Fixed-gain Controller Research Articles

Evaluation of supply air temperature control performance with different control strategies at air handling units

Due to its nonlinear feature, supply air temperature control systems at air handling units using single-loop fixed gain control frequently suffer unstable response under dynamic conditions. This paper is aimed at identifying a more effective control strategy to stabilize the valve control and achieve desirable supply air temperature response under various operation conditions by evaluating three control strategies, cascade, gain scheduling and cascade plus gain scheduling controls. Firstly, a control-oriented model is developed and calibrated through open-loop tests. Then, the control issues with single-loop fixed gain control are evaluated through simulations and field tests, which indicates that the system has more aggressive response or becomes unstable under lower load conditions when the chilled-water flow rate is less than 0.1 L/s. Finally, three control strategies are proposed and evaluated. The results reveal that all three proposed controls can achieve better robustness with the integral square error of supply air temperature decreased to 16%, 47% and 5%, respectively, under lower load condition. However, since high-frequency oscillation of the control valve occurs with cascade and cascade plus gain scheduling control, the gain scheduling control is recommended considering a trade-off among the supply air temperature response, valve lifetime and implementation complexity.

Model-Assisted Online Optimization of Gain-Scheduled PID Control Using NSGA-II Iterative Genetic Algorithm

In the practical control of nonlinear valve systems, PID control, as a model-free method, continues to play a crucial role thanks to its simple structure and performance-oriented tuning process. To improve the control performance, advanced gain-scheduling methods are used to schedule the PID control gains based on the operating conditions and/or tracking error. However, determining the scheduled gain is a major challenge, as PID control gains need to be determined at each operating condition. In this paper, a model-assisted online optimization method is proposed based on the modified Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) to obtain the optimal gain-scheduled PID controller. Model-assisted offline optimization through computer-in-the-loop simulation provides the initial scheduled gains for an online algorithm, which then uses the iterative NSGA-II algorithm to automatically schedule and tune PID gains by online searching of the parameter space. As a summary, the proposed approach presents a PID controller optimized through both model-assisted learning based on prior model knowledge and model-free online learning. The proposed approach is demonstrated in the case of a nonlinear valve system able to obtain optimal PID control gains with a given scheduled gain structure. The performance improvement of the optimized gain-scheduled PID control is demonstrated by comparing it with fixed-gain controllers under multiple operating conditions.

Improving the nonlinear control performance of the supply fan at air handling units using a gain scheduling control strategy

Due to its nonlinear nature, the supply fan at air handling units with the controller tuned at the design condition tends to be aggressive and oscillate under partial load conditions. The objective of this paper is to develop and validate a gain scheduling control strategy to improve its nonlinear control performance. First, a control-oriented model, which does not require numerous physical parameters and extensive test data, is developed to study the nonlinearity of the fan system. Based on the theoretical model and experimental verifications, the issue of an aggressive response with a conventional fixed-gain controller is caused by the fact that the system gain is proportional to the ratio of the duct static pressure to the fan speed. To address the issue, a scheduling function of the measurable duct static pressure and fan speed is proposed to be included in the conventional fixed-gain controller to compensate for the fan system gain variation. The gain scheduling control strategy is found to approximately maintain the identical control performance under all operation conditions. Most importantly, the gain scheduling control strategy can be readily implemented without intensive computation and additional measurements, showing a promising potential in industrial applications.

PSO Self-Tuning Power Controllers for Low Voltage Improvements of an Offshore Wind Farm in Taiwan

A de-loaded real power control strategy is proposed to decrease the real power output and increase the reactive power output of a grid-connected offshore wind farm in order to improve the voltage profile when the wind farm is subject to a grid fault. A simplified linear model of the wind farm is first derived and a fixed-gain proportional-integral (PI) real power controller is designed based on the pole-zero cancellation method. To improve the dynamic voltage response when the system is subject to a major disturbance such as a three-phase fault in the grid, a self-tuning controller based on particle swarm optimization (PSO) is proposed to adapt the PI controller gains based on the on-line measured system variables. Digital simulations using MATLAB/SIMULINK were performed on an offshore wind farm connected to the power grid in central Taiwan in order to validate the effectiveness of the proposed PSO controller. It is concluded from the simulation results that a better dynamic voltage response can be achieved by the proposed PSO self-tuning controller than the fixed-gain controller when the grid is subject to a three-phase fault. In addition, low voltage ride through (LVRT) requirements of the local utility can be met by the wind farm with the proposed power controller.

Adaptive region tracking control for underwater vehicles with large initial deviation and general uncertainty

A region tracking control system is developed for underwater vehicles with large initial deviation and general uncertainty. The developed system is a nonlinear cascaded system, consisting of two subsystems in series. The fixed-gain controller of the first subsystem is designed to compensate for the region tracking error caused by the large initial deviation. In the second subsystem, the radial basis function neural network is adopted to approximate the general uncertainty along with the external disturbance and modelling uncertainty. According to the Lyapunov theory, the control law of the two subsystems and adaptive law of the second subsystem are derived to ensure the region tracking errors asymptotically converge to zero. The validity of the control system is verified by a series of comparisons on simulation results.

Self-tuning state-feedback control of a rotary pendulum system using adjustable degree-of-stability design

This paper formulates an original hierarchical self-tuning control procedure to enhance the disturbance-rejection capability of under-actuated rotary pendulum systems against exogenous disturbances. The conventional state-feedback controllers generally make a trade-off between the robustness and control effort in a closed-loop system. To combine the aforementioned characteristics into a single framework, this paper contributes to develop and augment the baseline Linear-Quadratic-Regulator (LQR) with a novel “adjustable degree-of-stability design” module. This augmentation dynamically relocates the system's closed-loop poles in the stable (left-half) region of the complex plane by dynamically adjusting a single hyper-parameter that modifies the constituents of LQR's performance index. The hyper-parameter is adaptively modulated online via a pre-calibrated hyperbolic-secant-function that is driven by state-error variables. The performance of the proposed adaptive controller is benchmarked against fixed-gain controllers via credible hardware experiments conducted on the standard QNET Rotary Pendulum setup. The experimental outcomes indicate that the proposed controller significantly enhances the system's robustness against exogenous disturbances and maintains its stability within a broad range of operating conditions, without inducing peak servo requirements.

Quantitative Feedback Theory Velocity Control of a Single-rod Pump-controlled Actuator Using a Novel Flow Compensation Circuit

This paper employs the quantitative feedback theory (QFT) to design a robust fixed-gain linear velocity controller for a newly developed single-rod pump-controlled actuator. The actuator operates in four quadrants, with a load force becoming resistive or assistive alternatively. The controller also satisfies tracking, stability and sensitivity specifications in the presence of a wide range of system parametric uncertainties. Its performance is examined on an instrumented John Deere JD-48 backhoe. The experimental results show that the controller can maintain the actuator velocity within an acceptable response envelope, despite variation in load mass as high as 163 kg and the hydraulic circuit switching between operating quadrants.

WITHDRAWN: Analysis, design and implementation of hardware neural network based rotor oscillations compensator

In this paper, an Artificial Neural Network (ANN) based Rotor Oscillations Compensator (ROC) is proposed and its equivalent Hardware Realizable Neural Network (HARNN) has been developed on the Field Programmable Gate Array (FPGA) board. The basic function of a ROC is to minimize the low frequency electro mechanical oscillations created in the Electric power network. Whenever the fault is occurred outside the operating range of a fixed gain ROC, the system damping is insufficient and the machine is still under unstable mode. Hence a hardware having sufficient “intelligence” that is capable of producing necessary and sufficient damping to the system is proposed in this paper. A detailed study has been done with the plant response without any controller, with a fixed gain controller and a FPGA based ANN controller. The studies revealed that when an intelligent controller has been included in the Exciter loop, the oscillations have been eliminated more effectively. But it is also observed that the realization of an ANN absolutely on an FPGA is having its own design limitations. The ARTIX 7 FPGA development board has been used for the complete analysis and the results and outcomes discussed at the end.

Neural Network-Based Supplementary Frequency Controller for a DFIG Wind Farm

An artificial neural network (ANN)-based supplementary frequency controller is designed for a doubly fed induction generator (DFIG) wind farm in a local power system. Since the optimal controller gain that gives highest the frequency nadir or lowest peak frequency is a complicated nonlinear function of load disturbance and system variables, it is not easy to use analytical methods to derive the optimal gain. The optimal gain can be reached through an exhaustive search method. However, the exhaustive search method is not suitable for online applications, since it takes a long time to perform a great number of simulations. In this work, an ANN that uses load disturbance, wind penetration, and wind speed as the inputs and the desired controller gain as the output is proposed. Once trained by a proper set of training patterns, the ANN can be employed to yield the desired gain in a very efficient manner, even when the operating condition is not included in the training set. Therefore, the proposed ANN-based controller can be used for real-time frequency control. Results from MATLAB/SIMULINK simulations performed on a local power system in Taiwan reveal that the proposed ANN can yield a better frequency response than the fixed-gain controller.

Fuzzy Self-Tuning of Strictly Negative-Imaginary Controllers for Trajectory Tracking of a Quadcopter Unmanned Aerial Vehicle

Robustness in the face of uncertainties is an integral part of designing a real-time control system. Based on negative imaginary (NI) systems theory, we design robust and adaptive control systems for accurate trajectory tracking of a quadcopter aerial vehicle. Considering the challenging dynamics of unmanned aerial vehicles, we employ knowledge-based fuzzy inference systems (FIS) to facilitate automatic tuning in our SNI controllers, leading to the development of adaptive SNI control systems. Unlike fixed-gain controllers that have no ability to adapt to the variations in environmental conditions or changes in the dynamics of the plant, our adaptive SNI controllers are able to perform self-tuning to constantly update their parameters. The concept of adaptive autopilots will enhance the ability of the closed-loop control systems to accommodate large uncertainties. To demonstrate their efficacy, we design and implement our adaptive SNI controllers in the three-position control loops of the AR.Drone quadcopter after conducting extensive computer simulations. We also perform a rigorous comparative study with respect to the performance of fixed-gain SNI controllers, fixed-gain NI systems, in addition to model-predictive-control systems, and proportional integral derivative (PID) control systems as our benchmarks. To complete the study, we conduct a stability analysis based on Kharitonov's Theorem.

Online adaptive PID tracking control of an aero-pendulum using PSO-scaled fuzzy gain adjustment mechanism

This article is centered on the development of a robust position control and disturbance compensation strategy for a mechatronic aero-pendulum using the soft computing paradigm. The pendulum arm is rotated about its pivot via the thrust generated by two coaxial contra-rotating motorized propellers installed at its free end. The tracking error in arm’s angular position is fed to a multi-loop feedback controller. The proportional–integral–derivative (PID) controller, in the outer loop, stabilizes the arm at the reference position. The reference current control signals generated by the PID position controller are fed to two PI controllers, in the inner loop, that are responsible for regulating the current consumption of each motorized propeller. Initially, the fixed PID controller gains are evaluated by selecting the optimal value of the system’s closed-loop pole using the particle swarm optimization (PSO) algorithm. However, to mitigate the inefficacies of fixed gain controller and further enhance the system’s robustness against bounded exogenous disturbances and damping against oscillations, the closed-loop pole is dynamically adjusted via fuzzy inference system, after every sampling interval. The fuzzy membership functions are calibrated offline via PSO algorithm. The superior time optimal control behavior rendered by the proposed controller is validated by comparing its performance with fixed gain controller via credible real-time experiments.

Robust Model Predictive Controller Applied to Three-Phase Grid-Connected LCL Filters

Grid-connected inverters play an important role in renewable energy system nowadays, serving as an interface between the renewable energy source and the grid. However, the PWM modulation used to control the converters produces harmonic content that must be filtered properly. In this paper, an active damping strategy is used with a three-phase power converter with an LCL filter to achieve harmonic rejection. The control strategy which will be used is a continuous control set model predictive control (CCS-MPC) based on a state-space model of the system. This controller must ensure that the injected grid currents track sinusoidal references and reject harmonic disturbances from the grid voltage. This is achieved by using an augmented model of the system that contains resonant controllers. Following the unconstrained CCS-MPC methodology, a fixed gain controller that can be implemented similarly to a classical state-space feedback controller is obtained. An analysis of the impact of the CCS-MPC tuning parameters on the closed-loop response is made. Also, an a posteriori linear matrix inequality approach is used to show that the resulting closed-loop system is robust in regard to grid inductance uncertainties and variations. Simulation and experimental test results show that the proposed controller yields good results, complying with the IEEE 1547 Standard grid currents harmonic limits.

Robust stabilisation of rotary inverted pendulum using intelligently optimised nonlinear self-adaptive dual fractional-order PD controllers

ABSTRACTThis article presents an intelligently optimised self-tuning fractional-order control scheme to improve the attitude-stabilisation of an inverted pendulum. Primarily, the scheme employs two Fractional-order Proportional-Derivative (FPD) controllers acting concurrently on the system to minimise the deviations in its state-trajectories. Wherein, one FPD controller compensates the variations in pendulum-angle and its fractional-order derivative to vertically balance the pendulum, where as the other FPD controller acts as a position controller and regulates the variations in arm-angle and its fractional-order derivative. The integration of fractional calculus with conventional PD controllers optimises the reference-tracking performance of the control scheme by increasing its degrees-of-freedom and design flexibility. In order to further improve the system’s immunity against exogenous disturbances, the PD gains of each controller are dynamically adjusted after each sampling interval using piecewise nonlinear functions of their respective state-variations. The hyper-parameters of the nonlinear gain-adjustment functions as well as the fractional-number power of the derivative-operator of each controller are selected via Particle-Swarm-optimisation (PSO) algorithm. The proposed adaptive control scheme is tested on the QNET Rotary Inverted Pendulum setup via ‘hardware-in-the-loop’ experiments. The optimality and robustness of the proposed control scheme are validated by comparing its performance with PSO-based fixed-gain dual-PD and dual-FPD control schemes.

Circular Formation Algorithms for Multiple Nonholonomic Mobile Robots: An Optimization-Based Approach

This paper revisits the circular formation control problem proposed in [G. S. Seyboth, J. Wu, J. Qin, C. Yu, and F. Allgower, “Collective circular motion of unicycle type vehicles with nonidentical constant velocities,” IEEE Transactions on Control of Network Systems , vol. 1, no. 2, pp. 167–176, Jun. 2014.] for multiple nonholonomic mobile robots with nonidentical constant forward speeds. Specifically, we study two types of circular formations: First, a circular motion with synchronized/balanced phase configuration; and second, a concentric circular formation with both circular orbits control and phase synchronization/balancing. Existing works on the above problems utilize a fixed-gain control input, which increases the workload of the engineers for selecting favorable algorithm parameters. To reduce this workload, we study the above problems from a new perspective by utilizing optimization methods, based on which two variable-gain control algorithms are proposed such that much greater flexibility on the selection of algorithm parameters is acquired. The global convergence properties of the proposed algorithms are analyzed under some mild assumptions. Both simulations and field experiments are presented to validate the effectiveness of the proposed formation algorithms.

Adaptive control technique for suppression of resonance in grid‐connected PV inverters

Grid operating conditions have a significant effect on the harmonic and resonant performance of grid-connected photovoltaic (PV) inverters and changes in grid impedance can cause a notable change in the resonant excitation between the PV inverter and the grid. This study proposes an adaptive control algorithm for grid-connected PV inverters to suppress the resonance condition excited by grid inductance variation, resulting from the dynamic changes in the operating conditions of the distribution network. The causes of resonance between grid-connected PV inverters and the distribution grid are discussed and the design of an active band-pass filter for capturing resonance is described. The proportional gain within the proportional-integral controller is then adaptively controlled in real time to compensate for changes in the grid impedance and suppress resonant excitation while maintaining excellent low-order harmonic performance compared with alternative fixed gain controller techniques, particularly for systems with high values of grid inductance. The performance of the proposed controller is experimentally verified using a 240 V, 2 kVA single-phase grid-connected inverter.

Improving short-term retention after robotic training by leveraging fixed-gain controllers.

IntroductionWhen developing control strategies for robotic rehabilitation, it is important that end-users who train with those strategies retain what they learn. Within the current state-of-the-art, however, it remains unclear what types of robotic controllers are best suited for promoting retention. In this work, we experimentally compare short-term retention in able-bodied end-users after training with two common types of robotic control strategies: fixed- and variable-gain controllers.MethodsOur approach is based on recent motor learning research, where reward signals are employed to reinforce the learning process. We extend this approach to now include robotic controllers, so that participants are trained with a robotic control strategy and auditory reward-based reinforcement on tasks of different difficulty. We then explore retention after the robotic feedback is removed.ResultsOverall, our results indicate that fixed-gain control strategies better stabilize able-bodied users’ motor adaptation than either a no controller baseline or variable-gain strategy. When breaking these results down by task difficulty, we find that assistive and resistive fixed-gain controllers lead to better short-term retention on less challenging tasks but have opposite effects on the learning and forgetting rates.ConclusionsThis suggests that we can improve short-term retention after robotic training with consistent controllers that match the task difficulty.

Controller Design and Stability Analysis of Output Pressure Regulation in Electrohydrostatic Actuators

In this paper, a robust fixed-gain linear output pressure controller is designed for a double-rod electrohydrostatic actuator using quantitative feedback theory (QFT). First, the family of frequency responses of the system is identified by applying an advanced form of fast Fourier transform on the open-loop input–output experimental data. This approach results in realistic frequency responses of the system, which prevents the generation of unnecessary large QFT templates, and consequently contributes to the design of a low-order QFT controller. The designed controller provides desired transient responses, desired tracking bandwidth, robust stability, and disturbance rejection for the closed-loop system. Experimental results confirm the desired performance met by the QFT controller. Then, the nonlinear stability of the closed-loop system is analyzed considering the friction and leakage, and in the presence of parametric uncertainties. For this analysis, Takagi–Sugeno (T–S) fuzzy modeling and its stability theory are employed. The T–S fuzzy model is derived for the closed-loop system and the stability conditions are presented as linear matrix inequalities (LMIs). LMIs are found feasible and thus the stability of the closed-loop system is proven for a wide range of parametric uncertainties and in the presence of friction and leakages.

LMI-based design of an I-PD+PD type LPV state feedback controller for a gantry crane

In this paper, an alternative gain-scheduled PID tuning procedure is proposed for gantry crane control systems. In order to avoid excessive overshoot and aggressive control action due to the proportional kick and/or derivative kick effects, an I-PD+PD type control law is considered. The scheduling parameter is considered the cable length due to the payload lifting and lowering movements. A linear parameter-varying (LPV) gantry crane model is constructed to enable gain-scheduled controller design with a linear matrix inequalities (LMIs) framework. Based on the LPV model, a convex optimization problem is formulized to minimize L2 gain under regional pole location constraints. Then, a fixed gain L2 gain state feedback I-PD+PD type controller and a conventional pole placement state feedback I-PD+PD controller are designed to investigate the efficiency of the proposed controller. A pole placement controller is tuned to minimize the very common ITAE (integral of time multiplied by absolute error) performance index. Simulation results show that the proposed controller has superior tracking performance under time-varying cable length, when compared with nominal fixed gain controllers.

A gain scheduled robust linear quadratic regulator for vehicle direct yaw moment Control

Direct yaw moment controllers improve vehicle stability and handling in severe manoeuvres. In direct yaw moment control implementations based on Linear Quadratic Regulators (LQRs), the control system performance is limited by the unmodelled dynamics and parameter uncertainties. To guarantee robustness with respect to uncertainties, this paper proposes a gain scheduled Robust Linear Quadratic Regulator (RLQR), in which an extra control term is added to the feedback contribution of a conventional LQR to limit the closed-loop tracking error in a neighbourhood of the origin of its state-space, despite the uncertainties and disturbances acting on the plant. In addition, the intrinsic parameter-varying nature of the vehicle dynamics model with respect to the longitudinal vehicle velocity can compromise the closed-loop performance of fixed-gain controllers in varying driving conditions. Therefore, in this study the control gains optimally vary with velocity to adapt the closed-loop system to the variations of this parameter. The effectiveness of the proposed RLQR in improving the robustness of a classical LQR against model uncertainties and parameter variations is proven analytically, numerically and experimentally. The simulation and vehicle test results are consistent with the formal analysis proving that the RLQR reduces the ultimate bound of the error dynamics.

Simplified Decoupler-Based Multivariable Controller With a Gain Scheduling Strategy for the Exhaust Gas Recirculation and Variable Geometry Turbocharger Systems in Diesel Engines

This paper presents a simplified decoupler-based multivariable controller with a gain scheduling strategy in order to deal with strong nonlinearities and cross-coupled characteristics for exhaust gas recirculation (EGR) and variable geometry turbocharger (VGT) systems in diesel engines. A feedback controller is designed with the gain scheduling strategy, which updates control gains according to engine operating conditions. The gain scheduling strategy is implemented by using a proposed scheduling variable derived from indirect measurements of the EGR mass flow, such as the pressure ratio of the intake, exhaust manifolds, and the exhaust air-to-fuel ratio. The scheduling variable is utilized to estimate static gains of the EGR and VGT systems; it has a large dispersion in various engine operating conditions. Based on the estimated static gains of the plant, the Skogestad internal model control (SIMC) method determines appropriate control gains. The dynamic decoupler is designed to deal with the cross-coupled effects of the EGR and VGT systems by applying a simplified decoupler design method. The simplified decoupler is beneficial for compensating for the dynamics difference between two control loops of the EGR and VGT systems, for example, slow VGT dynamics and fast EGR dynamics. The proposed control algorithm is evaluated through engine experiments. Step test results of set points reveal that root-mean-square (RMS) error of the gain-scheduled feedback controller is reduced by 47% as compared to those of the fixed gain controller. Furthermore, the designed simplified decoupler decreased the tracking error under transients by 14–66% in various engine operating conditions.