Class Of Control Laws Research Articles

КОНЦЕПЦІЯ ПОБУДОВИ ГІБРИДНОЇ СИСТЕМИ КЕРУВАННЯ МІКРОГІДРОЕЛЕКТРОСТАНЦІЄЮ

Methods of increasing the energy efficiency of micro-hydroelectric power plants are considered in the work. A new hy-brid concept of building a control system is proposed, which combines classical control laws and fuzzy logic. This makes it possible to improve the quality of the regulation of the parameters of the generated electricity regardless of changes in network operation modes and external conditions. It is shown that the use of hybrid filters will allow to in-crease the electromagnetic compatibility and, as a result, the energy efficiency of the system as a whole. References 12, figures 3.

Design of optimal-robust control of step motors with partially unknown parameters and disturbances

The paper describes the design of a novel control law for step motor under participially unknown parameters and bounded external disturbances. The unknown parameters and disturbances are related to the motor torque, the rotor moment of inertia, the viscous friction, the resistance and the load torque. The design of a novel control law is based on feedback linearization and linear robust control. The gain of a linear control law is calculated by solving the linear matrix inequality and some optimization problems. The simulations illustrate the efficiency of the proposed scheme in comparison with classical PID-controller and adaptive control law.

Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation. The considered system can depict the behavior of a large class of engineering systems, such as vehicular systems, robot manipulators and satellites. All these systems are often characterized by highly nonlinear characteristics, heavy modeling uncertainties and unknown perturbations, therefore, accurate-model-based nonlinear control approaches become unavailable. Motivated by the challenge, a reinforcement learning (RL) adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics. The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error (RISE) control approach. Different from a classical RISE control law, a tanh(·) function is utilized instead of a sign(·) function to acquire a more smooth control signal. The developed controller requires very little prior knowledge of the dynamic model, is robust to unknown dynamics and exogenous disturbances, and can achieve asymptotic output tracking. Eventually, co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom (3-DOF) manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

A Sensor-Aided System for Physical Perfect Control Applications in the Continuous-Time Domain.

The recently introduced continuous-time perfect control algorithm has revealed a great potential in terms of the maximum-speed and maximum-accuracy behaviors. However, the discussed inverse model-originated control strategy is associated with considerable energy consumption, which has exceeded a technological limitation in a number of industrial cases. In order to prevent such an important drawback, several solutions could be considered. Therefore, an innovative perfect control scheme devoted to the multivariable real-life objects is investigated in this paper. Henceforth, the new IMC-related approach, strongly supported by the vital sensor-aided system, can successfully be employed in every real-time engineering task, where the precision of conducted processes plays an important role. Theoretical and practical examples strictly confirm the big implementation potential of the new established method over existing ones. It has been seen that the new perfect control algorithm outperforms the classical control law in the form of LQR (considered in two separate ways), which is clearly manifested by almost all simulation examples. For instance, in the case of the multi-tank system, the performance indices ISE, RT, and MOE for LQR without an integration action have been equal to 2.431, 2.4×102, and 3.655×10-6, respectively, whilst the respective values 1.638, 1.58×102, and 1.514×10-7 have been received for the proposed approach.

Mechatronic design of dynamically decoupled manipulators based on the control performance improvement

AbstractThe control of industrial robot manipulators presents a difficult problem for control engineers due to the complexity of their nonlinear dynamics models. Nonlinear controls based on feedback linearization are developed to meet control requirements. Model-based nonlinear control is highly sensitive to parameter errors and leads to problems of robustness for tracking trajectories at high speeds, and there is the additional problem of a heavy computational burden to consider in the design of nonlinear controllers. In this paper, a mechatronic design approach is proposed, which aims to facilitate controller design by redesigning the mechanical structure. The problem is approached in two steps: first, the dynamic decoupling conditions of manipulators are described and discussed, involving redistribution of the moving mass, which leads to the decoupling of motion equations. A classical linear control law is then used to track the desired efficient bang-bang profile trajectory. Then, in the presence of parameter uncertainty and external disturbances, the nonlinear controls with simple structures are adopted to stabilize the decoupled system asymptotically. An analysis of the results from a simulation of this approach demonstrates its effectiveness in controller design. The proposed improvement in control performance is illustrated via two spatial manipulators.

Neural network based visual servo for quick-change device alignment in context of fusion reactor remote maintenance

The remote maintenance process for fusion reactor is complex, which can be very time-consuming and labor-consuming. This paper proposes a modified neural network based visual servo method to align a quick-change device with robot camera system. A classical position-based visual servo control law is used to guide the robot to reach the desired position. The key target for the above visual servo controller is to obtain a robust pose estimator to calculate the quick-change device pose with respect to the camera. This pose estimator is trained using RepVGG model with a self built image samples. An attention mechanism is added to the neural network to enhance the stability of pose prediction for reflective metal objects. The robot joint speed is also smoothed to reduce the image motion blur effect and make the visual servo process stable. The performance of the proposed visual servo controller was verified on an UR5 robot, and the results show that the stable and rough alignment of the quick-change device can be realized.

Reflectivity Control Devices for Continuous Momentum Management of Reaction Wheels

This paper presents a concept for deep-space momentum management based on reflectivity control devices. These devices are placed in the far corners of the solar panels and exploit solar radiation pressure by changing their reflectively properties from specular reflection to absorption. A specialized control algorithm is developed according to an optimization problem that minimizes the difference between the feasible control and an ideal one, continuously dumping momentum from the reaction wheels while undertaking the mission. The ideal control fuses a classical quaternion tracking control law with a desaturation control law specially designed for the on–off actuation of these devices. Two case studies are considered: 1) a geostationary satellite on a geostationary orbit, and 2) a 12U CubeSat in a quasi-halo moon orbit, which demonstrate the usefulness of the concept and effectiveness of the control law.

Synergetic Synthesis of Nonlinear Laws of Throttle Control of a Pneumatic Drive

Currently, a significant trend in control in robotic systems is developing and improving linear and nonlinear control algorithms to improve the overall quality of production with high accuracy and adaptability. The present study considers a synergistic synthesis of throttle control of a pneumatic distributor valve and backpressure control for piston rod positioning. The article presents the synthesis of control laws for the position of a pneumatic cylinder piston using the method of analytical design of aggregated regulators (ADAR) of synergetic control theory (STC), which allows operation with nonlinear mathematical models, eliminating the loss of information about the object during linearization. A comparative calculation of the energy efficiency of backpressure control and throttle control methods was carried out, while the numerical value of the total airflow with throttle control is 0.0569 m3⁄s and, with backpressure control, it is 0.0337 m3⁄s. Using a P controller in a linear model gives a transient oscillatory process damped in 2–2.5 s. When using a PID controller, the process has an overshoot equal to 11.5%, while the synergistic controller allows you to smoothly move the drive stem to a given position without overshoot. The parametric uncertainty analysis of the considered mathematical model is carried out. The model’s main parameters are identified, which change the actual functioning of the system under study. The inconsistency of applying classical control laws based on typical controllers to parametrically indeterminate mathematical models is shown.

Optimal flatness placement of sensors and actuators for controlling chaotic systems

Controlling chaotic systems is very often investigated by using empirical laws, without taking advantage of the structure of the governing equations. There are two concepts, observability and controllability, which are inherited from control theory, for selecting the best placement of sensors and actuators. These two concepts can be combined (extended) into flatness, which provides the conditions to fulfill for designing a feedback linearization or another classical control law for which the system is always fully observable and fully controllable. We here design feedback linearization control laws using flatness for the three popular chaotic systems, namely, the Rössler, the driven van der Pol, and the Hénon–Heiles systems. As developed during the last two decades for observability, symbolic controllability coefficients and symbolic flatness coefficients are introduced here and their meanings are tested with numerical simulations. We show that the control law works for every initial condition when the symbolic flatness coefficient is equal to 1.

Adaptive State-Feedback Shared Control for Constrained Uncertain Mechanical Systems

This study develops two kinds of adaptive state feedback shared-control algorithms for a class of nonlinear mechanical systems in the presence of output constraints described by a set of linear inequalities. The parametric uncertainties and unknown external disturbances in the dynamical model are compensated by the proposed adaptive control laws for the feedback controller. In particular, an adaptive fixed-time state-feedback control law is also developed after the classical robust adaptive state-feedback control law design. The properties of the closed-loop shared-control systems are studied by using Lyapunov method. Simulation results demonstrate the effectiveness of the proposed approach.

An Identification-Based Method Improving the Transparency of a Robotic Upper Limb Exoskeleton

SUMMARYOver the past decade, research on human–robot collaboration has grown exponentially, motivated by appealing applications to improve the daily life of patients/operators. A primary requirement in many applications is to implement highly “transparent” control laws to reduce the robot impact on human movement. This impact may be quantified through relevant motor control indices. In this paper, we show that control laws based on careful identification procedures improve transparency compared to classical closed-loop position control laws. A new performance index based on the ratio between electromyographic activity and limb acceleration is also introduced to assess the quality of human exoskeleton interaction.

Vibration control of a piezoelectric cantilever smart beam by ℒ 1 adaptive control system

In this paper, the modelling and design of an state feedback control system applied for vibration control of a smart piezoelectric flexible Euler–Bernoulli cantilever beam is presented. For a Single-Input Single-Output (SISO) case by retaining the first two dominant vibratory modes, the dynamics of the system is presented. Classical adaptive control law, with time-varying parameters and in presence of disturbance, is employed to suppress the vibration of the beam. Three Piezoelectric patches, two as actuators and the other as a sensor, are bonded to the structure at the support of the beam and along the length of the beam. The beam structure is modelled in the state space form using the concept of piezoelectric theory, the Euler–Bernoulli beam theory and the Finite Element technique. Also, for comparing the performance of the proposed controller, PID and LQR control systems are applied. Simulation results, first for constant parameters and then for time-varying parameters and disturbance, represent the better performance of the proposed control system in comparison to the other mentioned controllers.

Synthesis and investigation of the control system for the process of сarbon article molding

Among the most energy-intensive industries is the production of carbon articles, therefore, the improvement of its efficiency is a relevant scientific and technical task. One of the ways to resolve the set task is to create a modern production management system. This paper considers the creation of a control system for one of the essential technological processes in carbon articles production – the process of their formation. Underlying the control system is the optimality criterion based on the specific cost of products taking into consideration their quality indicators. The control method used is Model Predictive Control (MPC). The results of studying the dependence of an optimality criterion on the setting parameters of an MPC-controller have made it possible to determine the optimum values for the prediction and control horizons, which could ensure the minimization of the products’ specific cost. The structure of the proposed control system, developed in the Simulink programming environment, makes it possible to investigate a given control system through computer simulations. The efficiency of the proposed system to control the process of carbon product molding was examined by comparing the quality of control by a given system and by the system that uses the classic PID control law. To this end, a three-circuit control system based on the PID-controllers was synthesized in the Simulink programming environment. Each controller was set, using a Powell method, for a minimum value of the integrated criterion. The results of the comparative study have demonstrated that at each operation cycle the optimality criterion value in the control system employing an MPC-controller was 8.8 % less than that in the system with PID-controllers at the same indicators of product quality. That testifies to the improvement in the technical and economic indicators of the formation process. This fact is of particular importance when taking into consideration the circularity of the technological process of carbon product formation.

Robust control based on adaptive neural network for Rotary inverted pendulum with oscillation compensation

A new stable adaptive neural network (ANN) control scheme for the Furuta pendulum, as a two-degree-of-freedom underactuated nonlinear system, is proposed in this paper. This approach aims to address the control problem of the Furuta pendulum in the steady state and also in the presence of external disturbances. The adaptive classical control laws such as e-modification present some limitations in particular when oscillations are presented in the input. To avoid this problem, two ANNs are implemented using filtered tracking error in the control loop. The first one is a single hidden layer network, used to approximate the equivalent control online, and the second is the feed-forward network, used to minimize the oscillations. The goal of the control is to bring the pendulum close to the upright position in the presence of the various uncertainties and being able to compensate oscillations and external disturbances. The main purpose of the second ANN is to minimize the chattering phenomenon and response time by finding the optimal control input signal, which also leads to the reduction of energy consumption. The learning algorithms of the two ANNs are obtained using the direct Lyapunov stability method. The simulation results are given to highlight the performances of the proposed control scheme.

Pulse width Prediction Control Technique Applied to a Half-Bridge Boost

The pulse width prediction control technique has been widely used in AC-DC converters with power factor correction in boost topology, where satisfactory results have been reported without the need to use classic control laws. However, this technique has not been explored for other types of ACDC converters with power factor correction. In this work, the use of this technique is proposed in an AC-DC converter with power factor correction in a half-bridge boost topology. This topology presents high efficiency because it uses the least number of semiconductors compared to other topologies. In this technique the duty cycle is predicted by processing the average values of state and input variables of the converter in half bridge boost topology, using only four multiplications and five additions, which implies less complexity in its implementation. For its validation simulations were performed using MATLAB R simulink, where it was possible to observe values of power factor and THD comparable with other control methods such as non-linear carrier control, and conventional analog control, both reported in the literature.

An alternative proof to the asymptotic stability of PID controllers for regulation of robot manipulators

The control of robot manipulators has been extensively studied for many decades. Although there exists a wide variety of sophisticated techniques, when it comes to position regulation a classical PD control law can be employed only by gravitational compensation, while the stability proof is rather trivial. If the gravity term is unknown and thus it cannot be compensated, exact regulation can be achieved by using a PID control law. However, for this case the stability analysis is by far more complex and gain tuning less intuitive. In this note, we show that a common change of variables may make the stability analysis easier. Simulation results are provided to show that stability conditions are correct and up to some point similar to those obtained with previous approaches.

On the Design of Hyperstable Feedback Controllers for a Class of Parameterized Nonlinearities. Two Application Examples for Controlling Epidemic Models.

This paper studies the hyperstability and the asymptotic hyperstability of a single-input single-output controlled dynamic system whose feed-forward input-output dynamics is nonlinear and eventually time-varying consisting of a linear nominal part, a linear incremental perturbed part and a nonlinear and eventually time-varying one. The nominal linear part is described by a positive real transfer function while the linear perturbation is defined by a stable transfer function. The nonlinear and time-varying disturbance is, in general, unstructured but it is upper-bounded by the combination of three additive absolute terms depending on the input, output and input-output product, respectively. The non-linear time-varying feedback controller is any member belonging to a general class which satisfies an integral Popov’s-type inequality. This problem statement allows the study of the conditions guaranteeing the robust stability properties under a variety of the controllers designed for the controlled system and controller disturbances. In this way, set of robust hyperstability and asymptotic hyperstability of the closed-loop system are given based on the fact that the input-output energy of the feed-forward controlled system is positive and bounded for all time and any given initial conditions and controls satisfying Popov’s inequality. The importance of those hyperstability and asymptotic hyperstability properties rely on the fact that they are related to global closed-loop stability, or respectively, global closed-loop asymptotic stability of the same uncontrolled feed-forward dynamics subject to a great number of controllers under the only condition that that they satisfy such a Popov’s-type inequality. It is well-known the relevance of vaccination and treatment controls for Public Health Management at the levels of prevention and healing. Therefore, two application examples concerning the linearization of known epidemic models and their appropriate vaccination and/or treatment controls on the susceptible and infectious, respectively, are discussed in detail with the main objective in mind of being able of achieving a fast convergence of the state- trajectory solutions to the disease- free equilibrium points under a wide class of control laws under deviations of the equilibrium amounts of such populations.

System Modeling and Controller Design for Lateral and Longitudinal Motion of F-16

Classical control laws are still widely used in aviation industry because of their good structural understanding, simplicity, and better tracking control performance. However in recent decades the application of such controllers are getting substantial interest of researchers. This paper addresses controller design method for longitudinal and lateral motion autopilots of F-16. Aircraft complete mathematical model was obtained using Newton-Euler formulism. The non-linear model was linearized around equilibrium points at certain trim conditions to obtain state space model of the system. Comparative analysis of two linear controllers, Proportional-Integral-Derivative (PID) and Linear-Quadratic-Regulator (LQR) is investigated and control algorithm is proposed. Both the control schemes use feedback control laws and a careful selection of tuning parameters for controllers is carried out to track the desired input reference. Effectiveness of both controllers is illustrated with the help Matlab/Simulink figures and results.

Open-closed Iterative Learning Control Algorithm for Exoskeleton Rehabilitation Purposes

The paper designs an appropriate iterative learning control (ILC) algorithm based on the trajectory characteristics of upper exosk el eton robotic system. The procedure of mathematical modelling of an exoskeleton system for rehabilitation is given and synthesis of a control law with two loops. First (inner) loop represents exact linearization of a given system, and the second (outer) loop is synthesis of a iterative learning control law which consists of two loops, open and closed loop. In open loop ILC sgnPDD2 is applied, while in feedback classical PD control law is used. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed advanced open-closed iterative learning control scheme.

Analytical Fuzzy Predictive Control Applied to Wastewater Treatment Biological Processes

A novel control fuzzy predictive control law is proposed and successfully applied to a wastewater treatment process in this paper. The proposed control law allows us to evaluate the control signal in an analytical way, each sampling time being a nonlinear and fuzzy alternative to other classic predictive controllers. The control law is based on the formalization of the internal fuzzy predictive model of the process as linear time‐varying state space equations that are updated every discrete time instant to take into account the nonlinearity effects due to disturbance action and changes in the operating point with time. The model is then used to evaluate the predictions, and, taking them as a starting point and considering them as a paradigm of the predictive functional control strategy, a control law, it is derived in an analytical and explicit way by imposing on the outputs of the follow‐up of certain reference trajectories previously established. The work presented here addresses the application of this particular strategy of intelligent predictive control to the case of an activated sludge wastewater treatment process successfully in a simulation environment of a real plant taking into account real data for the disturbance records. Such a process is multivariable, nonlinear, time varying, and difficult to control due to its biological nature. The proposed control law can be straightforwardly used within a dual-mode MPC scheme to handle constraints, as a nonlinear and fuzzy alternative to the classic state feedback control law.