Nonlinear Feedforward Research Articles

Second Harmonic Current Reduction for Two-Stage Single-Phase Rectifier With LLC Converter Based on Charge Control With Nonlinear Feedforward

Second Harmonic Current Reduction for Two-Stage Single-Phase Rectifier With <i>LLC</i> Converter Based on Charge Control With Nonlinear Feedforward

Comparative Real-Time Study of Three Enhanced Control Strategies Applied to Dynamic Process Systems

In this study, a comparative analysis of three different control methods for precise, real-time control of a complex dynamic double-tank liquid level process system was performed. Since the system in question has a time-delayed structure, feedforward proportional integral (FF-PI) control and cascaded nonlinear feedforward proportional integral delayed (CNPIR) controllers were tested on the process system. While the FF-PI controller improved the response time of the system, it showed limitations in handling external disturbances and nonlinearities. On the other hand, the CNPIR controller showed better improvements in control accuracy and lower overshoot compared to the FF-PI controller. Since the process system has a nonlinear model and is affected by external disturbances, these two controllers were inadequate in this study when compared to the fractional order adaptive proportional integral derivative sliding mode controller (FO-APIDSMC). The FO-APIDSMC controller provided fairly good performance in both tracking accuracy and disturbance rejection control for non-chattering, fast finite-time convergence, increased robustness, and uncertain dynamic processes. Experimental results reveal that the FO-APIDSMC controller achieves superior minimized tracking error and outperforms the FF-PI and CNPIR controllers by effectively handling uncertainties and external disturbances.

Distributed Leader-Following Consensus of Feedforward Nonlinear Delayed Multiagent Systems via General Switched Compensation Control.

This work examines the distributed leader-following consensus problem of feedforward nonlinear delayed multiagent systems involving directed switching topologies. In contrast to the existing studies, we focus on time delays acting on the outputs of feedforward nonlinear systems, and we permit that the partial topology dissatisfy the directed spanning tree condition. In the cases, we present a novel output feedback-based general switched cascade compensation control method that addresses the above-mentioned problem. First, we put forward a distributed switched cascade compensator by introducing multiple equations, and we design the delay-dependent distributed output feedback controller with the compensator. Subsequently, when the control parameters-dependent linear matrix inequality is met and the switching signal of the topologies obeys a general switching law, we prove that the established controller can render that the follower's state asymptotically tracks the leader's state by employing an appropriate Lyapunov-Krasovskii functional. The given algorithm allows output delays to be arbitrarily large and increases the switching frequency of the topologies. A numerical simulation is presented to demonstrate the practicability of our proposed strategy.

Exponential Stabilization of Stochastic Feedforward Nonlinear Systems: A Dynamic Gain Approach

Exponential Stabilization of Stochastic Feedforward Nonlinear Systems: A Dynamic Gain Approach

Stabilization for a Class of Feedforward Nonlinear Systems via Pulsewidth-Modulated Controllers

Stabilization for a Class of Feedforward Nonlinear Systems via Pulsewidth-Modulated Controllers

Global regulation of almost feedforward nonlinear systems by an adaptive event‐triggered controller with multi‐triggering conditions

AbstractWe consider a global regulation problem for almost feedforward nonlinear systems with uncertain time‐varying parameters using an adaptive event‐triggered controller. The systems we consider are called “almost” feedforward systems because, in addition to the feedforward nonlinearity with the unknown linear growth rate, there is a non‐zero input‐matching term. Notably, this input‐matching term is a non‐feedforward term and it cannot be directly canceled due to the presence of input uncertainty and the use of event‐triggered control. To solve our considered problem, we propose an adaptive event‐triggered controller with multi‐triggering conditions and dynamic gain. We show that the closed‐loop system is globally regulated and the times between executions are positively lower bounded. Moreover, we show that the positive lower bounds of interexecution times can be enlarged by a control parameter. For a clear illustration, a practical example is given.

Operation and control of compact offshore combined cycles for power generation

Gas turbines are the main means for generating power in offshore installations. To increase energy efficiency, and thus reduce CO2 emissions, a steam bottoming cycle can be added to produce additional power by recovering surplus heat from the gas turbine exhaust. Due to space constraints, this solution is not widespread for offshore power generation. To enable deployment, the system must be compact and be able to provide varying power demands. In this paper, we analyze the operation and control problem for such compact combined cycles, consisting of two gas turbines and one steam bottoming cycle. We analyze the steady-state performance of the combined cycle with respect to efficiency and CO2 emissions for two control strategies for coordinating the power setpoint of the two gas turbines. Equal load allocation of the gas turbines showed a higher thermal efficiency and lower emissions compared to keeping one gas turbine close to nominal and letting the second one handle load variations. The main controlled variables for the steam bottoming cycle are the superheated steam pressure and temperature. We implement decentralized control strategies based on standard PID-controllers and nonlinear feedforward. Due to reduced throttle losses, sliding pressure shows higher efficiency and lower CO2 emissions compared to keeping a constant steam pressure, which conversely provides a better temperature dynamic response and may be necessary with highly varying power demand.

Output Voltage Precise Tracking Control for Boost Converters Based on Noncausal and Nonlinear Feedforward Control

Output Voltage Precise Tracking Control for Boost Converters Based on Noncausal and Nonlinear Feedforward Control

Global Output-Feedback Stabilization for Uncertain Feedforward Nonlinear Systems via Dynamic Event-Triggered Control

In this brief, the global output-feedback stabilization is addressed for a class of uncertain feedforward nonlinear systems. By integrating a dynamic event-triggered mechanism, a novel observer-based control scheme is established, where a time-varying gain is introduced to cope with large uncertainties and sampling errors in the system. Under this control scheme, the global convergence is ensured for the resulting closed-loop system states. Different from the related results, the system states are intermittently monitored by imposing suspension time after each sampling, so that the inter-event intervals have a uniform positive lower bound, and the Zeno behavior is automatically ruled out. Additionally, an internal dynamic variable is incorporated into the triggering mechanism to further extend the inter-event intervals. Finally, the obtained results are extended to a class of feedforward nonlinear systems with unknown time-varying growth rates. A simulation example is given by applying the proposed scheme to a nonlinear liquid level control (LLC) resonant circuit system.

Nonlinear feedforward enabling quantum computation

Measurement-based quantum computation with optical time-domain multiplexing is a promising method to realize a quantum computer from the viewpoint of scalability. Fault tolerance and universality are also realizable by preparing appropriate resource quantum states and electro-optical feedforward that is altered based on measurement results. While linear feedforward has been realized and become a common experimental technique, nonlinear feedforward was unrealized until now. In this paper, we demonstrate that a fast and flexible nonlinear feedforward realizes the essential measurement required for fault-tolerant and universal quantum computation. Using non-Gaussian ancillary states, we observed 10% reduction of the measurement excess noise relative to classical vacuum ancilla.

Cascade-Based Non-Linear Feedforward Neural Networks for Bi-Directional Memory

In today's world, efficient computation is the key to success in many fields. Pattern association plays an influential role in many areas of life, such as learning and memory. In complex dynamics, bidirectional associative memory has been effectively demonstrated by neural networks. However, these neural networks face challenges in terms of performance, such as computational time. A random bipolar input pattern and output pattern of a matrix with different sizes were used to analyze the background of this study. In order to address the challenges of bidirectional associative memory, nonlinear memory association is considered to be the most feasible method. In this study, we present a cascade-based non-linear feedforward neural network that performs pattern association in two passes and behaves like a Bayesian algorithm. A random pattern and English alphabets with different patterns have been used to validate the results of this approach. Using the experimental results, the study evaluated BAM's equivalent performance, pattern association, and stability.

Global Prescribed-Time Stabilization for a Class of Uncertain Feedforward Nonlinear Systems

The problem of global prescribed-time stabilization (PTS) is addressed in this brief for a class of uncertain feedforward nonlinear systems. To achieve this task, a new time-scale transformation (TST) is first introduced, which has the good property that it changes the original global PTS into the global asymptotic stabilization of the transformed one but does not changes the system structures. Then, under the new framework, both state and output feedback controllers are developed to achieve the requiring performances with the help of the non-recursive technique. At last, simulation results of a nonlinear liquid level control resonant circuit (NLLCRC) system are provided to show the validity of the given approach.

Output Voltage Precise Tracking Control for Boost Converters based on Noncausal and Nonlinear Feedforward Control

Boost converters are key components of DC power conversion used for electric mobility and renewable energy applications. In addition to constant voltage control of the output, variable voltage control has been attracting attention in recent years for high-efficiency drive of loads. However, the dynamic characteristics of boost converters exhibit nonlinear and nonminimum phase characteristics. Therefore, the inverse model for feedforward control is unstable, making high-precision voltage trajectory tracking control challenging. This study aims to present a noncausal and nonlinear feedforward controller to compensate for the nonlinear and nonminimum phase characteristics of the boost converter and to achieve perfect tracking control with respect to the output voltage trajectory. This study also establishes a method for identifying circuit parameters and deriving the time length of noncausal control input for practical implementation. The effectiveness of this control method is demonstrated by experiments using a boost converter.

Event-Triggered Output Feedback Control for Leader–Follower Consensus of Feedforward Nonlinear Multiagent Systems

This article addresses the event-triggered output feedback control problem for feedforward nonlinear multiagent systems. A novel time-varying gain control method for the design of distributed consensus protocol is presented by means of a directed communication topology. An improved gain function is introduced to establish the relative threshold strategy and compensators. The distributed controllers are constructed such that the leader–follower consensus errors converge to a bounded region that is adjustable. The main advantage of the proposed method is that the consensus performance and event inter-execution intervals are taken into account. This strategy greatly accelerates the convergence rates of consensus errors and avoids the Zeno-behavior. The simulation results show the effectiveness of the established consensus protocol.

Microsatellite Uncertainty Control Using Deterministic Artificial Intelligence.

This manuscript explores the applications of deterministic artificial intelligence (DAI) in a space environment in response to unknown sensor noise and sudden changes in craft physical parameters. The current state of the art literature has proposed the method, but only ideal environments, and accordingly this article addresses the literature gaps by critically evaluating efficacy in the face of unaddressed parametric uncertainties. We compare an idealized combined non-linear feedforward (FFD) and linearized feedback (FB) control scheme with an altered feedforward, feedback, and deterministic artificial intelligence scheme in the presence of simulated craft damage and environmental disturbances. Mean trajectory tracking error was improved over 91%, while the standard deviation was improved over 97% whilst improving (reducing) control effort by 13%.

Comparison of Deep Learning and Deterministic Algorithms for Control Modeling.

Controlling nonlinear dynamics arises in various engineering fields. We present efforts to model the forced van der Pol system control using physics-informed neural networks (PINN) compared to benchmark methods, including idealized nonlinear feedforward (FF) control, linearized feedback control (FB), and feedforward-plus-feedback combined (C). The aim is to implement circular trajectories in the state space of the van der Pol system. A designed benchmark problem is used for testing the behavioral differences of the disparate controllers and then investigating controlled schemes and systems of various extents of nonlinearities. All methods exhibit a short initialization accompanying arbitrary initialization points. The feedforward control successfully converges to the desired trajectory, and PINN executes good controls with higher stochasticity observed for higher-order terms based on the phase portraits. In contrast, linearized feedback control and combined feed-forward plus feedback failed. Varying trajectory amplitudes revealed that feed-forward, linearized feedback control, and combined feed-forward plus feedback control all fail for unity nonlinear damping gain. Traditional control methods display a robust fluctuation for higher-order terms. For some various nonlinearities, PINN failed to implement the desired trajectory instead of becoming “trapped” in the phase of small radius, yet idealized nonlinear feedforward successfully implemented controls. PINN generally exhibits lower relative errors for varying targeted trajectories. However, PINN also shows evidently higher computational burden compared with traditional control theory methods, with at least more than 30 times longer control time compared with benchmark idealized nonlinear feed-forward control. This manuscript proposes a comprehensive comparative study for future controller employment considering deterministic and machine learning approaches.

An Improved Dynamic Gain Method to Global Regulation of Feedforward Nonlinear Systems

This article proposes an improved dynamic gain method for studying the global regulation problem of feedforward nonlinear systems. The uncertain nonlinear terms admit an incremental rate depending on an unknown constant and the input. With a transformation of the state, an output feedback controller and a state feedback controller are designed respectively, without resorting to the celebrated forwarding design method. Based on the Lyapunov analysis, it is proved that the global regulation of the closed-loop system is achieved. Different from most existing control constructs for feedforward nonlinear systems, a new time-varying function is used for the first time to design the controller with a dynamic gain, which improves the convergence speed of the system state. Through a simulation example, the advantages of the improved dynamic gain method are illustrated.

Gaussian Processes for Advanced Motion Control

Machine learning techniques, including Gaussian processes (GPs), are expected to play a significant role in meeting speed, accuracy, and functionality requirements in future data-intensive mechatronic systems. This paper aims to reveal the potential of GPs for motion control applications. Successful applications of GPs for feedforward and learning control, including the identification and learning for noncausal feedforward, position-dependent snap feedforward, nonlinear feedforward, and GP-based spatial repetitive control, are outlined. Experimental results on various systems, including a desktop printer, wirebonder, and substrate carrier, confirmed that data-based learning using GPs can significantly improve the accuracy of mechatronic systems.

Control of steam bottoming cycles using nonlinear input and output transformations for feedforward disturbance rejection

In this paper we analyze the control problem for a steam cycle that produces power by recovering waste-heat from gas turbines on an offshore installation. The waste-heat recovery unit is based on once-through steam generator technology. The main disturbances are large variations of the gas turbine exhaust gas flowrate and temperature. We analyze the effect of these disturbances on the operation of the steam cycle, specifically for the superheated steam pressure and temperature. We compare the performance of different decentralized control strategies based on standard PID-controllers and nonlinear feedforward. We consider floating pressure and constant pressure operation strategies. For steam temperature control we implement feedback, and feedback in combination with nonlinear input and output transformations for feedforward disturbance rejection. These transformations are based on the steady-state energy balance on the waste heat recovery unit, while for simulation purposes we use a high-fidelity dynamic model of the bottoming cycle designed to minimize the weight and volume. The outcome from this work can be used to propose control strategies for coordinating a combined cycle (gas turbine with a steam bottoming cycle) that can operate with large and rapid changes in power demand.

Dynamic Event-Triggered Output Feedback Control for a Class of High-Order Feedforward Nonlinear Time-Delay Systems

This brief addresses the event-triggered output feedback control issue for high-order feedforward nonlinear systems with time-varying delays. Different from the existing results, a novel event-triggering mechanism coupling with a dynamic gain is proposed. Finally, a novel event-triggered output feedback controller is designed to ensure the boundedness of the closed-loop system. Moreover, the Zeno behavior is avoided. Two examples are presented to verify the effectiveness of the proposed scheme.