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

Underwater Robots Part II: Existing Solutions and Open Issues

National audienceThis paper constitutes the second part of a general overview of underwater robotics. The first part is titled: Underwater Robots Part I: current systems and problem pose. The works referenced as (Name*, year) have been already cited on the first part of the paper, and the details of these references can be found in the section 7 of the paper titled Underwater Robots Part I: current systems and problem pose. The mathematical notation used in this paper is defined in section 4 of the paper Underwater Robots Part I: current systems and problem pose

Wave-based control of under-actuated flexible structures with strong external disturbing forces

Wave-based control of under-actuated, flexible systems has many advantages over other methods. It considers actuator motion as launching a mechanical wave into the flexible system which it absorbs on its return to the actuator. The launching and absorbing proceed simultaneously. This simple, intuitive idea leads to robust, generic, highly efficient, precise, adaptable controllers, allowing rapid and almost vibrationless re-positioning of the system, using only sensors collocated at the actuator-system interface. It has been very successfully applied to simple systems such as mass-spring strings, systems of Euler-Bernoulli beams, planar mass-spring arrays, and flexible three-dimensional space structures undergoing slewing motion. In common with most other approaches, this work also assumed that, during a change of position, the forces from the environment were negligible in comparison with internal forces and torques. This assumption is not always valid. Strong external forces considerably complicate the flexible control problem, especially when unknown, unexpected or unmodelled. The current work extends the wave-based strategy to systems experiencing significant external disturbing forces, whether enduring or transient. The work also provides further robustness to sensor errors. The strategy has the controller learn about the disturbances and compensate for them, yet without needing new sensors, measurements or models beyond those of standard wave-based control

Design Optimization, Analysis, and Control of Walking Robots

Passive dynamic walking refers to the dynamical behavior of mechanical devices that are able to naturally walk down a shallow slope in a stable manner, without using actuation or sensing of any kind. Such devices can attain motions that are remarkably human-like by purely exploiting their natural dynamics. This suggests that passive dynamic walking machines can be used to model and study human locomotion; however, there are two major limitations: they can be difficult to design, and they cannot walk on level ground or uphill without some kind of actuation. This thesis presents a mechanism design optimization framework that allows the designer to find the best design parameters based on the chosen performance metric(s). The optimization is formulated as a convex problem, where its solutions are globally optimal and can be obtained efficiently. To enable locomotion on level ground and uphill, this thesis studies a robot based on a passive walker: the rimless wheel with an actuated torso. We design and validate two control policies for the robot through the use of scalable methodology based on tools from mathematical analysis, optimization theory, linear algebra, differential equations, and control theory

Modeling and control of impact in mechanical systems: theory and experimental results

International audienceThis paper considers the equations of motion of mechanical systems subject to inequality constraints, which can be obtained by looking for the stationary value of the action integral. Two different methods are used to take into account the inequality constraints in the computation of the stationary value of the action integral: the method of the Valentine variables and the method of the penalty functions. The equations of motion resulting from the application of the method of the Valentine variables, which introduces the concept of " nonsmooth " impacts, constitute the exact model of the constrained mechanical system; such a model is suitable to be employed when the impacting parts of the actual mechanical system are very stiff. The equations of motion resulting from the application of the method of the penalty functions, which introduces the concept of " smooth impacts, " constitute an approximate model of the constrained mechanical system; such a model is suitable to be employed when the impacting parts of the actual mechanical system show some flexibility. Various feedback control laws from the natural outputs and from their time derivatives are studied with reference to both models of impact; the closed-loop systems resulting from the application of the same control law to both models show pretty much the same global asymptotic stability properties. The proposed control laws are only concerned with regulation problems in the presence of possible contacts and impacts among parts of the mechanical system or with the external environment ; the problem of controlling these mechanical systems along time-varying trajectories is not considered in this paper. The effectiveness of the proposed control structure has been tested experimentally with reference to a single-link robot arm, showing a valuable behavior

Running synthesis and control for monopods and bipeds with articulated

Bibliography: p. 179-20

Neural networks impedance control of robots interacting with environments

In this paper, neural networks impedance control is proposed for robot-environment interaction. Iterative learning control is developed to make the robot dynamics follow a given target impedance model. To cope with the problem of unknown robot dynamics, neural networks are employed such that neither the robot structure nor the physical parameters are required for the control design. The stability and performance of the resulted closed-loop system are discussed through rigorous analysis and extensive remarks. The validity and feasibility of the proposed method are verified through simulation studies