The fifth-generation (5G) new radio (NR) standard employs ultra-reliable and low-latency communication (URLLC) to provide real-time wireless interactive capability for the internet of things (IoT) applications. To satisfy the stringent latency and reliability demands of URLLC services, grant-free (GF) transmissions with the K-repetition transmission (K-Rep) have been introduced. However, fading fluctuations can negatively impact signal quality at the base station (BS), leading to an increase in the number of repetitions and raising concerns about interference and energy consumption for IoT user equipment (UE). To overcome these challenges, this paper proposes novel adaptive K-Rep control schemes that employ site diversity reception to enhance signal quality and reduce energy consumption. The performance evaluation demonstrates that the proposed adaptive K-Rep control schemes significantly improve communication reliability and reduce transmission energy consumption compared with the conventional K-Rep scheme, and then satisfy the URLLC requirements while reducing energy consumption.

Dual-motor driving servo systems are widely used in many military and civil fields. Since backlash nonlinearity affects the dynamic performance and steady-state tracking accuracy of these systems, it is necessary to study a control strategy to reduce its adverse effects. We first establish the state-space model of a system. To facilitate the design of the controller, we simplify the model based on the state-space model. Then, we design an adaptive controller combining a projection algorithm with dynamic surface control applied to a dual-motor driving servo system, which we believe to be the first, and analyze its stability. Simulation results show that projection algorithm-based dynamic surface control has smaller tracking error, faster tracking speed, and better robustness and stability than mere dynamic surface control. Finally, the experimental analysis validates the effectiveness of the proposed control algorithm.

This paper is a sequel to [4] in which the system is generalized by including unknown time-varying delays in both states and input. Regarding the controller, the design of adaptive gain is simplified by including only x1 and u whereas full states are used in [4]. Moreover, it is shown that the proposed controller is also applicable to a class of upper triangular nonlinear systems. An example is given for illustration.

Recent development in network technology can realize the control of a remote plant by a digital controller. However, there is a delay caused by data transmission of control inputs and outputs. The delay degrades the control performance without taking it into consideration. In general, it is a difficult problem to identify the delay beforehand. We also assume that the plant's parameters have uncertainty. To solve the problem, we use reinforcement learning to achieve optimal digital control. First, we consider state feedback control. Next, we consider the case where the plant's outputs are observed, and apply reinforcement learning to output feedback control. Finally, we demonstrate by simulation that the proposed control method can search for the optimal gain and that it can adapt to the change of the delay.

For systems with a delay in the input, the predictor method has been often used in state feedback controllers for system stabilization or regulation. In this letter, we show that for a chain of integrators with even an unknown input delay, a much simpler and memoryless controller is a good candidate for system regulation. With an adaptive gain-scaling factor, the proposed state feedback controller can deal with an unknown time-varying delay in the input. An example is given for illustration.

As the use of information technology (IT) is explosively spreading, reducing the power consumption of IT devices such as servers has become an important social challenge. Nevertheless, while the efficiency of the power supply modules integrated into computers has recently seen significant improvements, their overall efficiency generally depends on load rates. This is especially true under low power load conditions, where it is known that efficiency decreases drastically. Recently, power-saving techniques that work by controlling the power module configuration under low power load conditions have been considered. Based on such techniques, further efficiency improvements can be expected by an adaptive efficiency controls which interlocks the real-time data processing load status with the power supply configuration control. In this study, the performance counters built into the processor of a computer are used to predict power load variations and an equation that predicts the power consumption levels is defined. In a server application experiment utilizing prototype computer hardware and regression analysis, it is validated that the equation could precisely predict processor power consumption. The evaluation shows that significant power supply efficiency improvements could be achieved especially for light load condition. The dependency of the efficiency improvement and operation period is investigated and preferable time scale of the adaptive control is proposed.

Disturbance attenuation for a class of time-delay systems is performed by a combined simple adaptive control (SAC) with a new configuration of disturbance observer (DOB). The nominal system results from the Pade approximation, which is in the form of a non-minimum phase LTI system. For the implementation of SAC and DOB, two parallel feedforward compensators (PFC) are designed with the inverses of PD- and PID-controller, respectively. Simulation results show the effectiveness of the proposed controller to compensate the disturbance response and uncertain delay time.

In the present paper, the performance of cooperative relaying networks with adaptive relaying scheme selection is analyzed. Cooperative relaying is a new technique to achieve spatial diversity gain by using neighboring stations. However, when multiple stations transmit simultaneously, the number of interference signals increases. Therefore, the introduction of cooperative relaying in radio communication systems does not always increase the network capacity due to the co-channel interference. Therefore, in order to achieve high spectral efficiency, it is necessary to select cooperative relaying or non-cooperative relaying adaptively. Assuming both centralized and decentralized adaptive controls, the spectrum efficiency is evaluated. The performance under decentralized control is evaluated using a game-theoretic approach. Simulation results show that the introduction of cooperative relaying with centralized control always increases the spectral efficiency. On the other hand, Simulation results also show that, when each source selects a relaying scheme independently and selfishly to maximize its own spectral efficiency, the introduction of the cooperative relaying may reduce the spectral efficiency due to the increase in the number of interference signals.

This letter studies the tracking error in Multi-input Multi-output Feedback Error Learning (MIMO-FEL) system having insufficient excitation. It is shown that the error converges to zero exponentially even if the reference signal lacks the persistently excitation (PE) condition. Furthermore, by making full use of this fast convergence, we estimate the plant parameter while in operation based on frequency response. Simulation results show the effectiveness of the proposed method compared to a conventional approach.

The problem of designing a robust adaptive control for nonlinear systems with uncertain time-varying parameters is addressed. The upper bound of uncertain parameters, considered even in control coefficients, are not required to be known. An adaptive tracking controller is presented and, using the Lyapunov theory, the closed-loop stability and tracking error convergence is shown. In order to improve the performance of the method, a robust mechanism is incorporated into the adaptive controller yielding a robust adaptive algorithm. The proposed controller guarantees the boundedness of all closed-loop signals and robust convergence of tracking error in spite of time-varying parameter uncertainties with unknown bounds. The parametric uncertain systems under consideration describes a wide class of nonlinear circuits and systems. As an application, a novel parametric model is derived for nonlinear Chua's circuit and then, the proposed method is used for its control. The effectiveness of the method is demonstrated by some simulation results.

Various wireless systems are being developed to meet users' needs, and the rapid increase in frequency demand that accompanies the increasing popularity of wireless services means that more effective use of frequency resources is urgently needed. However, existing base stations are making no effort to use frequency resources effectively, and cooperation among wireless system base stations is needed to use frequency resources more effectively. Base stations can cooperate more efficiently if they are able to use multiple channels of many wireless systems simultaneously. We propose an autonomous adaptive base station (AABS) that can switch among various wireless systems the way software defined radio (SDR) base stations do. AABS can autonomously select and use the most suitable wireless system on the basis of user traffic and its hardware resources. Moreover, frequency resources are used effectively because AABS prevents unnecessary radio wave transmission when the number of users in the wireless systems decreases. AABS is also suitable for "multi-link communication" because it can use multiple channels of multiple wireless systems simultaneously. We developed AABS prototype and evaluated its performance. Our experimental and computer simulation results show the performance of AABS and its efficiency.

In this paper, a novel decentralized adaptive neural network controller is proposed for a class of large-scale nonlinear systems with unknown nonlinear, nonaffine subsystems and unknown nonlinear interconnections. The stability of the closed loop system is guaranteed by introducing a robust adaptive bound based on Lyapunov stability analysis. A radial-basis function type neural network is used in the paper. To show the effectiveness of the proposed method, we performed some simulation studies. The results of simulation become very promising.

It is an innovative idea for modern PLL generation to control the bandwidth proportionally to the reference frequency. Recently, a frequency of the operating clock in microprocessors has been required to be changed frequently and widely in order to manage power consumption and throughput. A new compact switched capacitor (SC) filter which has fully flat response has been developed for adaptive biased PLLs. We have also developed a new digital control method for achieving the wider frequency range. The measured performances of the test chip were good enough for the use in the microprocessors.

Consideration of manipulator dynamics and external disturbances in robot control system design can enhance the stability and performance properties of the whole system. In this paper, we present an approach to solve the control problem when the inertia parameters of robot are unknown, and at the same time robot is subjected to external force disturbances. This approach is based on simultaneous estimation of force signal and inertia parameters and utilizing them in the control law. The update laws and the control law are derived based on a single time-varying Lyapunov function, so that the global convergence of the tracking error is ensured. A theorem with a detailed proof is presented to guarantee the global uniform asymptotic stability of the whole system. Some simulations are made for a number of external forces to illustrate the effectiveness of the proposed approach.

This paper presents a method of simple adaptive control (SAC) using neural networks for a class of nonlinear systems with bounded-input bounded-output (BIBO) and bounded nonlinearity. The control input is given by the sum of the output of the simple adaptive controller and the output of the neural network. The neural network is used to compensate for the nonlinearity of the plant dynamics that is not taken into consideration in the usual SAC. The role of the neural network is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems. Furthermore, convergence and stability analysis of the proposed method is performed. Finally, the effectiveness of the proposed method is confirmed through computer simulation.

This paper is devoted to the problem of force sensorless disturbance rejection in robot manipulators. In the proposed approach, the control system uses position sensor signals and estimated values of external forces, instead of force sensor signals. The estimation process is performed via an adaptive force estimator. Then the estimated force vector is utilized to compensate for the force disturbance effect in order to achieve a better trajectory tracking performance. The force estimation is carried out directly using no environment model. Asymptotical stability of the proposed control system is analyzed by the invariant set and Lyapunov direct method establishing an appropriate theorem. Finally, the performance of the proposed control system is verified using numerical simulation.

This paper presents an adaptive control design for the ABR traffic congestion control in ATM networks. Firstly, we consider a control-based mathematical model to the ABR traffic congestion control problem. Then the feedback pole placement control design is applied to the ATM ABR traffic congestion control problem for the case of known delays. Finally, by using the online plant parameter estimation algorithm and modifying the controller parameters adaptively in real time, a method to treat the case of unknown time-varying delays is proposed. Several design modifications are introduced to solve practical control issues such as bounded command rate constraint, output buffer saturation and bounded values to the plant parameter estimation algorithm. Simulations are implemented to verify the proposed control design. It is shown that while considering these practical control issues, the control method satisfies the requirements of fairness to users, network efficiency, unknown time-varying delays, queue length control and good convergence performance at an acceptable computation effort.

A simple adaptive internal model control structure is designed and tested on the real-time temperature control of a process. The design procedure remains same for both minimum and non-minimum phase systems. The effect of the process zeros on the output is compensated by using adaptive finite impulse response filters. This guarantees the stability of the closed-loop.

This paper presents a method to compensate the effect of non-cancellable zeros of the plant on output by incorporating finite impulse response (FIR) filter in the feed-forward path. This FIR filter is designed using the approximate inverse system of the non-cancellable part of numerator polynomial of the plant. The proposed controller guarantees boundedness of all signals from the reference input to the plant output. Computer simulation and real-time control experiment results are given to demonstrate the effectiveness of the proposed method.

The system uses spectral shaping of a supercontinuum source followed by wavelength-to-time mapping to generate ultra wideband RF waveforms with arbitrary modulation. It employs an adaptive computer control to mitigate the non-ideal features inherent in the optical source and in the spectrum modulation process. As proof of concept, ultra-wideband frequency hopped CDMA waveforms are demonstrated.