers recently showcased for aerial grasping are highly dependent on the direction of the target object for successful grasping. This study pushes the boundaries of dynamic aerial grasping by developing an omnidirectional system for autonomous aerial manipulation. In particular, the paper investigates the design, fabrication, and experimental verification of a novel, highly integrated, modular, sensor-rich, universal jamming gripper specifically designed for aerial applications. Leveraging recent developments in particle jamming and soft granular materials, the presented gripper produces a substantial holding force while being very lightweight, energy-efficient and only requiring a low activation force. We show that the holding force can be improved by up to 50% by adding an additive to the membrane's silicone mixture. The experiments show that our lightweight gripper can develop up to 15N of holding force with an activation force as low as 2.5N, even without geometric interlocking. Finally, a pick and release task is performed under real-world conditions by mounting the gripper onto a multi-copter. The developed aerial grasping system features many useful properties, such as resilience and robustness to collisions and the inherent passive compliance which decouples the UAV from the environment.Comment: 21 pages, 19 figures; corrected affiliation

A Lightweight Universal Gripper with Low Activation Force for Aerial Grasping

Soft robotic grippers have numerous advantages that address challenges in dynamic aerial grasping. Typical multi-fingered soft grippers recently showcased for aerial grasping are highly dependent on the direction of the target object for successful grasping. This study pushes the boundaries of dynamic aerial grasping by developing an omnidirectional system for autonomous aerial manipulation. In particular, the paper investigates the design, fabrication, and experimental verification of a novel, highly integrated, modular, sensor-rich, universal jamming gripper specifically designed for aerial applications. Leveraging recent developments in particle jamming and soft granular materials, the presented gripper produces a substantial holding force while being very lightweight, energy-efficient and only requiring a low activation force. We show that the holding force can be improved by up to 50% by adding an additive to the membrane’s silicone mixture. The experiments show that our lightweight gripper can develop up to 15N of holding force with an activation force as low as 2.5N, even without geometric interlocking. Finally, a pick and release task is performed under real-world conditions by mounting the gripper onto a multi-copter. The developed aerial grasping system features many useful properties, such as resilience and robustness to collisions and the inherent passive compliance which decouples the UAV from the environment

Adaptive Neural Control for Safe Human-Robot Interaction

This thesis studies safe human-robot interaction utilizing the neural adaptive control design. First, novel tangent and secant barrier Lyapunov functions are constructed to provide stable position and velocity constrained controls, respectively. Then, neural backpropagation and the concept of the inverse differential Riccati equation are utilized to achieve the impedance adaption control for assistive human-robot interaction, and the optimal robot-environment interaction control, respectively. Finally, adaptive neural assist-as-needed control is developed for assistive robotic rehabilitation

Constrained Neural Adaptive PID Control for Robot Manipulators

The problem of designing an analytical gain tuning and stable PID controller for nonlinear robotic systems is a long-lasting open challenge. This problem becomes even more intricate if unknown system dynamics and external disturbances are involved. This paper presents a novel adaptive neural-based control design for a robot with incomplete dynamical modeling and facing disturbances based on a simple structured PID-like control. Radial basis function neural networks are used to estimate uncertainties and to determine PID gains through a direct Lyapunov method. The controller is further augmented to provide constrained behavior during system operation, while stability is guaranteed by using a barrier Lyapunov function. The paper provides proof that all signals in the closed-loop system are bounded while the constraints are not violated. Finally, numerical simulations provide a validation of the effectiveness of the reported theoretical developments

Backstepping Nussbaum gain dynamic surface control for a class of input and state constrained systems with actuator faults

This paper presents a novel constructive control design applicable for uncertain dynamic systems subject to input and state saturations, unknown direction, and actuation faults. The controller is based on the direct Lyapunov method, which by use of the Nussbaum-type function, provides an adaptive control scheme that can handle the effect of unknown control direction. The violation of constraints is avoided by relying on the bounding of the Lyapunov function in the closed-loop system using the barrier Lyapunov function (BLF). The time-varying cotangent-type BLF is constructed, and by introducing a novel flexible technique to generate state constraints, general constraints are formed systematically to relax different initial conditions. Proper input saturation is utilized, and it is shown that under the proposed control all the signals in the closed-loop system are bounded without violating the constraints, in both fault-free and faulty actuation. The performance of the theoretical results is illustrated using numerical simulations. Also, a comparison is made with the results of well-known logarithm BLF and traditional quadratic Lyapunov functions to further evaluate the effectiveness of the proposed controller

Neural network adaptive control design for robot manipulators under velocity constraints

This paper studies the neural adaptive control design for robotic systems with uncertain dynamics under the existence of velocity constraints and input saturation. The control objective is achieved by choosing a control Lyapunov function using joint error variables that are restricted to linear growth and furthermore by introducing a secant type barrier Lyapunov function for constraining the joint rate variables. The former is exploited to bind the forward propagation of the position errors, and the latter is utilized to impose hard bounds on the velocity. Effective input saturation is expressed, and neural networks are employed to tackle the uncertainty problem in the system dynamics. Feasibility conditions are formulated, and the optimal design parameters are obtained by solving the constrained optimization problem. We prove that under the proposed method, semi-global uniform ultimate boundedness of the closed-loop system can be guaranteed. Tracking errors meanwhile converge to small neighborhoods of the origin, and violations of predefined velocity constraints are avoided. Finally, numerical simulations are performed to verify the effectiveness of the theoretical developments