This paper is concerned with improving performance of a state estimationproblem over a network in which a send-on-delta (SOD) transmission method is used. TheSOD method requires that a sensor node transmit data to the estimator node only if itsmeasurement value changes more than a given specified δ value. This method has beenexplored and applied by researchers because of its efficiency in the network bandwidthimprovement. However, when this method is used, it is not ensured that the estimator nodereceives data from the sensor nodes regularly at every estimation period. Therefore, wepropose a method to reduce estimation error in case of no sensor data reception. When theestimator node does not receive data from the sensor node, the sensor value is known to bein a (−δ_i , δ_i ) interval from the last transmitted sensor value. This implicit information hasbeen used to improve estimation performance in previous studies. The main contribution ofthis paper is to propose an algorithm, where the sensor value interval is reduced to(−δ_i / 2, δ_i / 2) in certain situations. Thus, the proposed algorithm improves the overallestimation performance without any changes in the send-on-delta algorithms of the sensornodes. Through numerical simulations, we demonstrate the feasibility and the usefulness ofthe proposed method.

This paper is concerned with a networked estimation problem in which sensor data are transmitted over the network. In the event-based sampling scheme known as level-crossing or send-on-delta (SOD), sensor data are transmitted to the estimator node if the difference between the current sensor value and the last transmitted one is greater than a given threshold. Event-based sampling has been shown to be more efficient than the time-triggered one in some situations, especially in network bandwidth improvement. However, it cannot detect packet dropout situations because data transmission and reception do not use a periodical time-stamp mechanism as found in time-triggered sampling systems. Motivated by this issue, we propose a modified event-based sampling scheme called modified SOD in which sensor data are sent when either the change of sensor output exceeds a given threshold or the time elapses more than a given interval. Through simulation results, we show that the proposed modified SOD sampling significantly improves estimation performance when packet dropouts happen.

Collaborative robots (or cobots) are robots that are capable of safely operating in a shared environment or interacting with humans. In recent years, cobots have become increasingly common. Compliant actuators are critical in the design of cobots. In real applications, this type of actuation system may be able to reduce the amount of damage caused by an unanticipated collision. As a result, elastic joints are expected to outperform stiff joints in complex situations. In this work, the control of a 2-DOF robot arm with elastic actuators is addressed by proposing a two-loop adaptive controller. For the outer control loop, an adaptive sliding mode controller (ASMC) is adopted to deal with uncertainties and disturbance on the load side of the robot arm. For the inner loops, model reference adaptive controllers (MRAC) are utilised to handle the uncertainties on the motor side of the robot arm. To show the effectiveness of the proposed controller, extensive simulation experiments and a comparison with the conventional sliding mode controller (SMC) are carried out. As a result, the ASMC has a 50.35% lower average RMS error than the SMC controller, and a shorter settling time (5% criterion) (0.44 s compared to 2.11 s).

In this paper, a passivity-based control combined with sliding mode control for a DC-DC boost power converter is proposed. Moreover, a passivity-based control for a DC-DC boost power converter is also proposed. Using a co-ordinate transformation of state variables and control input, a DC-DC boost power converter is passive. A new plant is zero-state observable and the equilibrium point at origin of this plant is asymptotically stable. Then, a passivity-based control is applied to this plant such that the capacitor voltage is equal to the desired voltage. Additionally, the sliding mode control law is chosen such that the derivative of Lyapunov function is negative semidefinite. Finally, a passivity-based control combined with sliding mode control law is applied to this plant such that the capacitor voltage is equal to the desired voltage. The simulation results of the passivity-based control, the sliding mode control and the passivity-based control combined with sliding mode control demonstrate the effectiveness and show that the capacitor voltage is kept at the desired voltage when the desired voltage, the input voltage E and the load resistor R are changed. The results show that compared with the passivity-based control, the passivity-based control combined with sliding mode control has better performance such as shorter settling time, 8.5 ms when R changes and it has smaller steady-state error, which is indicated by the value of integral absolute error (IAE), 0.0679 when the desired voltage changes. The paper has limitations such as the assumed circuit parameters.

This study explores an application of robust nonlinear control to an underactuated inverted pendulum system (UIPS), a type of underactuated mechanical system, by first transforming the perturbed system into a form of a port-controlled Hamiltonian system (PCH), then utilizing the Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) methodology to achieve the stabilization of an unperturbed closed-loop PCH system with an assigned energy function that qualifies as a Lyapunov candidate at the unstable equilibrium point. From there, the problem of robustification of IDA-PBC for a perturbed closed-loop PCH system with the state-dependent input matrix of the UIPS, subject to constant matched disturbances, is addressed by adding an outer-loop controller with an additional state. This results in a new system that preserves the framework of a PCH system, rejects the disturbance, and has a new energy function that again serves as a Lyapunov function at the desired equilibrium point. This proposed methodology is called integral IDA-PBC (iIDA-PBC). The effectiveness and applicability of the proposed method are thoroughly assessed through numerical simulations and experimental validation on the UIPS. The results demonstrate the method's proficiency in handling the system’s constant matched disturbances and model inaccuracies, underscoring the potential of iIDA-PBC for broader applications in systems facing similar control challenges.

In this paper, a comparative study of nonlinear control and passivity-based control using neural networks for a bicycle robot is proposed. Bicycle robot is a nonlinear, multi-input multi-output system. Two inputs of a bicycle robot are the steering torque and kinetic energy. Its two outputs are the steering angle and the rolling angle. The control problem is that the steering angle and the rolling angle track a value of zero, and the velocity of the steering angle and velocity of the rolling angle track a value of zero to make a bicycle robot stabilize at its vertical balance. Firstly, an input-output linearization control law decouples the bicycle robot into single-input single-output systems. This plant is passive and zero-state observable. Secondly, the passivity-based control law is applied to each single-input single-output system. Finally, the neural network, which performs the passivity-based control, is applied to each single-input single-output system in order that the bicycle robot keeps its vertical balance. A training algorithm using the steepest descend method is proposed. The simulation results of the passivity-based control and the results of the passivity-based control using neural networks show that the bicycle robot keeps its vertical balance. The settling time of the steering angle and the rolling angle of the passivity-based control using a neural network, 1.8s, is shorter than that of the passivity-based control. There is a comparison with the passivity-based control combined with sliding mode control for a bicycle robot.