Adaptive manipulator control: A case study

The author's previous work (1986, 1987) utilized the particular structure of manipulator dynamics to develop a simple, globally convergent adaptive controller for manipulator trajectory control problems. After summarizing the basic algorithm, they demonstrate the approach on a high-speed two-degree-of-freedom semi-direct-drive robot. They show that the dynamic parameters of the manipulator, assumed to be initially unknown, can be estimated within the first half second of a typical run, and that accordingly, the manipulator trajectory can be precisely controlled. These experimental results demonstrate that the adaptive controller enjoys essentially the same level of robustness to unmodeled dynamics as a PD (proportional and differential) controller, yet achieves much better tracking accuracy than either PD or computed-torque schemes. Its superior performance for high-speed operations, in the presence of parametric and nonparametric uncertainties, and its relative computational simplicity, make it an attractive option both for addressing complex industrial tasks, and for simplifying high-level programming of more standard operations     

CPG-based control of a turtle-like underwater vehicle

This paper presents biologically inspired control strategies for an autonomous underwater vehicle (AUV) propelled by flapping fins that resemble the paddle-like forelimbs of a sea turtle. Our proposed framework exploits limit cycle oscillators and diffusive couplings, thereby constructing coupled nonlinear oscillators, similar to the central pattern generators (CPGs) in animal spinal cords. This paper first presents rigorous stability analyses and experimental results of CPG-based control methods with and without actuator feedback to the CPG. In these methods, the CPG module generates synchronized oscillation patterns, which are sent to position-servoed flapping fin actuators as a reference input. In order to overcome the limitation of the open-loop CPG that the synchronization is occurring only between the reference signals, this paper introduces a new single-layered CPG method, where the CPG and the physical layers are combined as a single layer, to ensure the synchronization of the physical actuators in the presence of external disturbances. The key idea is to replace nonlinear oscillators in the conventional CPG models with physical actuators that oscillate due to nonlinear state feedback of the actuator states. Using contraction theory, a relatively new nonlinear stability tool, we show that coupled nonlinear oscillators globally synchronize to a specific pattern that can be stereotyped by an outer-loop controller. Results of experimentation with a turtle-like AUV show the feasibility of the proposed control laws.     

Contraction analysis of time-delayed communications and group cooperation

We study stability of interacting nonlinear systems with time-delayed communications, using contraction theory and a simplified wave variable design inspired by robotic teleoperation. We show that contraction is preserved through specific time-delayed feedback communications, and that this property is independent of the values of the delays. The approach is then applied to group cooperation with linear protocols, where it is shown that synchronization can be made robust to arbitrary time delays.     

A theoretical study of different leader roles in networks

We study synchronization conditions for distributed dynamic networks with different types of leaders. The role of a "power" leader specifying a desired global state trajectory through local interactions has long been recognized and modeled. This note introduces the complementary notion of a "knowledge" leader holding information on the target dynamics, which is propagated to the entire network through local adaptation mechanisms. Different types of leaders can coexist in the same network. For instance, in a network of locally connected oscillators, the power leader may set the global phase while the knowledge leader may set the global frequency and the global amplitude. Knowledge-based leader-followers networks have many analogs in biology, e.g., in evolutionary processes and disease propagation.     

Wavelet Network for Semi-Active Control

This paper proposes a wavelet neurocontroller capable of self-adaptation and self-organization for uncertain systems controlled with semiactive devices that are ideal candidates for control of large-scale civil structures. A condition on the sliding surface for cantilever-like structures is defined. The issue of applicability of the control solution to large-scale civil structures is made the central theme throughout the text, as this topic has not been extensively discussed in the literature. Stability and convergence of the proposed neurocontroller are assessed through various numerical simulations for harmonic, earthquake, and wind excitations. The simulations consist of semiactive dampers installed as a replacement for the current viscous damping system in an existing structure. The controller uses only localized measurements. Results show that the controller is stable for both active and semiactive control using limited measurements and that it is capable of outperforming passive control strategies for earthquake and wind loads. In the case of wind loads, the neurocontroller is found to also outperform a linear quadratic regulator (LQR) controller designed using full knowledge of the states and system dynamics.     

Competition through selective inhibitory synchrony

Models of cortical neuronal circuits commonly depend on inhibitory feedback to control gain, provide signal normalization, and selectively amplify signals using winner-take-all (WTA) dynamics. Such models generally assume that excitatory and inhibitory neurons are able to interact easily because their axons and dendrites are colocalized in the same small volume. However, quantitative neuroanatomical studies of the dimensions of axonal and dendritic trees of neurons in the neocortex show that this colocalization assumption is not valid. In this letter, we describe a simple modification to the WTA circuit design that permits the effects of distributed inhibitory neurons to be coupled through synchronization, and so allows a single WTA to be distributed widely in cortical space, well beyond the arborization of any single inhibitory neuron and even across different cortical areas. We prove by nonlinear contraction analysis and demonstrate by simulation that distributed WTA subsystems combined by such inhibitory synchrony are inherently stable. We show analytically that synchronization is substantially faster than winner selection. This circuit mechanism allows networks of independent WTAs to fully or partially compete with other.     

Robust robot control with bounded input torques

A new scheme is presented to achieve effective on-line tracking control of robot manipulators in the presence of model uncertainty and torque limitations. Pointwise optimization techniques are incorporated into a “slow” feedback loop in order to generate feasible desired trajectories, optimally close to the original desired trajectories. A suction controller is used in the “fast” main feedback loop, allowing to combine robust tracking with simple controller design. Simulations show very significant performance improvement over standard techniques. The algorithm is computationally cheap and can easily be implemented on-line.     

A Contraction Theory Approach to Stochastic Incremental Stability

We investigate the incremental stability properties of Ito stochastic dynamical systems. Specifically, we derive a stochastic version of nonlinear contraction theory that provides a bound on the mean square distance between any two trajectories of a stochastically contracting system. This bound can be expressed as a function of the noise intensity and the contraction rate of the noise-free system. We illustrate these results in the contexts of nonlinear observers design and stochastic synchronization.     

Implementation of VSS control to robotic manipulators-smoothingmodification

The authors focus on the implementation of a variable structure systems (VSS) controller with smoothing laws in the design of effective tracking control for multi-input, multi-output robotic arms. The controller is realized by selecting powerful smoothing methods, such as balance conditions or their simplification, to reduce or remove undesirable chattering while keeping the robust characteristic that rejects system uncertainties. Giving careful consideration to actual system constraints, a design principle for selecting different smoothing methods is obtained and confirmed by experimental results     

Hamiltonian adaptive control of spacecraft

A new approach to the accurate attitude tracking control of rigid spacecraft handling large loads of unknown mass properties is proposed. The method, based on the construction of a physically motivated Lyapunov-like function, is inspired by the adaptive robot control algorithm of J.J.E. Slotine and W. Li (Int. J. Robot. Res., vol.6, no.3, 1987), and presents similar advantages over techniques based on inverse dynamics in terms of simplicity, easier approach to robustness issues, and adaptive capabilities. The approach is illustrated in simulations