Modern Control Techniques Research Articles

Hybrid Position and Force Control for Upper Limb Rehabilitation of Stroke Patient

This paper presents hybrid position and force control (HPFC) methodology for position and as well as force tracking for upper limb rehabilitation of stroke patients. Stroke is a leading cause of disability in humans. Traditional rehabilitative therapies help regain motor function and ameliorate impairment, but they depend on the therapist’s experience and require many therapists, which is cost-prohibitive. Most robotic tasks with high severity such as rehabilitation, demands an effective force as well as position control scheme to ensure the safe physical contact between the robot and its environment in this case the patient. Some rehabilitation robots have been developed to help stroke survivors recover motor function. In past robots used for upper-limb rehabilitation employed general control schemes such as proportional integral derivative. To overcome the problem of force tracking in rehabilitation, robots require modern control techniques. To mimic the human upper-limb, and universality of application, an end-effector based robot is used for this study. To ensure the convergence of position and force errors to zero extensive simulations are performed. Two of assistant modes passive and active assistive rehabilitation are considered. Most common rehabilitation trajectories horizontal reaching, and vertical reaching are selected as robot’s motion for both passive and active assistive activities. The mathematical model of robots’ kinematics, dynamics, alongside the proposed control scheme has been discussed in detail.

Automatic mass balancing of spacecraft attitude simulators by optimal feedback control

There is an increasing interest in development of spacecraft attitude simulators. A necessary and challenging task in preparation for any attitude control experiment is to remove the gravitational disturbances on the simulator by a mass balancing system which relocates the center of mass to coincide with the center of rotation. This research is aimed at filling a substantial gap in the literature by presenting a methodical framework for analysis and design of closed-loop automatic mass balancing systems. In this regard, a state-space representation for imbalanced dynamics of attitude simulators is derived to enable the application of modern control techniques to the problem which will be shown to be very effective in achieving an accurate balancing. The model is further enhanced by state augmentation and incremental control input to address the practical requirements for an efficient balancing strategy. Next, an optimal feedback control is designed on the enhanced dynamics model to minimize the rotational kinetic energy and leveling accuracy as key performance measures of the problem. The simulation results and comparative evaluations with a previously validated method verified that the proposed modeling and control approach is effective in achieving an accurate mass balancing while providing a convenient and methodical analysis and design procedure.

Modeling and control of boiler in thermal power plant using model reference adaptive control

The boiler is a multi-variable system, which is very difficult to control due to its nonlinear behavior, uncertainties, interactions between variables, and unmeasured and frequent disturbances. Instead of conventional control techniques, modern control techniques are being implemented in most boilers by industries. Mathematical modeling is a useful tool to analyze a complex system’s performance and design a controller for the same. The mathematical model is derived from the open-loop data obtained from the process station. The mathematical equation is then derived using the decoupling technique in terms of transfer function. An adaptive controller is designed and implemented for the model and the simulation study for the same is carried out using MATLAB. The proposed method discussed in the paper can adjust the controller parameters in response to changes in plant and disturbance in real-time by referring to the reference model that specifies the properties of the desired control system.

Contemporary Patterns and Progressions in Topologies of DC–DC Converters with Control Methodology

DC–DC converters have achieved great popularity in recent decades due to their immense penetration in various applications. With this motivation, the authors have conducted a thorough review of recent advancements in various topologies of DC–DC converters. The need for DC power has raised further for certain applications like grid integration of distributed generation (DG), solar photovoltaic (PV), wind power generation (WPG), fuel cells (FC), etc. The investigation of converter topology is performed to achieve the desired objective of a specific application. Like in a PV system, to obtain the inherent capability of a DC–DC converter for operating at the maximum power point (MPP) and thereby electronically extracting the maximum power from the source. Hence, a detailed review of topological advancements on the low to medium-voltage and medium-to-high-power DC–DC converters has been carried out. Moreover, a thorough investigation has been carried out on profuse closed-loop strategies and compared with each other for obtaining the optimum or maximum output performance and thereby obtaining the utmost source utilization. The modern control techniques though have relatively more calculation time but, they tend to reduce the steady-state error that leads to the stabilization of the converter. Lastly, certain applications of the DC–DC converters have been explained to get an overall idea of the usefulness of such power converters.

On Accurate Discrete-Time Dynamic Models of an Induction Machine

Induction machines have become the standard for highly demanding industrial applications. This has led to the utilization of modern discrete-time control techniques (such as model predictive control) that require the estimation of internal variables that are not subject to measurement (such as the rotational velocity in sensorless applications). From this point of view, it is fundamental to have accurate discrete-time models of induction machines, particularly given their nonlinear nature, so that control techniques perform according to design requirements. In spite of the above, the modeling of induction machines has not received much attention in the literature, even though more powerful machines and faster microcontrollers are currently being used. To better understand induction machine models for control, in this paper, we develop and compare various discrete-time models of induction machines based on Euler, Taylor, and Runge–Kutta methods. In addition, we compare the Extended Kalman Filter and Unscented Kalman Filter for state estimation in terms of accuracy and computational burden. The models are derived and compared through extensive Monte Carlo simulations and the state estimation techniques are compared in terms of root mean squared error, execution time, and maximum absolute error. Our simulations show that, in general, the Taylor method yields more accurate models than both the Runge–Kutta and Euler methods. In particular, the Taylor method results in a root mean square error that is one order of magnitude smaller than the Euler method for stator current and rotor flux linkages. For rotor angular speed, the Runge–Kutta methods are more accurate than both the Taylor and Euler methods, resulting in a root mean square error that is two orders of magnitude smaller than the Euler method. On the other hand, the Extended Kalman Filter results in smaller execution time than the Unscented Kalman Filter, up to two orders of magnitude. In terms of root mean squared error and maximum absolute error, both filtering algorithms perform similarly.

Model predictive controller for path following ships validated by experimental model tests

This paper introduces a practical model predictive controller for surface ships’ path following, with validation through experimental tests. Model predictive control (MPC) is a state-of-the-art modern control technique, providing local optimal control action based on prediction and reference. In this approach, a simple predictive model based upon the Nomoto model is employed to forecast the (near) future ship positions, while the Serret-Frenet frame and the Spline path function are used to generate a smooth ship speed-based reference. Two optimizers, namely the Spline optimizer and the Cobyla (Constrained Optimization by Linear Approximation) optimizer, are used to retrieve an optimal solution that minimizes the difference between the prediction and the reference. To achieve a real-time control feedback, the optimization variables are reduced significantly to lower calculation time by assuming a constant rudder angle throughout the prediction horizon, which is consistent with a realistic human controlled manoeuvre. In addition, an adaptive prediction horizon strategy is proposed to balance overshoot and accuracy.A series of free running model tests were conducted in the Towing Tank for Manoeuvres in Shallow Water (co-operation Flanders Hydraulics – Ghent University) in Ostend, Belgium, in order to evaluate the effectiveness and performance of the proposed controller, and to explore the impact of its parameter settings. The 20 m wide towing tank provides ample space to design complex paths for the ship to follow, which poses a significant challenge for the controller. Essential parameters in MPC, including weight factor for heading error, parameters of predictive model, prediction horizon, gain of adaptive prediction horizon and optimizer, are described in detail and their effects on the control outcomes are discussed with analysis of experimental results. The results demonstrate excellent control performance of the MPC, i.e. high tracking accuracy, smooth track transition, insensitivity to parameter setting and strong robustness in the presence of inherent model uncertainty, despite the simplifications made for practicality.

A Comprehensive Examination of Vector-Controlled Induction Motor Drive Techniques

This paper introduces a comprehensive examination of vector-controlled- (VC-) based techniques intended for induction motor (IM) drives. In addition, the evaluation and critique of modern control techniques that improve the performance of IM drives are discussed by considering a systematic literature survey. Detailed research on variable-speed drive control, for instance, VC and scalar control (SCC), was conducted. The SCC-based systems’ speed and V/f control purposes are clarified in closed and open loops of IM drives. The operations, benefits, and drawbacks of the direct and indirect field-oriented control systems are illustrated. Furthermore, the direct torque control (DTC) method for IMs is reviewed. Numerous VC methods established along with microprocessor/digital control, model reference adaptive control (MRAC), sliding mode control (SMC), and intelligent control (in terms of fuzzy logic (FL) and artificial neural networks (ANNs)) are described and examined. Uncertainties in the IM parameter are a considerable problem in VC drives. Therefore, this problem is addressed, and some studies that attempted to provide solutions are listed. Magnetic saturation and core loss impact are mentioned, as they are important issues in IM drives. Toward demonstrating the strengths and limitations of various VC configurations, a few experiments were simulated via MATLAB® and Simulink® that show the influence of machine parameter variation. Efforts are made to supply powerful guidelines for practicing engineers and researchers in AC drives.

Observer-Based Integral Sliding Mode Controller for a Two-Wheeled Inverted Pendulum

The modeling and control of a two-wheeled inverted pendulum (TWIP) is gaining much interest due to the various advancements in hardware and computing technologies. A TWIP system has many advantages and applications. However, it still faces many challenges such as positioning, disturbance rejection, parameter uncertainties, etc. In this paper, a control scheme based on modern control techniques is presented to overcome these issues. Furthermore, an observer-based robust integral sliding mode controller (ISMC) is proposed for the nonlinear TWIP system. Firstly, a robust integral sliding mode controller is designed to tackle short-term and long-term constant and time-varying disturbances as well as issues relating to parameter variation. Secondly, the reduced-order observer is designed to estimate immeasurable states, enabling simplified implementation. It is then applied to the nonlinear model of the TWIP system, and performance is observed under different transient conditions. A comparison with prevalent controllers in the literature is carried out. This comparison relates to graphical results, time domain specifications, and error performance indices, calculated for states. From this, a significant qualitative improvement can be observed with the proposed controller for all types of transients. In quantitative terms, zero steady-state error with disturbances and a five-fold improvement in settling time with parameter variations are observed. This paper also discusses the software Simulink realization for modeling and control of the TWIP system, which can be valuable for novices working in this area.

Smart Grid Communication Networks for Electric Vehicles Empowering Distributed Energy Generation: Constraints, Challenges, and Recommendations

Modern communication networks and digital control techniques are used in a smart grid. The first step is to classify the features of several communication networks and conduct a comparative investigation of the communication networks applicable to the smart grid. The integration of distributed generation has significantly increased as the global energy demand rises, and sustainable energy for electric vehicles and renewable energies worldwide are being pursued. Additional explanations for this surge include environmental concerns, the reforming of the power sector, and the advancing of small-scale electricity generation technologies. Smart monitoring and control of interconnected systems are required to successfully integrate distributed generation into an existing conventional power system. Electric-vehicles-based smart grid technologies are capable of playing this part. Smart grids are crucial to avoid becoming locked in an obsolete energy infrastructure and to draw in new investment sources and build an effective and adaptable grid system. To achieve reliability and high-quality power systems, it is also necessary to apply intelligent grid technologies at the bulk power generation and transmission levels. This paper presents smart grid applicable communication networks and electric vehicles empowering distributed generation systems. Additionally, we address some constraints and challenges and make recommendations that will give proper guidelines for academicians and researchers to resolve the current issues.

An FPGA Hardware-in-the-Loop Approach for Comprehensive Analysis and Development of Grid-Connected VSI System

Power electronic converters are used for an efficient and controlled conversion of power generated from renewable energy sources and can interface generated power to the grid. Among available power converters, voltage source inverters (VSIs) have been widely employed for grid-connected applications due to better controllability with higher efficiency. Although various conventional, as well as modern control techniques, have been developed for grid connected VSI system, there is a need to select suitable control technique based on application and control requirements. Hardware-in-the-loop (HIL) is considered as a realistic approach for the development of system and control due to the inclusion of an actual hardware system. In this paper, a HIL approach is adopted for the comprehensive analysis and development of a grid connected VSI system using a field programmable gate array (FPGA). The control techniques must deal with trade-off, based on the features and limitations. Therefore, a grid-connected VSI system is developed considering employment of two different conventional control techniques: hysteresis current control (HCC) and PI-based space vector modulation (PI-SVM), as well as finite state model predictive control (FS-MPC) as a modern control technique for investigation considering different parameters. All three control systems are developed through a digital simulator of Xilinx that is integrated with MATLAB-Simulink, while considering an FPGA based system development and testing through FPGA HIL co-simulation methodology.

Review on Solar Photovoltaic-Powered Pumping Systems

Water and energy are becoming more and more important in agriculture, urban areas and for the growing population worldwide, particularly in developing countries. To provide access to water it is necessary to use appropriate pumping systems and supply them with enough energy for operation. Pumps powered by solar photovoltaic energy are complex electromechanical systems that include hydraulic equipment, electrical machines, sensors, power converters, and control units. Therefore, solar photovoltaic pumping systems are associated with various fields of science and engineering. In remote, less-populated areas without electricity, where it is either challenging to connect to the grid or it is not possible, solar photovoltaic water pumping systems can play a significant role. To see whether solar photovoltaic pumping systems may be a practical, viable, and affordable method of pumping water it is necessary to study different aspects of their operation. The goal of this current article is to evaluate and outline recent research and advancement in the field of solar photovoltaic pumping systems. The major focus is on the standalone photovoltaic pumping system’s components, factors that affect system efficiency, performance evaluation, system optimization, and the potential for integration with modern control techniques. The main objective of this article is to give a broader overview of solar photovoltaic technologies for researchers, engineers, and decision-makers.

Relative Dynamics and Modern Control Strategies for Rendezvous in Libration Point Orbits

Deep space missions are recently gaining increasing interest from space agencies and industry, their maximum exponent being the establishment of a permanent station in cis-lunar orbit within this decade. To that end, autonomous rendezvous and docking in multi-body dynamical environments have been defined as crucial technologies to expand and maintain human space activities beyond near Earth orbit. Based on analytical and numerical formulations of the relative dynamics in the Circular Restricted Three Body Problem (CR3BP), a family of optimal, linear and nonlinear, continuous and impulsive, guidance and control techniques are developed for the design of end-to-end rendezvous trajectories between co-orbiting spacecraft in this multi-body dynamical environment. To this end, several modern control techniques are effectively designed and adapted to this problem, with particular emphasis on the design of low cost rendezvous manoeuvres. Finally, the designed hybrid rendezvous strategies, combining both discrete and continuous control techniques, are effectively tested and validated under several start-to-end deep space testbench mission scenarios, where their performance is compared and quantitatively assessed with a set of performance indices.

A Multivector-Based Model Predictive Current Control of PMSM Drive With Enhanced Torque and Flux Response

Model predictive current control (MPCC) is a modern current control technique with the provision to include multivariable objectives and nonlinearities. In this article, a multivector-based MPCC is proposed for the speed control of a two-level voltage source inverter (VSI) fed permanent magnet synchronous motor (PMSM) drive, to improve the steady-state (SS) torque and flux response. The multivector operation is obtained using an extended control set (ECS) that utilizes two adjacent-active voltage-space vectors (VSVs) with an appropriate distribution coefficient. However, the augmentation of the control set (CS) catalyzes an undesirable increase in the computational burden. To address this limitation, a lookup table-based VSV preselection scheme based on stator current error is employed. The VSV grouping is based on the gradient of stator current error and effectively replicates the control scheme with all ECS-VSVs. Subsequently, the amplitude of the selected optimum ECS-VSV is optimized using an average error minimization technique. The optimum duration of the ECS-VSV is determined without employing space vector modulation (SVM). Hence, the complexity of the proposed scheme is minimized, and the SS torque and flux performance are enhanced without compromising the dynamic performance of the drive. The effectiveness of the proposed scheme is verified by conducting comprehensive comparisons with conventional MPCC and recently published literature.

Optimal Plant Growth Through Thermo Mechatronic Analysis

This work is described as a proposal to apply modern control techniques and automation tools for optimal plant growth, also it was based on key agricultural strategies that were developed by ancient civilizations such as the Inca Empire. Many of them ancient techniques including the Inca engineering of andenes were forgotten or set aside through time. In this research, however, some of these key techniques are revisited to analyze and evaluate optimal plant growth using sensors and actuators that were not available in ancient civilizations. In addition, predictive and adaptive mathematical models are used for plant growth analysis of thermodynamic parameters such as temperature, humidity and potential of Hydrogen (pH). Furthermore, there were compared performances of sensors (electromechanical sensors) with designed sensors that were based in nanostructures, because of better study of the plant growth techniques.

Active Flutter Suppression of a Two-Dimensional Wing Using Linear Quadratic Gaussian Optimal Control

This paper aims to utilize the modern control theory in designing an active flutter suppression controller for a two-dimensional wing with a flap. In this study, the wing’s pitch–plunge linear aeroelastic response, in addition to the control flap motion, was modeled using Lagrange’s energy method, and Theodorsen’s classic unsteady aerodynamic theory. The derived mathematical model was represented in state-space so the modern control techniques can be implemented. The open-loop system was analyzed, and the flutter speed for a selected experimental-wing model was calculated. For model verification purposes, the open-loop simulation results were compared to an experimental study from the literature. Next, a linear quadratic Gaussian (LQG) compensator with integral action was designed and tuned using MATLAB®simulation environment to reach the best possible performance at a selected speed in the flutter region, where the results have proven excellent performance for this kind of controller in flutter suppression, as the stable flight region has successfully been extended by 29%.

A state‐of‐the‐art comprehensive review of modern control techniques for grid‐connected wind turbines and photovoltaic arrays distributed generation systems

A state‐of‐the‐art comprehensive review of modern control techniques for grid‐connected wind turbines and photovoltaic arrays distributed generation systems

Brake Current Control System Modeling Using Linear Quadratic Regulator (LQR) and Proportional integral derivative (PID)

This paper provides a comparative analysis between PID control as a classical control technique and modern control technique in the dinamometer Eddy current brakes system. Eddy current brakes is a modern braking system that requires a control system to support the braking performance. PID control is often used to be implemented but in some conditions it is less optimal. Therefore, it is necessary to develop a modern and optimal control, such as a full state feedback Linear Quadratic Regulator (LQR). The comparison of the braking time responses were simulated using Matlab/Simulink. The simulation results show that the response of LQR control is better than the PID, with Ts = 2.12 seconds, Tr = 1.18 seconds, and without overshoot. On the other side, PID control, although having Ts = 0.27 seconds and Tr = 0.18 seconds, there is still an overshoot about 0.7%.

An emotional discrete controller PSO tuned and designed for a real industrial pumping system

The application of automation techniques to water pump systems, combined with modern control techniques, has been increasing the hydraulic and energy efficiency of such systems. In this context, the objective of this work is to present an intelligent method of flow control based on Brain Emotional Learning Basic Intelligent Control (BELBIC), which will be applied to an experimental workbench of a pumping system, located in the Energy Efficiency and Energy Quality Laboratory (LEEQE) at Federal University of Pernambuco (UFPE). The parameters of this controller are optimized with a particle swarm optimization (PSO) technique with minimization of Integral Absolute Error (IAE). Initial tests were performed in a computational environment so that the system’s performance could be pre-tested, thereby the dynamics of the system was modeled from real data generated in the process. The experimental results were obtained through the implementation of this control system in a programmable logic controller (PLC), which was the device responsible for all the automation of the workbench previously mentioned. The data of this workbench were collected using a supervisory system exclusively developed for this work. These data were then used to analyze the performance of the proposed control system, which demonstrated that its behavior was efficient.

Classical and Modern gain estimation approach of PID controller for the pitch control of the RCTA aircraft

In the current age of globalization, the development of autopilots is superficial as the standard of living is improved through aerial surveillance, defense applications, and door- to-door transportation. To design the PID controller, a mathematical model is created to address the system identification for estimating longitudinal derivatives and to study the handling qualities of aircraft in order to improve piloting performance. This paper exhibits a comparative assessment between the classical closed-loop PID tuning methods like ZN, Modified ZN, Tyreus-luyben, Astrom- Hagglund and the modern control techniques like pole placement, LQR for the pitch controller design. The simulation results are displayed in the time-domain, which demonstrates the effectiveness of the approach used to design a robust controller.

A novel switched model predictive control of wind turbines using artificial neural network-Markov chains prediction with load mitigation

The existing model predictive control algorithm based on continuous control using quadratic programming is currently one of the most used modern control strategies applied to wind turbines. However, heavy computational time involved and complexity in implementation are still obstructions in existing model predictive control algorithm. Owing to this, a new switched model predictive control technique is developed for the control of wind turbines with the ability to reduce complexity while maintaining better efficiency. The proposed technique combines model predictive control operating on finite control set and artificial intelligence with reinforcement techniques (Markov Chains, MC) to design a new effective control law which allows to achieve the control objectives in different wind speed zones with minimization of computational complexity. The proposed method is compared with the existing model predictive control algorithm, and it has been found that the proposed algorithm is better in terms of computational time, load mitigation, and dynamic response. The proposed research is a forward step towards refining modern control techniques to achieve optimization in nonlinear process control using novel hybrid structures based on conventional control laws and artificial intelligence.