Issues of Designing a Model Adaptive Controller Without a State Observer

It can be challenging to develop a controller using conventional techniques for a plant with a linear or nonlinear dynamical system or model uncertainty. Model adaptive control is a new alternative to classical control techniques and a simple way to update controller parameters. Because model reference adaptive control is unable to anticipate the state in real time if the state observer is not designed with, we will review some of the most major disadvantages of the most commonly used design techniques without state observer in this work.

The Right Lateral Prefrontal Cortex Impacts Control Perception as a Function of Probabilistic Stimulus Processing

Being able to control inner and environmental states is a basic need of living creatures. Control perception (CP) itself may be neurally computed as the subjective ratio of outcome probabilities given the presence and the absence of behavior. If behavior increases the perceived probability of a given outcome, action-outcome contingency is met, and CP may emerge. Nonetheless, in regard of this model, not much is known on how the brain processes CP from these information. This study uses low-intensity transcranial focused ultrasound neuromodulation in a randomized-controlled doubleblind cross-over design to investigate the impact of the right inferior frontal gyrus on this process. Fourty healthy participants visited the laboratory twice (once in a sham, once in a neuromodulation condition) and rated their control perception regarding a classical control illusion task. EEG alpha and theta power density were analyzed in a hierarchical single trial based mixed modeling approach. Results indicate that the right lateral PFC modulates action-outcome learning by providing stochastic information about the situation with increased alpha responses during low control situations (in which the ratio of probabilities is zero). Furthermore, this alpha response was found to modulate mid-frontal theta by altering its relationship with self-reported effort and worrying. These data provide evidencefor right lateral PFC mediated probabilistic stimulus processing during the emergence of CP.

Centralized Control System of the Flotation Department: Ross-Eshby Principle

The aim of the study is to scientifically substantiate the need to use the axiomatic approach when choosing and studying the processes of centralized dispatching control of technological parameters for a complex of flotation machines. During the research, the au-thors performed: comparison of a real object and its properties with the classical control axioms of system analysis; substantiated the use of a hierarchical structure of interrelated technological parameters and their application for situational management. Methods of classical control axioms of system analysis were used for the research. The authors formu-lated the criteria for situational management, the concept of visualization in the dispatching system. The results obtained allow us to assert that the implementation of the Ross-Ashby principle significantly increases the controllability of technological processes with a large number of units and measured parameters.

A NEW EVOLUTIONARY METHOD TO PARAMETERS AND ORDERS IDENTIFICATION AND SYNCHRONIZATION OF CHAOTIC FRACTIONAL-ORDER SYSTEMS

System identification is an important task in the control theory. Classical control theory is usually known for integer-order processes. Nowadays real processes are fractional order usually. According to a large number of fractional-order systems, identification of these systems is so important. This paper aims to evaluate an improved Biogeography-based Optimization (BBO) approach to estimate the parameters and orders of fractional-order systems. After that, a method based on this algorithm has been introduced to synchronization of chaotic systems. Results show that the proposed scheme has high accuracy.

Computationally Efficient Nonlinear Model Predictive Control Using the L1 Cost-Function

Model Predictive Control (MPC) algorithms typically use the classical L2 cost function, which minimises squared differences of predicted control errors. Such an approach has good numerical properties, but the L1 norm that measures absolute values of the control errors gives better control quality. If a nonlinear model is used for prediction, the L1 norm leads to a difficult, nonlinear, possibly non-differentiable cost function. A computationally efficient alternative is discussed in this work. The solution used consists of two concepts: (a) a neural approximator is used in place of the non-differentiable absolute value function; (b) an advanced trajectory linearisation is performed on-line. As a result, an easy-to-solve quadratic optimisation task is obtained in place of the nonlinear one. Advantages of the presented solution are discussed for a simulated neutralisation benchmark. It is shown that the obtained trajectories are very similar, practically the same, as those possible in the reference scheme with nonlinear optimisation. Furthermore, the L1 norm even gives better performance than the classical L2 one in terms of the classical control performance indicator that measures squared control errors.

Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

As technology improves, the complexity of controlled systems increases as well. Alongside it, these systems need to face new challenges, which are made available by this technology advancement. To overcome these challenges, the incorporation of AI into control systems is changing its status, from being just an experiment made in academia, towards a necessity. Several methods to perform this integration of AI into control systems have been considered in the past. In this work, an approach involving GP to produce, offline, a control law for a reentry vehicle in the presence of uncertainties on the environment and plant models is studied, implemented and tested. The results show the robustness of the proposed approach, which is capable of producing a control law of a complex nonlinear system in the presence of big uncertainties. This research aims to describe and analyze the effectiveness of a control approach to generate a nonlinear control law for a highly nonlinear system in an automated way. Such an approach would benefit the control practitioners by providing an alternative to classical control approaches, without having to rely on linearization techniques.

COVID-19 Pandemic Analysis (Preprint)

BACKGROUND Evolving infectious diseases are a continuing threat to public health, especially in democratic nations where persuasion is required to adjust population behavior to counter the threat. COVID-19 is particularly dangerous because one can be infected by inhaling air exhaled by an infectious person who may not even be symptomatic. Experience in 2020-2021 can be used to guide future control efforts. OBJECTIVE This investigation sought to understand the factors that controlled the development of the COVID-19 pandemic, particularly the highs and lows in death rates as they were influenced by government leaders and media. METHODS Published data on COVID-19 death rates in eight selected nations and eleven selected U.S. states for the period 1 Mar. 2020 through 31 May 2021 were compared with calculations using a Susceptible-Exposed-Infectious-Recovered (SEIR) model that included an adjustment of the population’s basic reproduction number, R0, in response to information. RESULTS The death rates attributed to COVID-19 exhibited high peaks in most of the developed democratic nations in March/April 2020 and Dec. 2020/Jan. 2021. Initial values of R0 as high as 5.0 were deduced from the growth curves. The plateau near 700 deaths per million population reached in the summer of 2020 mirrored the effects of herd immunity in the model, but seroprevalence surveys proved that the population immune fraction was too low. An average of 169 people tested positive for COVID-19 antibodies for each death, i.e., more than twice the number of reported “cases” in the U.S. It was postulated that the March/April 2020 peak was primarily due to a ~20% fraction of the population that denied the threat and continued to interact normally. Nevertheless, toward the end of 2020 the general population relaxed its vigilance and another major peak in death rates occurred, even in areas that had suffered the most in the earlier peak. A reasonable selection of parameters for population response to information about “cases” and “deaths” produced the observed interval between peaks in the model and predicted a third peak in Sept. 2021 if less than 80% of the population were vaccinated. CONCLUSIONS Cyclic death waves are manifestations of a classical control loop with its feedback delayed by disease progression, political controversy, and natural population inertia. The pandemic was prolonged in the U.S. because the population chose to keep R0 near 1.0 by relaxing restrictions once the death rate subsided. If no social restrictions had been adopted while the quality of medical care was sustained, approximately 1.6 million deaths would have resulted in the U.S. The vaccine, although developed and deployed at record speed, was too late to ameliorate this result.

Damping of Torsional Vibrations in a Type-IV Wind Turbine Interfaced to a Grid-Forming Converter

This paper introduces a Type-IV wind turbine interfaced to a grid-forming converter. In order to retain the stable operation of a wind turbine in the presence of a grid-forming control, the classical control of a back-to-back converter has to be modified. The modification of this control creates a strong link between a wind turbine and grid dynamics. From the grid side perspective, this link allows provision of the inertial response from a wind turbine during transient events. On the wind turbine side, this coupling causes the appearance of the torsional vibrations within the drivetrain structure. These vibrations are then propagated to the grid as power oscillations. As a result, there is a negative impact on the mechanical components of a wind turbine as well as on the power system operation. In this work, a solution is introduced in order to suppress the undesired vibrations by applying a damping technique to the control of a back-to-back converter combined with a grid-forming control. Based on the conducted analysis, the addition of a damping filter results in the mitigation of torsional vibrations.