Yet Another Tutorial of Disturbance Observer: Robust Stabilization and Recovery of Nominal Performance

This paper presents a tutorial-style review on the recent results about the disturbance observer (DOB) in view of robust stabilization and recovery of the nominal performance. The analysis is based on the case when the bandwidth of Q-filter is large, and it is explained in a pedagogical manner that, even in the presence of plant uncertainties and disturbances, the behavior of real uncertain plant can be made almost similar to that of disturbance-free nominal system both in the transient and in the steady-state. The conventional DOB is interpreted in a new perspective, and its restrictions and extensions are discussed

Addendum for "A Study of Disturbance Observers with Unknown Relative Degree"

[Preprint] The paper "A Study of Disturbance Observers with Unknown Relative Degree" [1] by the authors could not include the proofs for Theorem 5 and Theorem 6 due to the page limit. We provide them in this supplementary document, and an example is included with simulation results

Recovering Nominal Tracking Performance In An Asymptotic Sense For Uncertain Linear Systems

In this paper, we consider the problem of recovering a (predefined) nominal output trajectory in the presence of model uncertainty and external disturbance. In particular, whereas the nominal performance recovery (NPR) has been studied in an approximate fashion in the literature, we extend the notion of the NPR in an asymptotic sense from the perspective of the internal model principle: that is, as long as the disturbance and reference signals are generated by an exogenous system, the actual output not only is kept close to the nominal trajectory as much as desired but also asymptotically converges to the nominal one as time elapses. It is shown via the singular perturbation theory that the asymptotic NPR can be achieved for uncertain minimum-phase systems under arbitrarily large (but bounded) model uncertainty. A disturbance observer (DOB) approach is employed in the controller design, with the internal model embedded into the so-called Q-filter, which is a key component of the DOB. Simulation results for mechanical positioning systems illustrate that the asymptotic NPR can enhance robust performance of control systems

Cooperative Control Of Heterogeneous Multi-Agent Systems In A Sampled-Data Setting

This paper deals with a cooperative control problem of networked heterogeneous input-output passivity-short (PS) multi-agent systems in a sampled-data setting. The dynamics of each system are continuous, whereas the exchange of information on a communication network is operated in a discrete-time manner. The analysis and cooperative control design are transformed into representative forms of discretized systems using a zero-order holder and an ideal sampler. Based on the concept of PS, a design of a distributed static output feedback control for achieving output consensus is proposed. Compared with the concept of passivity, it is shown that PS extends the systems under consideration to higher relative degree and/or non-minimum phase. This extension allows the design of a distributed controller by quantifying the impact of each system in networked operation. Furthermore, properties of PS are discussed both in the continuous and discrete-time domain, and conditions for preserving PS through discretization are presented

On Robust Stability Of Disturbance Observer For Sampled-Data Systems Under Fast Sampling: An Almost Necessary And Sufficient Condition

Despite increased interest on discrete-time disturbance observer (DOB) in both theory and application, study of robust stabilization via the DOB does not seem to be mature. This is because most of the existing studies on robust stability are based on the small-gain theorem, so that the results are just sufficient conditions and the amount of uncertainty that the DOB-based control system can tolerate is conservative. In this paper, motivated by a recent work on the continuous-time case, we present an almost necessary and sufficient condition for robust stability of the sampled-data system controlled by a discrete-time DOB under fast sampling. In particular, our study clarifies the phenomenon that the sampling process can hamper stability of the DOB-controlled systems by generating additional (and possibly nonminimum-phase) zeros, and explains why the blind discretization of the continuous-time DOB controller using fast sampling may fail. For the robust stabilization against both these extra zeros and plant uncertainty, we also propose a new design methodology for the nominal model and the Q-filter. It turns out that arbitrarily large uncertainty can be compensated by appropriately designing the Q-filters and by fast sampling. A benchmark problem is revisited to illustrate the validity of the proposed analysis and design method

Embedding Internal Model In Disturbance Observer With Robust Stability

The disturbance observer has been widely employed in applications due to its powerful ability for disturbance rejection and robustness under plant uncertainties. However, it rejects the disturbance approximately rather than exactly since it is usually designed without considering structural properties of disturbance. In order to improve the disturbance rejection performance, we propose a design method to embed the internal model of disturbance into the disturbance observer structure. Furthermore, a systematic design procedure is proposed so that one can always design the disturbance observer to guarantee robust stability of the closed-loop system even though uncertain parameters of the plant belong to an arbitrarily large (but bounded) set

Reduced Graphene Oxide-Based Artificial Synapse Yarns for Wearable Textile Device Applications

A brain-inspired neuromorphic system is a promising computing concept that processes information at low power. Such systems can be applied to wearable devices in which low power consumption is important. Solid-state devices that have been used for neuromorphic device applications are not suitable for wearable applications that require high flexibility. Here, two-terminal memristor-based artificial synapses are proposed that are simply constructed by crossing two yarns coated with reduced graphene oxide (RGO) by electrochemical deposition. The artificial synapses mimic several important synaptic functions of biological synapses, including excitatory postsynaptic current, paired-pulse facilitation, and a transition from short-term plasticity to long-term plasticity. The artificial synapses can be operated stably without degradation during mechanical bending. By implementing a 2 x 2 cross-point array using RGO-coated yarns, the possibility of integrating artificial synapses for wearable neuromorphic systems is demonstrated. The yarn-based artificial synapses can be a key component of future neuromorphic wearable systems.11sciescopu

Dynamic Modeling And Control Of Hopping Robot In Planar Space

The paper presents two-mass inverted pendulum (TMIP) model and its control scheme for hopping robot. Unlike the conventional spring-loaded inverted pendulum (SLIP) model, the proposed TMIP model is able to provide the functions of energy storing and releasing by using a linear actuator. Also it becomes more accurate comparing to the conventional SLIP model by taking the foot mass into consideration. Furthermore how to determine both takeoff angle and velocity for hopping is analytically suggested to accomplish the desired stride and height of hopping robot. The control method for the TMIP model is also presented in the paper. Finally, the effectiveness of the proposed model and control scheme is verified through the simulation