Stochastic resonance is a representative phenomenon in which the degree of synchronization with a weak input signal is enhanced using additive stochastic noise. In systems with multiple chaotic attractors, the chaos-chaos intermittent behavior in attractor-merging bifurcation induces chaotic resonance, which is similar to the stochastic resonance and has high sensitivity. However, controlling chaotic resonance is difficult because it requires adjusting the internal parameters from the outside. The reduced-region-of-orbit (RRO) method, which controls the attractor-merging bifurcation using an external feedback signal, is employed to overcome this issue. However, the lower perturbation of the feedback signal requires further improvement for engineering applications. This study proposed an RRO method with more sophisticated and less perturbed feedback signals, called the double-Gaussian-filtered RRO (DG-RRO) method. The inverse sign of the map function and double Gaussian filters were used to improve the local specification, i.e., the concentration around the local maximum/minimum in the feedback signals, called the DG-RRO feedback signals. Owing to their fine local specification, these signals achieved the attractor-merging bifurcation with significantly smaller feedback perturbation than that in the conventional RRO method. Consequently, chaotic resonance was induced through weak feedback perturbation. It exhibited greater synchronization against weak input signals than that induced by the conventional RRO feedback signal and sustained the same level of response frequency range as that of the conventional RRO method. These advantages may pave the way for utilizing chaotic resonance in engineering scenarios where the stochastic resonance has been applied.

In cyber-physical systems (CPSs) that interact between physical and information components, there are many sensors that are connected through a communication network. In such cases, the reduction of communication costs is important. Event-triggered control that the control input is updated only when the measured value is widely changed is well known as one of the control methods of CPSs. In this paper, we propose a design method of output feedback controllers with decentralized event-triggering mechanisms, where the notion of uniformly ultimate boundedness is utilized as a control specification. Using this notion, we can guarantee that the state stays within a certain set containing the origin after a certain time, which depends on the initial state. As a result, the number of times that the event occurs can be decreased. First, the design problem is formulated. Next, this problem is reduced to a BMI (bilinear matrix inequality) optimization problem, which can be solved by solving multiple LMI (linear matrix inequality) optimization problems. Finally, the effectiveness of the proposed method is presented by a numerical example.

Compressed sensing is a rapidly growing research field in signal and image processing, machine learning, statistics, and systems control. In this survey paper, we provide a review of the theoretical foundations of compressed sensing and present state-of-the-art algorithms for solving the corresponding optimization problems. Additionally, we discuss several practical applications of compressed sensing, such as group testing, sparse system identification, and sparse feedback gain design, and demonstrate their effectiveness through Python programs. This survey paper aims to contribute to the advancement of compressed sensing research and its practical applications in various scientific disciplines.

To maintain blockchain-based services with ensuring its security, it is an important issue how to decide a mining reward so that the number of miners participating in the mining increases. We propose a dynamical model of decision-making for miners using an evolutionary game approach and analyze the stability of equilibrium points of the proposed model. The proposed model is described by the 1st-order differential equation. So, it is simple but its theoretical analysis gives an insight into the characteristics of the decision-making. Through the analysis of the equilibrium points, we show the transcritical bifurcations and hysteresis phenomena of the equilibrium points. We also design a controller that determines the mining reward based on the number of participating miners to stabilize the state where all miners participate in the mining. Numerical simulation shows that there is a trade-off in the choice of the design parameters.

In video distribution services such as video streaming, the providers must satisfy the various quality demands of the users. One of the human-centric indexes used to assess video quality is the quality of experience (QoE). In video streaming, the video bitrate, video freezing time, and video bitrate switching are significant determiners of QoE. To provide high-quality video streaming services, adaptive streaming using the Moving Picture Experts Group dynamic adaptive streaming over Hypertext Transfer Protocol (MPEG-DASH) is widely utilized. One of the conventional bitrate selection methods for MPEG-DASH selects the bitrate such that the amount of buffered data in the playback buffer, i.e., the playback buffer level, can be maintained at a constant value. This method can avoid buffer overflow and video freezing based on feedback control; however, this method induces high-frequency video bitrate switching, which can degrade QoE. To overcome this issue, this paper proposes a bitrate selection method in an adaptive video steaming for MPEG-DASH to improve the QoE by minimizing the bitrate fluctuation. To this end, the proposed method does not change the bitrate if the playback buffer level is not around its upper or lower limit, corresponding to the full or empty state of the playback buffer, respectively. In particular, to avoid buffer overflow and video freezing, the proposed method selects the bitrate based on proportional-derivative (PD) control to maintain the playback buffer level at a target level, which corresponds to an upper or lower threshold of the playback buffer level. Simulations confirm that, the proposed method offers better QoE than the conventional method for users with various preferences.

This paper considers a design method of a discrete-time adaptive output feedback control system with a feedforward input based on almost strict positive realness (ASPR-ness). The proposed scheme utilizes the property of ASPR of the controlled plant, and the reference signal is used as feedforward input. The parallel feedforward compensator (PFC) which renders an ASPR augmented controlled plant is also investigated. Besides, it is shown that the output of original plant can track reference signal perfectly without any steady state error. The effectiveness of the proposed scheme is confirmed through a pilot-scale temperature control system.

This paper considers an optimal stabilization problem of quantitative discrete event systems (DESs) under the influence of disturbances. We model a DES by a deterministic weighted automaton. The control cost is concerned with the sum of the weights along the generated trajectories reaching the target state. The region of weak attraction is the set of states of the system such that all trajectories starting from them can be controlled to reach a specified set of target states and stay there indefinitely. An optimal stabilizing controller is a controller that drives the states in this region to the set of target states with minimum control cost and keeps them there. We consider two control objectives: to minimize the worst-case control cost (1) subject to all enabled trajectories and (2) subject to the enabled trajectories starting by controllable events. Moreover, we consider the disturbances which are uncontrollable events that rarely occur in the real system but may degrade the control performance when they occur. We propose a linearithmic time algorithm for the synthesis of an optimal stabilizing controller which is robust to disturbances.

In this paper, we formulate an optimal stabilization problem of quantitative discrete event systems (DESs) under partial observation. A DES under partial observation is a system where its behaviors cannot be completely observed by a supervisor. In our framework, the supervisor observes not only masked events but also masked states. Our problem is then to synthesize a supervisor that drives the DES to a given target state with the minimum cost based on the detected sequences of masked events and states. We propose an algorithm for deciding the existence of an optimal stabilizing supervisor, and compute it if it exists.

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.

A regulated charge pump (CP) with an extended range of load current is presented. A power-efficient adaptive feedback controller is adopted. Verified by a 0.18µm CMOS technology with a power supply of 3.3V, the measured output voltage of the CP is regulated above 5V when the load current is varied from 2.5mA to 50mA. The measured power efficiency spans from 81.7% at lighter load to 75.2% when load current is 50mA. The measured output ripples are small and below 24mV.

We consider a chain of integrators system that has an uncertain delay in the input. Also, there is a measurement noise in the feedback channel that only noisy output is available. We develop a new output feedback control scheme along with amplification such that the ultimate bounds of all states and output of the controlled system can be made arbitrarily small. We note that the condition imposed on the sensor noise is quite general over the existing results such that the sensor noise is uncertain and is only required to be bounded by a known bound. The benefit of our control method is shown via an example.

This manuscript presents a simple scheme to improve the performance of a feedback control system that uses power line channels for its feedback loop. The noise and attenuation of power lines, and thus the signal to noise ratio, are known to be cyclostationary. Such cyclic features in the channel allow us to predict virtually error free transmission instants as well as instants of high probability of errors. This paper introduces and evaluates the effectiveness of a packet transmission scheduling that collaborates with a predictive control scheme adapted to this cyclostationary environment. In other words, we explore the cooperation between the physical and application layers of the system in order to achieve an overall optimization. To rate the control quality of the system we evaluate its stability as well as its ability to follow control commands accurately. We compare a scheme of increased packet rate against our proposed scheme which emulates a high packet rate with the use of predictive control. Through this comparison, we verify the effectiveness of the proposed scheme to improve the control quality of the system, even under low signal to noise ratio conditions in the cyclostationary channel.

Sensor noise prevents the exact measurement of output, which makes it difficult to guarantee the ultimate bound of the actual output and states, which is smaller than the sensor noise amplitude. Even worse, the time-varying delay in the input does not guarantee the boundedness of the actual output and states under sensor noise. In this letter, our considered system is a chain of integrators in which time-varying delay exists in the input and there is an additive form of sensor noise in the output measurement. To guarantee the arbitrarily small ultimate bound of the actual output and states, we newly propose an adaptive output feedback controller whose gain is tuned on-line. The merits of our control method over the existing results are clearly shown in the example.

AC conversion has a huge variety of applications and so there are many ongoing research topics as in every type of power electronic conversion. New semiconductors allow the increase of the switching frequency fact that brings a whole new prospective improvement in converter's operation. Many other possible nonlinear operation regimes, including period doubling and chaotic oscillations, appear besides the conventional steady state operation. In this work, a nonlinear discrete-time model of an AC/AC buck type converter is proposed. A discrete time iterative map is derived to highlight the sensitive switching dynamics. The model is able to observe fast scale phenomena and short transient effects. It offers more information compared to other methods such as the averaging ones. According to Electro-Magnetic Compatibility (EMC) regulations, low wide-band noise is more acceptable than the high narrow-band, therefore the goal of this work is to spread the harmonic noise into a wide frequency spectrum which has lower amplitudes compared to the conventional comb-like spectrum with distinctive amplitudes at switching frequency multiples. Through the numerical and experimental consideration the converter can operate in a chaotic motion and the advantages of the performance improvement are also discussed.

We consider an output feedback control problem of a chain of integrators under sensor noise. The sensor noise enters the output feedback channel in an additive form. A similar problem has been addressed most recently in [9], but their result has been developed only under AC sensor noise. We generalize the result of [9] by allowing the sensor noise to include both AC and DC components. With our new output feedback controller, we show that the ultimate bounds of all states can be made arbitrarily small. We show the generality of our result over [9] via an example.

In this letter, we consider the global exponential stabilization problem by output feedback for a class of nonlinear systems. Along with a newly proposed matrix inequality condition, the proposed control method has improved flexibility in dealing with nonlinearity, over the existing methods. Analysis and examples are given to illustrate the improved features of our control method.

This paper discusses the reduction of the amount of transmitted information for the efficient use of frequency resources in wireless feedback control systems, and clarify the effect of the reduction of the amount of transmitted information. As a typical example of the underactuated controlled object, a rotary inverted pendulum is considered. We propose a reduction method for state information fed back from the controller to the controlled object. It estimates angle or velocity state from the previous state. In addition, we propose a reduction method that temporally omits less important control information and state information. Numerical examples clarify the effect of the reduction methods on the control quality. And we show that the reduction methods achieve large reduction of the amount of transmitted information with small disadvantage of the control quality.

In this letter, we consider a control problem of a chain of integrators where there is an uncertain delay in the input and sensor noise. This is an output feedback control result over [10] in which a state feedback control is suggested. The several generalized features are: i) output feedback control is developed instead of full state feedback control, ii) uncertain delay in the input is allowed, iii) all states are derived to be arbitrarily small under uncertain sensor noise.

This paper discusses a control system that employs a power line to transfer signals to control the motion of a single machine, and explores the influence of packet losses on the quality of the control. As an example of a controlled system, a controller with a rotary inverted pendulum as a controlled object, is considered. The feedback loop in between is the power line. The control performance is evaluated in the power line cyclostationary noise environment and compared against the performance in a stationary noise environment. As a result, it is confirmed that the power line and its cyclostationary noise features present an advantage against transmission in a channel with stationary noise.

The optimization of nonlinear feedback fuzzy system using the product-sum-gravity method is described in this paper. The fuzzy control discussed here is the nonlinear feedback control in which the feedback laws are determined by IF-THEN type fuzzy production rules through product-sum-gravity method. To prove existence of optimal control, we applied compactness of a set of membership functions in L2 space and continuity of the approximate reasoning, and prepared some propositions concerning product-sum-gravity method. By considering fuzzy optimal control problems as problems of finding the minimum (maximum) value of the integral cost (benefit) function on an appropriate set of membership functions, the existence of fuzzy optimal control is shown.