Classical Control System Research Articles

Implementation of partially tuned PD controllers of a multirotor UAV using deep deterministic policy gradient

The present methodology employed in classical control systems is characterized by high costs, significant processing requirements, and inflexibility. In conventional practice, when the controller exhibits instability after being implemented on the hardware, it is often adjusted to achieve stability. However, this approach is not suitable for mass-produced systems like drones, which possess diverse manufacturing tolerances and delicate stability thresholds. The aim of this study is to design and evaluate a controller for a multirotor unmanned aerial vehicle (UAV) system that is capable of adapting its gains in accordance with changes in the system dynamics. The controller utilized in this research employs a Simulink-constructed model that has been taught by reinforcement learning techniques, specifically employing a deep deterministic policy gradient (DDPG) network. The Simulink model of the UAV establishes the framework within which the agent engages in learning through interaction with its surroundings. The DDPG algorithm is an off-policy reinforcement learning technique that operates in continuous action spaces and does not require a model. The efficacy of the cascaded PD controllers and neural network tuner is evaluated. The results revealed that the controller exhibited stability during several flight phases, including take-off, hovering, path tracking, and landing manoeuvres.

Active Willis metamaterials with programmable density and stiffness

Investigation and implementation of Active Willis Metamaterials (AWM) have been done exclusively, in all the available literature, by approaches that do not rely on any solid control theory basis. When coupled with piezoelectric control elements, the available approaches have not included, from the first principles, the exact form of the constitutive relationship of the piezoelectric materials. Furthermore, in all these approaches, stability analysis, robustness, ability to accommodate uncertainty or parameter changes, or consideration of disturbance rejection has not been addressed at all. More importantly, the available formulations have always mixed the flow and effort variables of the AWM, resulting in a form that is totally incompatible for the use in generating, investigating, or even designing any appropriate sensing or control applications of the material. In this paper, the piezoelectric-based AWM is modeled, from the first principles, to develop a constitutive coupling form that enables its use in actuation, sensing, and as an integrated controller that can be analyzed, designed, and optimized using the classical, optimal, and robust control system theories. Lagrange dynamics formulation is used to generate the equations governing the Willis coupling, the piezoelectric coupling, as well as the active robust controller. With this developed controlled-based structure of the AWM, the inherent and powerful capabilities of the AWM that lie in its ability to robustly control the material properties themselves such as the compliance (or stiffness) and specific volume (or density) are demonstrated in great detail via several numerical examples. Controlling these properties enables the AWM to be used in numerous important and imaginative applications such as cloaking, beam shifting, beam focusing, as well as many other applications that are limited only by our imagination.

Application of two-step input to reduce overshoot of the transient response at automated control systems

The problems of quality improving for technology control are very important and are considered in works on the theory of automatic control and related fields. Various approaches to solving the problems are known especially by the decreasing overshoot of step response. To provide this the most of existing methods require: to adjust a controller parameters; development of a mathematical model of a controlled object; additional filters applications, that as a whole is difficult to implement under industrial conditions. The authors have proposed another approach to solving the above problems, contrary to the known ones. Namely, to apply two successive inputs with lower amplitudes and delay in time instead of the known one step input. The response of the controlled system on the complex two step inputs was considered and the optimal time delay was defined. The investigations were conducted by the modelling a typical classic linear automation control system that consists of a static control object of the first order with time delay, proposal - integration - difference controllers. The modelling results for a transient under the various applied one and two step inputs, including their ramp variation were obtained. It was shown that the two-step inputs with their ramp application gives the decreasing of the maximum displacement for a transient in a typical control system: more than 3 times increase was shown comparing with the one-step approach. The optimal time interval between the steps was determined that leads to the maximum effectiveness of the technology application. The procedure to define the details of the approach application was developed

Model predictive-based DNN control model for automated steering deployed on FPGA using an automatic IP generator tool

With the increase in the non-linearity and complexity of the driving system’s environment, developing and optimizing related applications is becoming more crucial and remains an open challenge for researchers and automotive companies alike. Model predictive control (MPC) is a well-known classic control strategy used to solve online optimization problems. MPC is computationally expensive and resource-consuming. Recently, machine learning has become an effective alternative to classical control systems. This paper provides a developed deep neural network (DNN)-based control strategy for automated steering deployed on FPGA. The DNN model was designed and trained based on the behavior of the traditional MPC controller. The performance of the DNN model is evaluated compared to the performance of the designed MPC which already proved its merit in automated driving task. A new automatic intellectual property generator based on the Xilinx system generator (XSG) has been developed, not only to perform the deployment but also to optimize it. The performance was evaluated based on the ability of the controllers to drive the lateral deviation and yaw angle of the vehicle to be as close as possible to zero. The DNN model was implemented on FPGA using two different data types, fixed-point and floating-point, in order to evaluate the efficiency in the terms of performance and resource consumption. The obtained results show that the suggested DNN model provided a satisfactory performance and successfully imitated the behavior of the traditional MPC with a very small root mean square error (RMSE = 0.011228 rad). Additionally, the results show that the deployments using fixed-point data greatly reduced resource consumption compared to the floating-point data type while maintaining satisfactory performance and meeting the safety conditions

A Comparative Analysis of the Performance of Classical Multidimensional Control Systems and Systems with a Neural Controller on the Example of a Real Object

A Comparative Analysis of the Performance of Classical Multidimensional Control Systems and Systems with a Neural Controller on the Example of a Real Object

Understanding via Exploration: Discovery of Interpretable Features With Deep Reinforcement Learning.

Understanding the environments through interactions has been one of the most important human intellectual activities in mastering unknown systems. Deep reinforcement learning (DRL) has already been known to achieve effective control through human-like exploration and exploitation in many applications. However, the opaque nature of deep neural network (DNN) often hides critical information about feature relevance to control, which is essential for understanding the target systems. In this article, a novel online feature selection framework, namely, the dual-world-based attentive feature selection (D-AFS), is first proposed to identify the contribution of the inputs over the whole control process. Rather than the one world used in most DRL, D-AFS has both the real world and its virtual peer with twisted features. The newly introduced attention-based evaluation (AR) module performs the dynamic mapping from the real world to the virtual world. The existing DRL algorithms, with slight modification, can learn in the dual world. By analyzing the DRL's response in the two worlds, D-AFS can quantitatively identify respective features' importance toward control. A set of experiments is performed on four classical control systems in OpenAI Gym. Results show that D-AFS can generate the same or even better feature combinations than the solutions provided by human experts and seven recent feature selection baselines. In all cases, the selected feature representations are closely correlated with the ones used by underlying system dynamic models.

Comparing actor-critic deep reinforcement learning controllers for enhanced performance on a ball-and-plate system

The optimization of controller parameters remains an ongoing challenge in the field of control system applications. This study introduces a novel approach involving the creation of custom actor-critic deep reinforcement learning (DRL) based PID controllers. These controllers are designed with the goal of achieving adaptive tuning, precise trajectory tracking, and stability in a ball-and-plate system. To achieve this objective, multiple actor-critic reinforcement learning agents were developed using different learning algorithms: Soft actor critic (SAC), deep deterministic policy gradient (DDPG), and twin delayed deep deterministic policy gradient (TD3). These agents incorporate multilayer-perceptron (MLP) policy learning algorithms in both the actor and critic network architectures, employing non-linear activation functions. This enables them to fine-tune PID control parameters within an infinite search space. Additionally, a custom reward function derived from the system’s environment was integrated into the learning process. The performance of the proposed methods was compared against a benchmark method, specifically, an existing deep reinforcement learning controllers reported in the literature. The evaluation of these controllers and other approaches was based on error metrics and time response analysis. Results demonstrate that the proposed controller denoted as SAC-PID(5) excelled in trajectory tracking and outperformed other methods. It exhibited minimal predictive errors and the shortest time responses in the majority of experiments. This highlights the significance of designing a customized SAC agents with appropriate network architecture, which positively impacts the learning process for intelligent tuning of controllers for classical control systems.

Structural and Parametric Synthesis of Neural Network Controllers for Control Objects with Limiters

The article presents a methodology for the synthesis of digital control systems for nonlinear objects with limiters under conditions of incomplete information. Closed-loop tracking systems with negative feedback are considered. Artificial neural networks are proposed to build a controller, which is included in series with the control object. This approach is effective when known classical methods do not allow to synthesize control. This is the case, for example, if the mathematical model is essentially nonlinear and is not fully defined. The developed methods allow us to expand the class of technical systems, for which the direct (without using various kinds of simplifications) synthesis of control laws that are close to optimal is possible. In addition, neural network controllers possess the properties of robustness, adaptivity, and are initially digital, i.e. those qualities, which are very much in demand in practice. In article main attention is given to such problems, as a choice of neural network structure for neural simulator and neural controller, construction of training sample, ensuring convergence of the process of weights correction. For training neural networks the method of back propagation of error is used as a basic one. The effectiveness of the proposed technique is demonstrated by the example of the synthesis of a neuroregulator for a specific technical object and its comparison with classical control systems. It should be noted that today neural network technologies are widespread enough in various spheres of activity. The successes demonstrated in sound processing, image processing, automatic translation, in navigation systems, in big data processing are impressive. However, their application in automatic control systems is not so widespread. The authors of this article believe that the potential of artificial neural networks can be used in this direction. It should be understood that the use of neural networks is effective only under certain conditions and properties of the control object.

Model Predictive Control With Wind Preview for Aircraft Forced Landing

Autonomous emergency landing capability of fixed-wing aircraft is essential for opening airspace for civil unmanned aviation. This paper proposes a goal-oriented control scheme to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exploit</i> wind information for the benefit of forced landing. Different from general disturbances in a classic control system, a favorable wind would help aircraft to glide to a selected landing site more easily so increase the level of safety, while an adverse wind may render a selected landing site infeasible. We formulate the forced landing problem with wind preview information in the framework of Economic Model Predictive Control (EMPC) which aims to maximize the aircraft's final altitude when reaching a target region. A double-layer MPC scheme is adopted to lessen the computational burden and to increase the prediction time window for practical implementation, where a piecewise-constant disturbance preview based EMPC (DP-EMPC) maximizes the altitude at the upper-level, and a linear MPC is employed at the lower-level to track the reference signal optimized by the upper-level planner. Moreover, the effectiveness of the goal-oriented optimal control scheme is illustrated by several case studies, where an unmanned aircraft is gliding towards potential landing sites under various conditions.

Relationships between vibrations of main device and mechanical network in a classical vibration control system

For a classical vibration control system consisting of a main device, an auxiliary device and a mechanical network, two major results are obtained. The first major result is analyzing the effect of adjusting the main device vibration on the vibration of the mechanical network. The second major result is establishing the upper and lower bounds of the amplitude–frequency curve of the mechanical network when the amplitude–frequency curve of the main device is fixed. For theoretical research, the first conclusion can be used to predict the variation trend of the vibration of the mechanical network when the vibration of the main device is further suppressed. For engineering applications, the second conclusion can be used to evaluate whether the vibration suppression performance indicators can be achieved. Then, to demonstrate the effectiveness, these two major results are applied to wind turbine load mitigation. A 7-DOF onshore wind turbine model including nacelle relative displacement is established. By replacing the rigid connection between the nacelle and the tower top with a mechanical network, the tower top relative displacement is reduced under both wind loads and seismic waves. Finally, a theoretical explanation of the simulation results is provided using the two major results above.

Self-Optimizing Control System to Maximize Power Extraction and Minimize Loads on the Blades of a Wind Turbine

This research proposes a methodology for designing and testing a self-optimizing control (SOC) algorithm applied to a wind energy conversion system (WECS). The SOC maximizes WECS power output and reduces the mechanical stress of the wind turbine (WT) blades by optimizing a multiobjective cost function. The cost function computation uses a combined blade element momentum (BEM) and thin-wall beam (TWB) model for calculating wind the turbine power output and blades’ stress. The SOC deployment implies a low computational cost due to an optimization space reduction via a matrix projection applied to a measurement vector, based on a prior offline calculation of a projection matrix, H. Furthermore, the SOC optimizes the operation of the WECS in the presence of uncertainty associated with the wind speed variation by controlling a linear combination of measured variables to a set point. A MATLAB simulation of a wind turbine model allows us to compare the WECS operating with the SOC, a baseline classic control system (BCS), and a nonlinear model predictive controller (NMPC). The SOC algorithm is evaluated in terms of power output, blades’ stress, and computational cost against the BCS and NMPC. The power output and blades’ stress performance of the SOC algorithm are compared with that of the BCS and NMPC, showing a significant improvement in both cases. The simulation results demonstrate that the proposed SOC can effectively optimize a WECS operation in real time with minimal computational costs.

Model reference adaptive control for nonlinear time‐varying hybrid dynamical systems

SummaryThis paper presents the first model reference adaptive control system for nonlinear, time‐varying, hybrid dynamical plants affected by matched and parametric uncertainties, whose resetting events are unknown functions of time and the plant's state. In addition to a control law and an adaptive law, which resemble those of the classical model reference adaptive control framework for continuous‐time dynamical systems, the proposed framework allows imposing instantaneous variations in the reference model's trajectory to rapidly steer the trajectory tracking error to zero, while retaining the closed‐loop system's ability to follow a user‐defined signal. These results are enabled by the first extension of the classical LaSalle–Yoshizawa theorem to time‐varying hybrid dynamical systems, which is presented in this paper as well. A numerical simulation shows the key features of the proposed adaptive control system and highlights its ability to reduce both the control effort and the trajectory tracking error over a classical model reference adaptive control system applied to the same problem.

The common welfare human resource management system and innovativeness: the mediating role of altruism

PurposeThe paper analyzes and develops Chiva's (2014) proposal on the common welfare HRM system and uncovers its relationship with innovativeness, using altruism as a mediator.Design/methodology/approachThe common welfare HRM system implies a certain human and organizational development of the classic control and commitment HRM systems, and its main goal is to promote innovation through a prosocial approach. To this end, the authors investigated its HRM practices, developed a measurement instrument and provided initial illustrative evidence of some of its main implications for innovativeness and altruism. They tested these relationships on a sample of 269 Spanish firms using structural equations and bootstrapping to confirm the significance of the mediated effect.FindingsResults confirm the study’s hypotheses, thus supporting the common welfare HRM system as a relevant tool for developing innovativeness through the power of altruism. This paper therefore provides empirical evidence of these relationships.Practical implicationsThis study has implications that can help managers to increase innovativeness through a specific HRM system. The findings reveal that a coherent set of HRM practices based on common welfare principles and a high level of consciousness creates a climate of altruism that results in innovativeness.Originality/valueThis research shows that humanistic HRM practices also have an impact on performance variables such as innovativeness, through altruistic employees' behaviors. It also develops a measurement instrument for the common welfare HRM system and provides some initial illustrative evidence of some of its main implications.

Series APF based on Five and Seven-Level NPC Inverters using modified PQ Method

This paper presents the performance analysis of Series Active Power Filter (Series APF) based on five and seven-level neutral point clamped (npc) using modified instantaneous reactive power control strategy to use for harmonic and all voltage disturbances compensation. The conventional configuration based on standard voltage source inverter (VSI) with hysteresis controller presents several drawbacks such us uneven switching frequency and limited power applications. Multi-level inverters such us Five and seven-level are currently being investigated and used in various industrial applications; their advantages include the capability to reduce the harmonic content and decrease the voltage or current ratings of the semiconductors with low switching losses, low electromagnetic interference, and low acoustic noise. Fuzzy controllers are successfully employed in various industrial applications and are found more robust than conventional controllers; they represent a good alternative to classic control systems. To benefit of all these advantages two configuration of Series APF are presented in this paper adopting the same control scheme based on Phase Disposition-Sinusoidal Pulse Width Modulation (PD-SPWM) and fuzzy logic control (FLC). The proposed two series APF are evaluated using MATLAB-Simulink software and SimPowerSystem Toolbox for harmonic compensation and in case of all voltage disturbances compensation. The simulation results show the performance and the efficiency of the proposed Series APF based on seven-lever (npc) inverter in the improvement of the power quality.

Robust Stability Analysis of Smith Predictor Based Interval Fractional-Order Control Systems: A Case Study in Level Control Process

The robust stability study of the classic Smith predictor-based control system for uncertain fractional-order plants with interval time delays and interval coefficients is the emphasis of this work. Interval uncertainties are a type of parametric uncertainties that cannot be avoided when modeling real-world plants. Also, in the considered Smith predictor control structure it is supposed that the controller is a fractional-order proportional integral derivative (FOPID) controller. To the best of the authors' knowledge, no method has been developed until now to analyze the robust stability of a Smith predictor based fractional-order control system in the presence of the simultaneous uncertainties in gain, time-constants, and time delay. The three primary contributions of this study are as follows: i) a set of necessary and sufficient conditions is constructed using a graphical method to examine the robust stability of a Smith predictor-based fractional-order control system—the proposed method explicitly determines whether or not the FOPID controller can robustly stabilize the Smith predictor-based fractional-order control system; ii) an auxiliary function as a robust stability testing function is presented to reduce the computational complexity of the robust stability analysis; and iii) two auxiliary functions are proposed to achieve the control requirements on the disturbance rejection and the noise reduction. Finally, four numerical examples and an experimental verification are presented in this study to demonstrate the efficacy and significance of the suggested technique.

State estimation accuracy analysis of an induction electric drive by the algorithms of Luenberger and Kalman

In complex electromechanical objects containing electric drives with induction motors, it is often difficult or impossible to install sensors of output variables. In this case, to determine the output coordinates of the motor, it is necessary to introduce state observers into the control system of the electric drive. The main problems of creating observers are the presence of noise and interference in the measuring circuits of the control system which affect the accuracy of the estimation of immeasurable state variables. The paper presents a comparison of the accuracy of estimates obtained as a result of the work of the observer algorithms based on the Kalman filter and the observer of the Luenberger in the induction electric drive system, with vector control at the noise level of the current measurement channels in the stator windings of the induction motor. To synthesize algorithms for state observers, methods of identification theory and quasi-linearization of nonlinear models of the control object under consideration were used. The simulation model of an induction motor is based on a classical vector field-oriented control system where an estimate of the angular speed of the motor shaft is used as a feedback signal. The model implements the following blocks: a mathematical model of an induction motor in a two-phase fixed coordinate system α–β; the structure of the observer algorithm; the procedure for converting the basis of the current vector and the control voltage from stationary to rotating and vice versa; proportional-integral regulators of current, flux linkage and angular speed. The S-shaped intensity setter forms a speed setting curve. The input signals for observers are the stator voltages and currents of the reference model of an induction motor. The adaptation coefficients for the Luenberger observer were selected experimentally from the condition of obtaining the average minimum value of the difference modulus of the estimated values. The covariance matrices for the observer based on the Kalman filter are configured on the basis of the experiment, ensuring a minimum of the average value of the absolute error. The time dependences of the transients of the angular speed of the shaft, the modulus of the flux linkage vectors of the rotor and stator currents are obtained. The dependencies were evaluated when starting an induction motor with nominal values and values of frequency and voltage amounting to 10 % of the nominal values. The work of estimation algorithms in the presence of a noise component, as well as when changing the parameters of the induction motor replacement circuit by ± 10 %, is investigated. The results of modeling the operation of the electric drive in starting modes with a mechanical load equal to the nominal value at a supply voltage frequency of 50 Hz and at 10 % of the nominal value for a voltage of 1 Hz are obtained. It is shown that the greatest relative estimation errors occur in the starting mode of the electric drive, and the maximum accuracy is achieved in the case of using a nonlinear Kalman filter. The results of the work can be used in the development of automatic control systems for sensorless electric drives and frequency-controlled electric drive of centrifugal pumping units for oil production.

Modeling of energy-efficient direct torque control system for traction induction motor

A brief analysis of existing control systems for traction electric drives with induction motors has been carried out, as a result of which it has been established that vector control and direct torque control systems are the most promising. The comparative characteristic of these control systems according to different parameters of electric drive quality has shown that in energy-saving algorithms it is appropriate to use the systems of direct torque control. The work aims to simulate the electric drive with a direct torque control system, which provides the reduction of power losses in an asynchronous motor. The value of the stator winding power factor is chosen as a control action. The calculation and functional dependences between the main parameters of the control system and the induction motor are synthesized, the fulfillment of which allows reducing duce power losses. The functional diagram of the electric drive with an energy-efficient control system is given. The hardware of this control system does not differ from the currently used frequency converters. In the power channel of the frequency converter, a three-level autonomous voltage inverter with locking diodes is used. The results of modeling for electric drive with 11 kW motor, analysis of which showed the adequacy of the developed model and performance of the synthesized control system, the efficiency of the electric drive with energy-efficient control system increases to 18% compared with the classical direct torque control system.

Interdisciplinary Trends in the Reintegration of Organisms with Perceptron Units

Various artificial algorithms have emerged from imitating the behavior of primitive cells. Despite their independent development, these algorithms still have the potential to integrate with the intelligence of the primitive biological cells and control their behavior. Research on biologically inspired intelligence emerged earlier, with the goal of combining algorithms with the inherent intelligence of living organisms. When microbial behavior meets the requirements of the algorithm, such microorganisms can be used as a carrier for computation. This review analyzes the shortcomings of this approach and points out potential directions for development, including using existing structures inside cells to simulate control systems such as circuits, in order to construct cells that fully satisfy the needs of the algorithm. However, when using classical control systems to achieve the goal, it is necessary to construct a fairly complex internal cell structure, and this review analyzes the shortcomings of this approach. Subsequently, by analyzing recent research, this review points out the latest novel research direction, in which some scholars attempt to construct artificial cell structures to directly achieve the function of the algorithm by building low-implementation-difficulty internal control systems in cells. However, there are still some problems to be solved, and this review summarizes the relevant research and briefly discusses the current theoretical challenges.

DYNAMIC MODELING AND CONTROL OF AN ELECTRO-MECHANICAL FIN LOADING SYSTEM

In this study, the design of an electro-mechanical fin loading system which is utilized to simulate the effects of the aerodynamic moment occurring in an atmospheric flight on a missile system is dealt with. In this extent, the dynamic modeling of the dedicated loading system is constructed and then the control system is designed on that model. As the control algorithm, the classical control system operating with regard of the PID (proportional plus integral plus derivative) control action and the robust control system designed according to the H∞ norm are taken into consideration. Once the mentioned control systems are built, the relevant computer simulations are performed with the inclusion of a realistic control actuation system model in accordance with the consistent numerical values of the necessary parameters. As a result of this work, it is observed that the robust control system yields more satisfactory results than its classical counterpart even under more severe conditions.

Event-based MPC for propofol administration in anesthesia

Background and Objective: The automatic control of anesthesia is a demanding task mostly due to the presence of nonlinearities, intra- and inter-patient variability and specific clinical requirements to be meet. The traditional approach to achieve the desired depth of hypnosis level is based on knowledge and experience of the anesthesiologist. In contrast to a typical automatic control system, their actions are based on events that are related to the effect of the administrated drug. Thus, it is interesting to build a control system that will be able to mimic the behavior of the human way of actuation, simultaneously keeping the advantages of an automatic system.Methods: In this work, an event-based model predictive control system is proposed and analyzed. The nonlinear patient model is used to form the predictor structure and its linear part is exploited to design the predictive controller, resulting in an individualized approach. In such a scenario, the BIS is the controlled variable and the propofol infusion rate is the control variable. The event generator governs the computation of control action applying a dead-band sampling technique. The proposed control architecture has been tested in simulation considering process noise and unmeasurable disturbances. The evaluation has been made for a set of patients using nonlinear pharmacokinetic/pharmacodynamic models allowing realistic tests scenarios, including inter- and intra-patient variability.Results For the considered patients dataset the number of control signal changes has been reduced of about 55% when compared to the classical control system approach and the drug usage has been reduced of about 2%. At the same time the control performance expressed by the integrated absolute error has been degraded of about 11%.Conclusions: The event-based MPC control system meets all the clinical requirements. The robustness analysis also demonstrates that the event-based architecture is able to satisfy the specifications in the presence of significant process noise and modelling errors related to inter- and intra-patient variability, providing a balanced solution between complexity and performance.