Controlling soft robots with precision is a challenge due to the difficulty of constructing models that are amenable to model-based control design techniques. Koopman operator theory offers a way to construct explicit dynamical models of soft robots and to control them using established model-based control methods. This approach is data driven, yet yields an explicit control-oriented model rather than just a “black-box” input–output mapping. This work describes a Koopman-based system identification method and its application to model predictive control (MPC) design for soft robots. Three MPC controllers are developed for a pneumatic soft robot arm via the Koopman-based approach, and their performances are evaluated with respect to several real-world trajectory following tasks. In terms of average tracking error, these Koopman-based controllers are more than three times more accurate than a benchmark MPC controller based on a linear state-space model of the same system, demonstrating the utility of the Koopman approach in controlling real soft robots.

Regenerative peripheral nerve interfaces show promise to improve prosthetic rehabilitation in people with upper limb loss.

This paper describes a paradigm for human/automation control sharing in which the automation acts through a motor coupled to a machine's manual control interface. The manual interface becomes a haptic display, continually informing the human about automation actions. While monitoring by feel, users may choose either to conform to the automation or override it and express their own control intentions. This paper's objective is to demonstrate that adding automation through haptic display can be used not only to improve performance on a primary task but also to reduce perceptual demands or free attention for a secondary task. Results are presented from three experiments in which 11 participants completed a lane-following task using a motorized steering wheel on a fixed-base driving simulator. The automation behaved like a copilot, assisting with lane following by applying torques to the steering wheel. Results indicate that haptic assist improves lane following by least 30%, p < .0001, while reducing visual demand by 29%, p < .0001, or improving reaction time in a secondary tone localization task by 18 ms, p = .0009. Potential applications of this research include the design of automation interfaces based on haptics that support human/automation control sharing better than traditional push-button automation interfaces.

Hybrid vehicle fuel economy performance is highly sensitive to the energy management strategy used to regulate power flow among the various energy sources and sinks. Optimal non-causal solutions are easy to determine if the drive cycle is known a priori. It is very challenging to design causal controllers that yield good fuel economy for a range of possible driver behavior. Additional challenges come in the form of constraints on powertrain activity, such as shifting and starting the engine, which are commonly called “drivability” metrics and can adversely affect fuel economy. In this paper, drivability restrictions are included in a shortest path stochastic dynamic programming (SP-SDP) formulation of the real-time energy management problem for a prototype vehicle, where the drive cycle is modeled as a stationary, finite-state Markov chain. When the SP-SDP controllers are evaluated with a high-fidelity vehicle simulator over standard government drive cycles, and compared to a baseline industrial controller, they are shown to improve fuel economy more than 11% for equivalent levels of drivability. In addition, the explicit tradeoff between fuel economy and drivability is quantified for the SP-SDP controllers.

Controlling soft continuum manipulator arms is difficult due to their infinite degrees of freedom, nonlinear material properties, and large deflections under loading. This letter presents a data-driven approach to identifying soft manipulator models that enables consistent control under variable loading conditions. This is accomplished by incorporating loads into a linear Koopman operator model as states and estimating their values online via an observer within the control loop. Using this approach, autonomous control of a pneumatically actuated soft continuum manipulator is achieved. In several trajectory following experiments, this controller is shown to be more accurate and precise than controllers based on models that do not explicitly account for loading. The manipulator also successfully performs pick and place of objects with unknown mass, demonstrating the efficacy of this approach in executing real-world manipulation tasks.

Each year, approximately 185,000 Americans suffer the devastating loss of a limb. The effects of upper limb amputations are profound because a person's hands are tools for everyday functioning, expressive communication, and other uniquely human attributes. Despite the advancements in prosthetic technology, current upper limb prostheses are still limited in terms of complex motor control and sensory feedback. Sensory feedback is critical to restoring full functionality to amputated patients because it would relieve the cognitive burden of relying solely on visual input to monitor motor commands and provide tremendous psychological benefits. This article reviews the latest innovations in sensory feedback and argues in favor of peripheral nerve interfaces. First, the authors examine the structure of the peripheral nerve and its importance in the development of a sensory interface. Second, the authors discuss advancements in targeted muscle reinnervation and direct neural stimulation by means of intraneural electrodes. The authors then explore the future of prosthetic sensory feedback using innovative technologies for neural signaling, specifically, the sensory regenerative peripheral nerve interface and optogenetics. These breakthroughs pave the way for the development of a prosthetic limb with the ability to feel.