Gain Tuning Method Research Articles

Gate-controlled gain tuning of fully depleted silicon-on-insulator-based 1 T pixel for in-sensor white balance

White balance (WB) is a critical back-end processing function in image sensors to keep color constancy under various lighting conditions by adjusting the RGB color channel gain values. In this work, we proposed a novel gate-controlled gain-tuning method for a fully depleted silicon-on-insulator (FDSOI) one-transistor (1 T) pixel to achieve WB inside the sensor. Based on the pixel structure of a p-well under the buried oxide for sensing and an n-type nMOSFET on the top for readout, the 1 T pixel output gain is modulated by the MOSFET gate according to the transistor transfer characteristics. About 5x gain modulation range in RGB spectrum photoresponse (nonlinearity < 3%) is experimentally demonstrated in the devices fabricated by 22 nm FDSOI-based technology. The scheme for in-sensor WB demonstration is provided with a novel 1 T pixel array design, and the evaluation result shows in-sensor WB achieving an almost equivalent performance (Delta-E deviation < 1) compared with using conventional back-end WB.

Gain tuning method of closed-loop-injection low-order feedforward controller considering axis dynamics

Gain tuning method of closed-loop-injection low-order feedforward controller considering axis dynamics

Design and Performance Analysis of Hybrid Electric Vehicles using Matlab/Simulink

In this paper introduces an integrated method for the design and performance analysis of hybrid electric vehicles. This method considers a set of parameters that influence the system's performance. This project presents an approach for modelling electric vehicles considering the vehicle dynamics, drive train, rotational wheel and load dynamics. The performance of the hybrid electric vehicle is not satisfactory owing to the difficulties of optimal gain selections. To overcome this problem, a new fuzzy logic controller is required to set the rules for better performance. Therefore, in this project fuzzy logic-based gain tuning method for PID controller is proposed and compared with some previous control techniques for the better performance of electric vehicles with an optimal balance of acceleration, speed, travelling range, improved controller quality and response. The model was developed in MATLAB/Simulink, simulations were conducted, and results were observed

Novel gain-tuning for sliding mode control of second-order mechanical systems: theory and experiments

The sliding mode control is well-known as a useful control technique that can be applied in several real-world applications. However, a straightforward and efficient process of selecting the sliding mode control gains remains a challenging but interesting topic. This paper investigates a novel gain tuning method for the sliding mode control of second-order mechanical systems. Firstly, we obtain relations between the gains and the natural and damping ratio of the closed-loop system. Secondly, the time constant of the system’s actuators and the system response performance criteria, including settling time and delay time, are taken into consideration to determine appropriate ranges of the gains. These gain ranges allow control designers to select the controller gains in a time-saving manner and ensure that the desired system performance is met and the actuators work properly. Finally, the proposed method is applied to the gain tuning process of a sliding mode altitude controller for an actual quadcopter unmanned aerial vehicle. Simulation and experimental results demonstrate the applicability and effectiveness of this method.

Analysis of the Gains Tuning Problem in a Backstepping Controller Applied to an Electrohydraulic Drive

This paper highlights the problem of tuning the gains of a non-adaptive backstepping controller in an electrohydraulic servo system. While the other non-adaptive controllers in the literature have precise gains tuning methods, the non-self-tuning backstepping controller has no rigorous gain tuning method. The proposed study aims to analyze the contribution of each backstepping controller gain in the closed-loop performance. Our final goal is to establish a rigorous gains-tuning method for the non-adaptive backstepping controller. The study starts with the development of three-stage gains backstepping controller using a non-conventional time derivative Lyapunov function. This particular Lyapunov function makes it possible to analyze the response of the system when all the controller gains are cancelled. Then, we analyze the effect of each gain by cancelling out the values of the others. The first simulation results show that the convergence of the tracking error to zero is not maintained when all gains are set to 0 despite the presence of a negative definite of the Lyapunov function time derivative. In this case, the equilibrium point is not the expected one as time goes to infinity. The second set of results indicates that adjusting the gain related to the feedback of the actual output only ensures the asymptotic convergence of the tracking error to zero as time goes to infinity. However, developing a heuristic tuning of the three controller gains like Ziegler Nichols tuning remains a challenge.

A Tunable Gain and Bandwidth Low-Noise Amplifier with 1.44 NEF for EMG and EOG Biopotential Signal

This paper presents a low-noise inverter-based current-mode instrumentation amplifier with tunable gain and bandwidth for electromyogram (EMG) and electrooculogram (EOG) biopotential signals, targeting low input noise while maintaining low power consumption. The gain tuning method is based on pseudo-resistors, whereas the bandwidth is tunable due to a varactor system that is controlled by the same control voltage that tunes the gain. The circuit was designed and manufactured using the 110 nm UMC CMOS technology node, occupying an area of 0.624 mm2. The circuit presents a functioning mode for each biopotential signal with different characteristics, for the EMG a gain of 34.7 dB and a bandwidth of 1412 Hz was measured, with an input referred noise of 1.407 μV which matches a noise efficiency factor of 1.44. The EOG mode achieves a 39.5 dB gain and a 22.4 Hz bandwidth while presenting an input-referred noise of 0.829 μV corresponding to a noise efficiency factor of 6.37. For both modes, the supply voltage is 1.2 V and the circuit consumes 1 μA.

Online Gain Tuning Using Neural Networks: A Comparative Study

This paper addresses the problem of adapting a control system to unseen conditions, specifically to the problem of trajectory tracking in off-road conditions. Three different approaches are considered and compared for this comparative study: The first approach is a classical reinforcement learning method to define the steering control of the system. The second strategy uses an end-to-end reinforcement learning method, allowing for the training of a policy for the steering of the robot. The third strategy uses a hybrid gain tuning method, allowing for the adaptation of the settling distance with respect to the robot’s capabilities according to the perception, in order to optimize the robot’s behavior with respect to an objective function. The three methods are described and compared to the results obtained using constant parameters in order to identify their respective strengths and weaknesses. They have been implemented and tested in real conditions on an off-road mobile robot with variable terrain and trajectories. The hybrid method allowing for an overall reduction of 53.2% when compared with a predictive control law. A thorough analysis of the methods are then performed, and further insights are obtained in the context of gain tuning for steering controllers in dynamic environments. The performance and transferability of these methods are demonstrated, as well as their robustness to changes in the terrain properties. As a result, tracking errors are reduced while preserving the stability and the explainability of the control architecture.

Extremum-Seeking-Based Adaptive Model-Free Control and Its Application to Automated Vehicle Path Tracking

Traditional automated vehicle path-tracking algorithms require plant models to derive the respective control laws. However, the accurate vehicle model is difficult to obtain due to the complex tire-road interaction, time-varying parameters, and unknown disturbances. Consequently, data-driven controllers, which do not rely on a predefined plant model, have become increasingly popular. Notably, the model-free control (MFC), which expresses the derivatives of a controlled output as the sums of an amplified control input and an offset term, provides a straightforward solution to ground vehicle path tracking. Despite its ease of use, the control gain tuning of MFC remains largely a trial-and-error process, which could be both painstaking and poorly performing. Existing adaptive gain-tuning methods either rely on the command at the last step to iteratively update the control gain at the current step, or try to simultaneously identify the control gain and the offset term. However, they could yield chattering or unbounded control gains. To provide a new perspective for the MFC control gain tuning while improving MFC's control performance, we integrate MFC with the extremum-seeking control (ESC). ESC updates the control gain of MFC in real time to progressively enhance the control performance of MFC. Simulink–CarSim simulations and scaled car field tests demonstrate the effectiveness of the proposed extremum-seeking-based adaptive model-free controller.

Chattering performance criteria for multi-objective optimisation gain tuning of sliding mode controllers

Sliding mode control (SMC) is a robust control technique which, however, often shows undesirable chattering phenomena. In the design and gain tuning of SMCs, it is therefore important to try to minimise chattering. When tuning controller gains, there are often conflicting objectives, such as minimising error, minimising control input magnitude, and minimising chattering, that need to be taken into consideration. For nonlinear systems such as quadrotors, optimal gain tuning taking into account multiple conflicting objectives, can be obtained using multi-objective optimisation (MOO) methods. While error and control input performance criteria exist that can be used in MOO gain tuning methods, performance criteria specifically for SMC chattering are required. Therefore, this paper presents chattering performance criteria for SMCs that can be used to minimise chattering when tuning the controller gains. The chattering performance criteria are based on frequency analysis of the control input, making use of the discrete Fourier transform. In this paper, multi-objective particle swarm optimisation (MOPSO) is used for gain tuning of the SMCs, using the developed chattering performance criteria. Simulated and experimental results for the pitch angle control of a quadrotor are used as an example to test the effectiveness of the proposed chattering performance criterion. The results show that the proposed chattering index gives a good measure of chattering. Using the chattering index as one of the objectives for the MOPSO algorithm, gains that minimise chattering and control input, while providing good tracking control, can be selected.

Brain activation measurement for motion gain decision of surgical endoscope manipulation.

Robotic surgery improves minimally invasive interventions. However, it is challenging to determine the best gain settings for control of the endoscope. Providing the surgeon with the ability to manipulate the endoscope at an appropriate speed will likely improve the surgery by reducing the surgeon's stress. In this study, we validated the feasibility of a gain-tuning method in which the operator's brain activity is measured and used to evaluate stress levels. We developed an endoscope-manipulation simulator and used functional near-infrared spectroscopy to measure the prefrontal cortical activity, while participants controlled the simulator at different gain values. The brain activation levels in the prefrontal cortex exhibited significant differences under different viewpoint motion gain conditions. The stress-related brain activity was significantly reduced at specific gains, indicating that the brain activity evaluation would be useful to determine the parameters related to the operator's feelings.

Autonomous Driving Learning Preference of Collision Avoidance Maneuvers

Recently, with the active development of automated driving systems (ADSs) corresponding to SAE automated driving level 2, the comfort of ADSs has gained significant attention. In our previous research, it was proposed that the comfort of ADSs is affected by their maneuvers and the degree of information sharing between ADS and drivers. However, even if the drivers are well-informed of the recognized traffic environment and ADS-controlled trajectory, the comfort and trust of drivers in ADSs could be insufficient. This is because each driver has distinct preferred trajectories. Although some researchers have proposed ADSs that learn driver preferences, the maneuvers presented by these systems are not easily modified, because of off-line learning. In addition, a few quantitative investigations on the subjective evaluation of comfort have been conducted. This study proposes an on-demand learning collision avoidance ADS that learns the preferred maneuvers of drivers through driver intervention. In our system, the drivers teach their preferred trajectory to the system only if they are unsatisfied with the maneuver presented by the system. The system updates the parameters and shows the learned maneuver at the next avoidance. To realize the proposed system, we applied modified risk potential functions and gain-tuning method, as well as a cost function and learning method for gradual and stable maneuver learning. Driving simulator experiments demonstrated stable trajectory learning and smooth intervention. Furthermore, the drivers were satisfied with the learned maneuvers of the ADS, and their comfort and trust in the ADS improved when using the proposed ADS.

Precision force control of an underactuated stance leg exoskeleton for human performance augmentation

Lower limb exoskeleton which augments the human performance is a wearable human–machine integrated system used to assist people carrying heavy loads. Recently, underactuated lower limb exoskeleton systems with some passive joints become more and more attractive due to the advantages of smaller weight, lower system energy consumption and lower cost. However, because of the less of control inputs, the existed control methods of fully actuated exoskeletons cannot be extended to underactuated systems, which makes the robust controller design of underactuated lower limb exoskeletons becomes more challenged. This article focuses on the high-performance human–machine interaction force control design of underactuated lower limb exoskeletons with passive ankle joint. In order to solve the reduction of control inputs, the holonomic constraint from the wearer is considered, which help transform the dynamics of 3-degree-of-freedom underactuated exoskeleton in joint space into a 2-degree-of-freedom fully actuated system in Cartesian space. A two-level interaction force controller using adaptive robust control algorithm is proposed to effectively address the negative effect of various model uncertainties and external disturbances. In order to facilitate the control parameter selection, a gain tuning method is also presented. Comparative simulations are carried out, which indicate that the proposed two-level interaction force controller achieves smaller interaction force and better robust performance to various modeling errors and disturbances.

Precision Interaction Force Control of an Underactuated Hydraulic Stance Leg Exoskeleton Considering the Constraint from the Wearer

Hydraulic lower limb exoskeletons are wearable robotic systems, which can help people carry heavy loads. Recently, underactuated exoskeletons with some passive joints have been developed in large numbers for the purpose of decreasing the weight and energy consumption of the system. There are many control algorithms for a multi-joint fully actuated exoskeleton, which cannot be applied for underactuated systems due to the reduction in the number of control inputs. Besides, since the hydraulic actuator is not a desired force output source, there exist high order nonlinearities in hydraulic exoskeletons, which makes the controller design more challenging than motor driven exoskeleton systems. This paper proposed a precision interaction force controller for a 3DOF underactuated hydraulic stance leg exoskeleton. First, the control effect of the wearer is considered and the posture of the exoskeleton back is assumed as a desired trajectory under the control of the wearer. Under this assumption, the system dynamics are changed from a 3DOF underactuated system in joint space to a 2DOF fully actuated system in Cartesian space. Then, a three-level interaction force controller is designed in which the high-level controller conducts human motion intent inference, the middle level controller tracks human motion and the low-level controller achieves output force tracking of hydraulic cylinders. The MIMO adaptive robust control algorithm is applied in the controller design to effectively address the high order nonlinearities of the hydraulic system, multi-joint couplings and various model uncertainties. A gain tuning method is also provided to facilitate the controller gains selection for engineers. Comparative simulations are conducted, which demonstrate that the principal human-machine interaction force components can be minimized and good robust performance to load change and modeling errors can be achieved.

Finite Control Set Model‐Predictive Speed Control with a Load Torque Compensation

This paper presents a gain tuning method of the load torque compensator for direct speed control based on finite control set model predictive control (FCS‐MPC). In this method, the transfer function between the error in predicted speed and the load torque is constructed and used for gain parameter tuning of a load torque observer‐based prediction error correction. By using the proposed method, the stability and the responsiveness of FCS‐MPC with a load torque compensation can be improved without a fully simulating the electric drive systems and experiments. The simulation and experimental results obtained with a permanent magnet synchronous motor, fed by a two‐level three‐phase voltage source inverter, show that the tracking performance of the direct speed control tuned by the proposed method was excellent under load torque conditions. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Tuning Method of Resonant Current Controller With DC Elimination for PWM Rectifiers in Electric Multiple Units

Current control in electric multiple unit (EMU) pulsewidth-modulated (PWM) rectifiers is crucial for the system's stable operation and fast transient response. It should not only regulate the sinusoidal current under very low switching frequency but also eliminate dc current avoiding transformer core saturation. Therefore, proportional integral (PI) plus vector proportional integral with phase compensation (VPIc) controller is chosen for the current control in such single-phase rectifiers. However, the gains of PI and VPIc are coupled and determine the system performance together, invalidating the traditional gain tuning method. Therefore, a gain tuning method for PI + VPIc based on Nichols chart is proposed in this article, which decouples the two gains and simplifies the gain tuning into the tuning of gain margin and phase crossover frequency, while settling time and overshoot are considered for the optimal gain tuning. The designed system has good transient responses, stability, and robustness. The experimental results validate the theoretical analysis of the tuning method on a laboratory prototype and the effectiveness of the current control on the EMU PWM rectifiers.

Multirate iterative learning disturbance observer with robustness to frequency deviation for high precision attitude stabilization of flexible spacecraft subject to complex disturbances

High precision attitude stabilization of spacecraft is essential for the operation of precision instruments onboard. However, attitude vibrations of spacecraft are inevitably induced by complex disturbances. Disturbance observer based control scheme (DOBC) is a promising anti-disturbance technique, while it is restricted by the inadequate estimation performance of conventional disturbance observers for complex disturbances when taking structural complexity and noise sensitivity into account. To achieve high performance attitude stabilization of spacecraft subject to complex disturbances, a multirate iterative learning disturbance observer (MILDO) based controller is designed in this paper. Firstly, an augmented disturbance model is constructed in view of the periodicities of disturbances with known fundamental frequencies. Then, a generic proportional-derivative type MILDO (PD-MILDO) is designed by using previous disturbance estimates to update current estimates. The gain tuning method and the disturbance estimation performance of PD-MILDO are also presented. It is demonstrated that accurate disturbance estimation for low-frequency disturbance and multiple periodic disturbances can be achieved by PD-MILDO. Besides, the robustness of PD-MILDO to undesired frequency deviation can be significantly improved by introducing the proportional-derivative correction terms. Based on MILDO, a feedback controller is then designed to stabilize the spacecraft attitude. In the concept of DOBC, the controller and observer can be tuned independently. Finally, the effectiveness of the proposed control scheme is verified by simulations.

Automatic Gain Tuning Method of a Quad-Rotor Geometric Attitude Controller Using A3C

In this paper, we address an automated gain tuning method using Asynchronous Advantage Actor-Critic (A3C) reinforcement learning approach. A quad-rotor Unmanned Aerial Vehicle (UAV) with nonlinear geometric tracking controller is introduced to test our approach. In the geometric controller, two attitude gains must be provided appropriately to achieve stable error dynamics. To ease the difficulties while optimizing the controller performances, such as minimizing tracking error together with reducing control energy, we made Reinforcement Learning (RL) agents to substitute the entire gain tuning process. By training the RL agents with multiple quad-rotor configurations, we were not only able to reduce our efforts putting into the gain tuning by the trial-and-error methods, but also able to deal with the parameter changes by constructing an adaptive structure.

Quadrant glitch compensation by a modified disturbance observer for linear motor stages

In this paper, we propose to develop a novel disturbance force compensator to effectively suppress quadrant glitches in a feed drive system using a linear motor and linear motion ball guides (LMBGs). In the circular motions, feed drive systems using LMBGs generate a following error called quadrant glitches at the motion direction changing point of each axis. Quadrant glitches are engendered by disturbance force due to friction, and disturbance force compensation is necessary to compensate quadrant glitches. A disturbance observer is a disturbance force compensation method in the motion control field. If a disturbance observer causes quadrant glitch compensation, the height of quadrant glitches decreases. However, after a quadrant glitch, a deviation with a sign opposite to that of the quadrant glitch, called “inverse response”, is generated. An inverse response degrades the contouring motion accuracy and causes an excessive cutting of the cylindrical process. The proposed compensator, called a “disturbance suppressor”, presents two advantages over an ordinary disturbance observer: 1) there is no inverse response, and 2) the compensator exhibits a superior quadrant glitch compensation capability. The disturbance suppressor has two structural differences compared to an ordinary disturbance observer: 1) a pull-off point and a summing point are interchanged, and 2) two gain constants, K and β, are added to a disturbance observer. In this study, we investigate the causes of the inverse response suppression and quadrant glitch compensation improvement when applying the disturbance suppressor. We also propose a gain tuning method for the disturbance suppressor in a quadrant glitch compensation based on our results. The effectiveness of this compensator was verified by experiments using a feed-drive system. In the case of applying the disturbance observer, the height of the quadrant glitch was 10.4 nm and the amplitude of the inverse response was 3 nm. In the case of applying the optimized disturbance suppressor, the height of the quadrant glitch was 7.7 nm and there was no inverse response.

Improved Optimal Decentralized Load Modulation for Power System Primary Frequency Regulation

Nowadays the interest in smart load technologies for primary frequency regulation is spurred due to the increasing penetration of renewable energy resources. In this paper, an improved optimal load control (improved OLC) is introduced by applying a multiobjective optimization-based gain-tuning method to the conventional OLC approach. The objective is to minimize the frequency nadir, time response, steady-state error, total load shed, and aggregated disutility of controllable loads subject to power balance over the network. Simulation results indicate that enabling a multiobjective optimization-based gain-tuning procedure in the OLC approach can provide better power system frequency regulation. Time-domain analysis confirms the superior performance of improved OLC in terms of frequency nadir (Hz), steady-state error (Hz), control effort, and NERC-based performance metrics (MW/0.1 Hz), with detailed load and wind farm models. Furthermore, small-signal analysis demonstrates that the improved OLC enhances the system closed-loop performance and stability margins by increasing the damping ratio of the system's critical modes.

New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm

In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder–Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.