Desired Control Signal Research Articles

Solid oxide fuel cell (SOFC) control strategy enhancement by adaptive neuro-fuzzy inference system (ANFIS)

Global warming is a vital problem that many researchers tried to solve with so many solutions such as: reducing electricity production with conventional generators by using renewable energy resources and using hydrogen as an alternative to fossil fuels. The universal Economic crisis came across to cut off certain quantities at scheduled times. This, directly, affects renewable energy sources connected to the grid due to voltage and frequency variations. To solve this dilemma, in this paper, the grid-connected solid oxide fuel cell (SOFC) model which is fed by green hydrogen to produce AC power, is developed by an Adaptive neuro-fuzzy inference system (ANFIS). It is one of the artificial intelligence applications to improve grid-connected SOFC dynamic response. ANFIS is mathematically presented and simulated using MATLAB/SIMULINK. The results show that the ANFIS controller has succeeded in enhancing most of the desired control and operation signals.

Novel Single Bounded Input Control Synchronization Criterion for a Category of Hyperchaotic and Chaotic Systems in Presence of Uncertainties

Abstract Dynamical systems that have a chaotic underlying structure have a sensitive dependency on the initial conditions and the values of their parameters. In this piece of work, a straightforward method for solving the synchronization issue in master–slave arrangement for a category of chaotic or hyperchaotic systems, in which perturbations are present in the parameters of the response system, is discussed. The desired control signal is bounded by the initial state when the controller is activated. There is just one control input that is used, and it is derived from Lyapunov's concept of stability. In general, it is tricky to synchronize hyperchaotic or chaotic systems with single controller, and the work turns out to be significantly more complex when the parameters of the slave system are perturbed. The feedback controller using single input that has been constructed makes certain that the state variables of the response system are in synchronization with the state variables that correspond to them in the drive system. In order to attain the desired level of synchronization, the required conditions that must be satisfied to do so have been identified utilizing Lyapunov's stability analysis in a simple manner. In addition, numerical illustrations have been provided in order to support and confirm the theoretical findings of the paper.

Damping Magnetorheological Systems Based on Optimal Neural Networks Preview Control Integrated with New Hybrid Fuzzy Controller to Improve Ride Comfort

<div>Adaptive neural networks (ANNs) have become famous for modeling and controlling dynamic systems. However, because of their failure to precisely reflect the intricate dynamics of the system, these have limited use in practical applications and perform poorly during training and testing. This research explores novel approaches to this issue, including modifying the simple neuron unit and developing a generalized neuron (GN). The revised version of the neuron unit helps to develop the system controller, which is responsible for providing the desired control signal based on the inputs received from the dynamic responses of the vehicle suspension system. The controller is then tested and evaluated based on the performance of the magnetorheological (MR) damper for the main suspension system. These results of the tests show that the optimal preview controller designed using the GN both ∑-Π-ANN and Π-∑-ANN can accurately capture the complex dynamics of the MR damper and improve their damping characteristics compared with other methods. The seat and main suspension systems work together to provide more support and comfort for the driver and passengers. The short stroke of the MR damper is used in seat suspension as it allows for more precise control over the suspension and can provide a smoother ride. The new hybrid fuzzy type-2 (T-2) control is designed to accurately estimate the desired damping force for the seat MR damper. This system also allows for the damping force to be adjusted to meet the desired requirements of the seat MR damper. This integration of damping systems allows better control and stability of the vehicle and provides a smoother ride for drivers and passengers. Furthermore, integrating the damping systems increases the overall performance of the vehicle, making it better able to handle various road conditions.</div>

Data-Driven Control Based on Estimation of Desired Control Signals

In this paper, a new data-driven control is proposed to realize desired closed-loop responses. Proposed methods can tune the feedback controller by estimating desired control signals. The evaluation function can be minimized by least square method because the inverse model of the feedback controller is not required. The validity of the proposed method is verified via numerical simulation and experiments.

CALCULATING THE BOUNDARIES OF THE AIRCRAFT EXIT ZONE TO A SET POINT

The article considers the method of calculating the boundaries of the exit zone of the aircraft to a given point based on the optimal control method. To find the optimal control, it is proposed to use a direct method based on parameterization of the desired control signals using third-order Hermitian splines. The choice of Hermitian cubic splines was motivated by the fact that these splines and their first order derivatives are smooth and continuous functions, on the one hand, and, on the other, do not require the additional solution of algebraic equations to meet the specific conditions in spline nodes which is obligatory for classic cubic splines. Spline parameters estimation is achieved through solution of the unconditional multiparametric optimization problem. The target functional includes the squares of mismatches between the desired output signals and the object model output signals. In this paper the parameter estimates are obtained using the widely known numerical optimization algorithm – the particle swarm method. The paper considers the aircraft motion in the vertical plane, for which a mathematical model of the control object is formed and the target functional is formulated. The proposed solution is advisable to apply when calculating the optimal trajectories and flight profiles of aircraft when planning their functioning for the designed purpose. The developed method allows solving a number of tasks in the process of modern aircraft design and flight tests. The application of the proposed method, the required structure of the mathematical model of the object and the features of the formation of the minimized functional are shown in a specific example.

Boundary adaptive fault-tolerant control for a flexible Timoshenko arm with backlash-like hysteresis

This study is concerned with a novel adaptive fault-tolerant control design for a flexible Timoshenko arm considering the effects of actuator failures, backlash-like hysteresis, and external disturbances. First, the actuator failures and backlash-like hysteresis are integrated together and resolved into desired control signals and nonlinear errors. Second, these errors and external disturbances are deemed as composite disturbance terms to be handled with adaptive techniques. Third, adaptive fault-tolerant controllers with online updates are established to eliminate the shear deformation and elastic oscillation, lay the arm in a desired angle, counteract the hybrid effects of actuator failures and hysteresis, and deal with the uncertainty of composite disturbances. Then, based on the Lyapunov’s stability theory, the proposed strategy guarantees the uniformly bounded stability in the controlled system. Finally, numerical examples are presented to illustrate the efficacy of the suggested scheme.

Improved Tracking of Optimal Torque by Artificial Neural Network for Wind Energy Systems

This paper presents an effective scheme for tracking the optimal power extracting from the natural wind of a Doubly-Fed-Induction-Generator based on Wind Turbine (DFIG-WT). The identification and the operation of the model components on dynamic behaviour, and vector control concerning the DFIG, in a synchronously rotating d-q framework are introduced. Thus, the desired control signals of the electromagnetic torque and the reactive power are regulated by controlling the machine side converter, combined with the implementation of the flexible scheme of the optimal torque algorithm to follow continually the trajectory of the reference electromagnetic torque during rapid fluctuation of the wind speed, and under different supplying scenarios of reactive power generation. The maximum power point is achieved directly by the value of wind speed, estimated by the artificial neural network through real measured data of two input samples as the mechanical power of a wind turbine and its mechanical rotor speed. Particularly, this scheme leads to better dynamic efficiency in wind generation, which does not need an anemometer, which is available, or a wind sensor. In essence, the analysis and the comparison between the proposed scheme and the conventional algorithm at the similar conditions are provided via demonstrative simulation studies. Obviously, the effectiveness and the feasibility of a smart-sensorless controller in terms of good performance and fast convergence are confirmed.

Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi‐speed scheduler

This study presents an adaptive motion planning strategy for automated vehicle collision avoidance systems to be associated with the variation of collision speed region based on the position of the obstacle. This is done by designing the motion planner using an artificial potential field (APF) with the incorporation of an adaptive multi-speed scheduler using fuzzy system in the motion planning structure. The knowledge database information is developed based on the risk perception of the driver that consists of APF parameters and was optimised by using particle swarm optimisation algorithm. This study contributes to the improvement of a feasible reference motion generated by the motion planner that can be converted into desired control signals. The reference motion resulted to provide the control command that managed to avoid collision successfully by evasive manoeuvre without lane departure when adapting to variation in the vehicle speeds with different obstacle positions. The results indicated the reduction of the lateral error with respect to the reference avoidance trajectory data of up to 87% compared to base-type APF with maximum reference lateral motion is reduced of up to 26%. Then, a hardware-in-loop test is conducted to verify the proposed strategy using a steering wheel system.

Adaptive fuzzy ILC of nonlinear discrete-time systems with unknown dead zones and control directions

This paper presents an adaptive fuzzy iterative learning control (ILC) design for non-parametrized nonlinear discrete-time systems with unknown input dead zones and control directions. In the proposed adaptive fuzzy ILC algorithm, a fuzzy logic system (FLS) is used to approximate the desired control signal, and an additional adaptive mechanism is designed to compensate for the unknown input dead zone. In dealing with the unknown control direction of the nonlinear discrete-time system, a discrete Nussbaum gain technique is exploited along the iteration axis and applied to the adaptive fuzzy ILC algorithm. As a result, it is proved that the proposed adaptive fuzzy ILC scheme can drive the ILC tracking errors beyond the initial time instants into a tunable residual set as iteration number goes to infinity, and keep all the system signals bounded in the adaptive ILC process. Finally, a simulation example is used to demonstrate the feasibility and effectiveness of the adaptive fuzzy ILC scheme.

Open loop control of the five-axis missile and target flight motion simulator implementation

An important phase in missile systems development is flight testing and simulation in the environmental conditions dictated by the operation the system is made for. Since field testing of complex systems means a big financial burden and a time consuming process, hardware in the loop (HIL) simulations represent a very effective solution for saving both costs and time. This paper presents an implementation of a control application that integrates synchronized data generation and acquisition on the five-axis missile and target flight motion simulator owned by the HIL laboratory. The described simulation application is a result of a missile development engineer's need for a simple way to acquire information on a real missile response, when desired control signals are provided. The solution is realized in Lab VIEW software using a robust software design architecture named 'classic state machine'.

Direct adaptive neural control of nonlinear strict‐feedback systems with unmodeled dynamics using small‐gain approach

SummaryIn this paper, a novel direct adaptive neural control approach is presented for a class of single‐input and single‐output strict‐feedback nonlinear systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. Radial basis function neural networks are used to approximate the unknown and desired control signals, and a direct adaptive neural controller is constructed by combining the backstepping technique and the property of hyperbolic tangent function. It is shown that the proposed control scheme can guarantee that all signals in the closed‐loop system are semi‐globally uniformly ultimately bounded in mean square. The main advantage of this paper is that a novel adaptive neural control scheme with only one adaptive law is developed for uncertain strict‐feedback nonlinear systems with unmodeled dynamics. Simulation results are provided to illustrate the effectiveness of the proposed scheme. Copyright © 2015 John Wiley & Sons, Ltd.

Mathematical design and analysis of anti-lock brake system for electric vehicle based on brake-by-wire technology

The current study presents the derivation of mathematical modelling of anti-lock brake system solely from brake-by-wire perspective. The study also proposes permanent magnet DC (PMDC) motor to perform suitable braking action which is selected based on predetermined electric actuator characteristics. The brake system and electric motor specifications are then designed, taking into consideration maximum magnitude of electric vehicle power supply. In addition to that, a study case based on proportional integral derivative (PID) controller is carried out to further examine performance behaviour of the proposed mathematical model and the validity of computational specifications for brake system design as well as the PMDC motor. Simulation analysis and investigation which are conducted in MATLAB software revealed efficient operating brake system that leads to obtain desired control signal successfully with outstanding performance. Furthermore, the result output behaviour of the plant mimics predetermined brake system analysis which ensures the validity of the proposed mathematical design.

Modeling and Control of a Dragonfly-like Micro Aerial Vehicle

The modeling and control design of a dragonfly-like flapping wing micro aerial vehicle (FWMAV) are studied in this paper. The aerodynamic force model of flapping wings is presented first, which is obtained by the local air velocity of the wing and local attack angle of the wing, unlike some existing works. Then, the complete mathematic model of FWMAV is developed by combining the aerodynamic force model and a kinematic model in which the micro aerial vehicle is regarded as a 6 degree-of-freedom rigid body. To mimic real dragonflies, the tail of the FWMAV swings only, unlike fixed-wing aircrafts that possess conventional control surfaces in tail. This yields a control difficulty due to the loss of the maneuverability in tail. To design an appropriate control mechanism, the complete FWMAV model, which is highly nonlinear, is rewritten in a companion form. The controller is designed to iteratively solve for a desired control signal profile by means of a dual-loop nonlinear dynamic inversion with Newton-Raphson solution. Numerical simulation results show that the effectiveness and convergence performance of the nonlinear controller are obtained.

Approximation-Based Adaptive Tracking Control for MIMO Nonlinear Systems With Input Saturation.

In this paper, an approximation-based adaptive tracking control approach is proposed for a class of multiinput multioutput nonlinear systems. Based on the method of neural network, a novel adaptive controller is designed via backstepping design process. Furthermore, by introducing Nussbaum function, the issue of unknown control directions is handled. In the backstepping design process, the dynamic surface control technique is employed to avoid differentiating certain nonlinear functions repeatedly. Moreover, in order to reduce the number of adaptation laws, we do not use the neural networks to directly approximate the unknown nonlinear functions but the desired control signals. Finally, we provide two examples to illustrate the effectiveness of the proposed approach.

Adaptive fuzzy output-feedback controller design for nonlinear systems via backstepping and small-gain approach.

This paper focuses on an input-to-state practical stability (ISpS) problem of nonlinear systems which possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances. The dynamic disturbances depend on the states and the measured output of the system, and its assumption conditions are relaxed compared with the common restrictions. Based on an input-driven filter, fuzzy logic systems are directly used to approximate the unknown and desired control signals instead of the unknown nonlinear functions, and an integrated backstepping technique is used to design an adaptive output-feedback controller that ensures robustness with respect to unknown parameters and uncertain nonlinearities. This paper, by applying the ISpS theory and the generalized small-gain approach, shows that the proposed adaptive fuzzy controller guarantees the closed-loop system being semi-globally uniformly ultimately bounded. A main advantage of the proposed controller is that it contains only three adaptive parameters that need to be updated online, no matter how many states there are in the systems. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples.

Robust Adaptive Fuzzy Control of Nonlinear Systems with Unknown and Time‐Varying Saturation

AbstractIn this paper, a robust adaptive fuzzy control approach is proposed for a class of nonlinear systems in strict‐feedback form with the unknown time‐varying saturation input. To deal with the time‐varying saturation problem, a novel controller separation approach is proposed in the literature to separate the desired control signal from the practical constrained control input. Furthermore, an optimized adaptation method is applied to the dynamic surface control design to reduce the number of adaptive parameters. By utilizing the Lyapunov synthesis, the fuzzy logic system technique and the Nussbaum function technique, an adaptive fuzzy control algorithm is constructed to guarantee that all the signals in the closed‐loop control system remain semiglobally uniformly ultimately bounded, and the tracking error is driven to an adjustable neighborhood of the origin. Finally, some numerical examples are provided to validate the effectiveness of the proposed control scheme in the literature.

DC Motor Speed Control Using SMS Application

Motors are normally used for industrial control, automation and home electrical appliance. It spans everything from residential washing machines, fans, hand-held power tools, automotive window lift, traction control system, industrial drives and many more. Motor application is not completed without a control system. The inventions of microprocessor and microcontroller make the control system become easier. This control system is basically controlling the switch, speed, and direction of motor. Control signal is generated by a switch that is connected directly to the control circuit. In order to control and monitor the motor, user need to be at the place where the switch is located. In this system, mobile phone is used as a control switch. By sending text message using short message service (SMS) which is a part of the Global System for Mobile Communications (GSM), control signal is sent wirelessly to the control circuit. By implementing GSM module on the control circuit of motor, it can enable the user to send text message that contain command from any mobile phone to that GSM module. The GSM module will receive the text command and send it to the microcontroller to be processed and converted into desired control signal. Controlling motor using SMS is really convenient and give mobility as the user is able to control and monitor the motor from anywhere as long as the places have coverage. Moreover, sending text message is considered very low cost and most of people have their own mobile phones and it already becomes one of the basic needs in life.

Biogeography Based Optimal State Feedback Controller for Frequency Regulation of a Smart Microgrid

Development of “Q” and “R” matrices for designing a Linear Quadratic Regulator (LQR) is still a research challenge. The theory says they should belong to the group of positive definite matrices, so we need to find out the most suitable amongst them in order to obtain the desired response. In this paper biogeography based optimization (BBO) technique has been applied to come up with the best “Q” and “R” matrices such that the frequency excursion following a disturbance in a microgrid is minimized. As all the states in a practical system may not be measurable hence, we have used Kalman estimator to estimate them. These estimated states along with other measured states are used by the LQR to produce the desired control signal. The microgrid is made smarter by using the agent based scheme integrated with a master controller and a proper communication protocol. The simulation results show that the proposed approach improves the microgrid frequency response and also gives a new alternative method for frequency control of a smart microgrid.

Optimal Tracking Control of Motion Systems

Tracking control of motion systems typically requires accurate nonlinear friction models, especially at low speeds, and integral action. However, building accurate nonlinear friction models is time consuming, friction characteristics dramatically change over time, and special care must be taken to avoid windup in a controller employing integral action. In this paper a new approach is proposed for the optimal tracking control of motion systems with significant disturbances, parameter variations, and unmodeled dynamics. The ‘desired’ control signal that will keep the nominal system on the desired trajectory is calculated based on the known system dynamics and is utilized in a performance index to design an optimal controller. However, in the presence of disturbances, parameter variations, and unmodeled dynamics, the desired control signal must be adjusted. This is accomplished by using neural network based observers to identify these quantities, and update the control signal on-line. This formulation allows for excellent motion tracking without the need for the addition of an integral state. The system stability is analyzed and Lyapunov based weight update rules are applied to the neural networks to guarantee the boundedness of the tracking error, disturbance estimation error, and neural network weight errors. Experiments are conducted on the linear axes of a mini CNC machine for the contour control of two orthogonal axes, and the results demonstrate the excellent performance of the proposed methodology.

Direct adaptive neural control of chaos in the permanent magnet synchronous motor

In this paper, a direct adaptive neural speed tracking control is addressed for the chaotic permanent magnet synchronous motor (PMSM) drive systems via backstepping. Neural networks are directly used to approximate unknown and desired control signals and a novel direct adaptive tracking controller is constructed via backstepping. The proposed adaptive neural controllers guarantee that the tracking error converges to a small neighborhood of the origin. Compared with the conventional backstepping method, the designed neural controller’s structure is very simple. Simulation results show that the proposed control scheme can suppress the chaos of PMSM and guarantees the perfect tracking performance even with the existence of unknown parameters.