Performance Of Designed Controller Research Articles

Fixed-time prescribed performance control for nonlinear multi-agent systems with novel uncertainties

This paper addresses an approach to fixed-time prescribed performance control design for multi-agent systems with novel uncertainties, i.e., the uncertainties from system input powers, and a new neural backstepping control design is proposed such that the prescribed performance can be guaranteed within a fixed-time. Unlike existing adaptive results, the upper boundary of stability time is decided only by the design parameters and also is independent of the initial states of the system. By leveraging Lyapunov stability theory and graph theory, it is demonstrated that every follower agent can synchronize to the leader agent, and tracking errors converge to the origin’s neighborhood. Finally, the simulation results verify the effectiveness of the control strategy.

Design and evaluation of self-tuning optimal control for vibration suppression of magnetorheological semi-active suspension

The objective of this paper is to enhance the vibration damping capabilities of magnetorheological (MR) semi-active suspension by utilizing a self-tuning LQG control method. Firstly, to determine the mechanical model of MR damper using the hyperbolic tangent model, mechanical experiments were performed with a damping force test machine under various input excitations. Experimental and simulation data were compared to confirm the accuracy of the mechanical model for MR damper. The MR damper is then incorporated into the vehicle’s semi-active suspension system to create a quarter-car suspension model. In order to tackle the problems associated with low control accuracy and poor dynamic adjustment in traditional LQG controllers where the weighting matrix coefficients are difficult to determine, a self-tuning LQG controller based on gravity search algorithm (GSA-LQG) was designed. Finally, the designed controller performance was evaluated by the simulation and experimental results obtained from the random and sinusoidal excitation roads. Experimental data reveal that when subjected to GSA-LQG control, the suspension control performance is considerably enhanced compared to the passive control, PID control and conventional LQG control suspension. Based on these outcome, it can be concluded that the proposed algorithm is effective and the experimental platform is feasible.

Optimal controller design for high performance of solar energy for grid-connected systems

Optimal controller design for high performance of solar energy for grid-connected systems

Adaptive Super-Twisting Controller Design for Accurate Trajectory Tracking Performance of Unmanned Aerial Vehicles

Adaptive Super-Twisting Controller Design for Accurate Trajectory Tracking Performance of Unmanned Aerial Vehicles

An improved sampled-data control for a nonlinear dynamic positioning ship with Takagi-Sugeno fuzzy model.

This article considered the sampled-data control issue for a dynamic positioning ship (DPS) with the Takagi-Sugeno (T-S) fuzzy model. By introducing new useful terms such as second-order term of time, an improved Lyapunov-Krasovskii function (LKF) was constructed. Additionally, the reciprocally convex method is introduced to bound the derivative of LKF. According to the constructed LKF, the sampling information during the whole sampling period was fully utilized, and less conservatism was obtained. Then, the stability condition, robust performance, mode uncertainty and sampled-data controller design were analyzed by means of the linear matrix inequality (LMI). Finally, an example was given to demonstrate the effectiveness of the proposed method.

Optimized Adaptive Fuzzy Security Control of Nonlinear Systems With Prescribed Tracking Performance.

This article studies the optimized fuzzy prescribed performance control problem for nonlinear nonstrict-feedback systems under denial-of-service (DoS) attacks. A fuzzy estimator is delicately designed to model the immeasurable system states in the presence of DoS attacks. To achieve the preset tracking performance, a simper prescribed performance error transformation is constructed considering the characteristics of DoS attacks, which helps obtain a novel Hamilton-Jacobi-Bellman equation to derive the optimized prescribed performance controller. Furthermore, the fuzzy-logic system, combined with the reinforcement learning (RL) technique, is employed to approximate the unknown nonlinearity existing in the prescribed performance controller design process. An optimized adaptive fuzzy security control law is then proposed for the considered nonlinear nonstrict-feedback systems subject to DoS attacks. Through the Lyapunov stability analysis, the tracking error is proved to approach the predefined region by the preset finite time, even in the presence of DoS attacks. Meanwhile, the consumed control resources are minimized due to the RL-based optimized algorithm. Finally, an actual example with comparisons verifies the effectiveness of the proposed control algorithm.

Performance investigation and control design of SSI grid-connected system for PV applications with maximum power extraction

Split-source inverter (SSI) has been recently developed as an alternative to the commonly used ZSI where it can serve an interface between the renewable energy sources and the common grid. This paper presents a mathematical model and control strategy for grid-connected SSI system with either constant voltage source or photovoltaic (PV) source. The control strategy is based on linear PI controllers which are fine-tuned by the frequency loop shaping method with the help of the derived mathematical model to ensure the stability of the system with robust dynamic behavior. The derived mathematical model provides a straightforward and efficient understanding of the dynamics resulting from the interaction between the PV and the SSI grid-connected system. The simulation results indicate that the system has a robust dynamic behavior when subjected to various transient conditions. Finally, a downscaled prototype of SSI grid-connected system is implemented to experimentally validate both the mathematical model and control strategy.

PECLOS path-following control of underactuated AUV with multiple disturbances and input constraints

This paper presents a new position error-constrained line-of-sight (PECLOS) path-following (PF) control strategy for an underactuated AUV under multiple disturbances and input constraints. Firstly, considering that many previous studies ignored the influence of stochastic noises, which may degrade the designed controller performance or even lead to instability in practical applications, the multiple disturbances are divided into two categories based on their features, i.e., the slowly time-varying deterministic disturbances and the stochastic noises. On this basis, the time-varying deterministic disturbances are further treated as a single equivalent disturbance and a linear stochastic differential model of underactuated AUV is established by augmenting the equivalent disturbance as an extended state. Secondly, a novel PF control strategy is proposed based on an extended state based Kalman-Bucy filter (ESKBF) and auxiliary dynamic compensators. The PECLOS guidance law is established to maintain the PF errors within the specified time-varying bounds, the ESKBF technology is utilized to estimate and compensate for the equivalent disturbances under the stochastic noises, and the auxiliary dynamic compensators are adopted to overcome the input constraints. Eventually, the stability of the overall closed-loop system is verified. Simulation and comparative analysis are presented to demonstrate the enhanced performance of the presented controller.

An improved event-triggered control method for fuzzy systems under membership functions online learning

This paper is concerned with the problem of adaptive event-triggered (AET) based optimal fuzzy controller design for nonlinear networked control systems (NCSs) characterized by Takagi–Sugeno (T–S) fuzzy models. An improved AET communication scheme with a memory adaptive rule is proposed to enhance the utilization of the state response vertex data. Different from the existing ET based results, the improved AET scheme can save more communication resources and acquire better system performance. The sufficient criteria of performance analysis and controller design are presented for the closed-loop control system subject to mismatched membership functions (MFs) and AET scheme. And then, a new MFs online learning algorithm on the basis of the gradient descent approach is employed to optimize the MFs of fuzzy controller and obtain optimal fuzzy controller for further improving system performance. Finally, two simulation examples are presented to verify the advantage and effectiveness of the provided controller design technique.

Limit state design of resilient earthquake-resisting systems

Sustainable seismic design (SSD) is the first step towards next-generation earthquake engineering. SSD of mixed multiple seismic systems (MMSSs), in which more than one earthquake-resisting structure is used, is a challenging structural engineering issue. Seismic sustainability implies survivability during and after an earthquake, preceded by post-earthquake realignment and repairs (PERR). Purpose-specific MMSSs are ideally suited for SSD. However, contemporary codes address neither the sequences nor the failure mechanisms of the earthquake-resisting structures of MMSSs. The difference between conventional design and SSD is their approach to expected behaviour during and after an earthquake. Earthquakes are natural and dynamic occurrences, whereas PERR is a manual and static process. SSD does not favour unreal detailing or oversimplifying assumptions; it is a multi-faceted effort that involves realistic structural analysis and planned manual operations. In this context, design implies operability with a view to PERR and requires a change from damageability assessment and performance-based seismic design to performance control and reparability-based design, including the development of new analytic tools and purpose-specific details. This article presents graphical solutions and theoretical principles to help achieve practical SSD for MMSSs. These new techniques may be used for practical reliability, economy and environmental protection.

Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise

Economic model predictive control and tracking model predictive control are two popular advanced process control strategies used in various of fields. Nevertheless, for a given process, which controller should be chosen to achieve better performance is uncertain when noise exists. To this end, a sensitivity-based performance assessment approach is proposed to pre-evaluate the dynamic economic and tracking performance of them and guide the controller selection in this work. First, their controller gains around the optimal steady state are evaluated using the sensitivities of corresponding constrained dynamic programming problems. Second, the controller gains are substituted into the control loop to derive the propagation of process and measurement noise. Subsequently, the Taylor expansion is introduced to simplify the calculation of variance and mean of each variable. Finally, the tracking and economic performance surfaces are plotted and the performance indices are precisely calculated through integrating the objective functions and the probability density functions. Moreover, boundary moving (i.e., back off) and target moving can be pre-configured to guarantee the stability of controlled processes based on the proposed approach. Extensive simulations under different cases prove that the proposed approach can provide useful guidance on performance assessment and controller design.

Adaptive Fuzzy Appointed-Time Control for Uncertain Systems Bounded by Positive Functions

This brief presents an appointed-time prescribed performance control design for uncertain nonlinear systems via adaptive fuzzy technique. Compared with existing results, the controller achieves appointed-time tracking control for higher-order nonlinear systems with uncertainties bounded by positive functions. Firstly, barrier Lyapunov functions integrated with appointed-time prescribed performance functions are designed to cope with the requirements of output constraints and quantitative performance constraints. Then, adaptive fuzzy systems are constructed to approximate the unknown continuous upper-bound functions of the uncertainties. Subsequently, through the backstepping framework, a novel adaptive fuzzy appointed-time control scheme is proposed for the uncertain nonlinear system bounded by positive functions. Thus, the appointed settling time, predetermined steady-state error, and quantitative transient-state performance of the closed-loop system are ensured. Finally, the fixed-time convergence of the closed-loop system is proved via Lyapunov analysis, while comparative simulations are conducted to illustrate the superiority of the proposed scheme.

Adaptive optimal formation control for unmanned surface vehicles with guaranteed performance using actor‐critic learning architecture

SummaryIn this article, an optimized formation control algorithm is presented for unmanned surface vehicles (USVs) with collision avoidance and prescribed performance. The prescribed formation geometry is designed in the leader‐follower formation architecture, in which each vehicle tracks its intermediary leader with preserving a desired separation. A prescribed performance control design technique is introduced to guarantee the transient and steady‐state performance specifications on formation errors. Radial basis function neural networks (NNs) are employed to approximate modeling uncertainties including damping terms and unmodeled dynamics. Based on an actor‐critic learning strategy, a reinforcement learning (RL) algorithm is proposed to ensure the optimality of formation control and the specified tracking accuracy simultaneously, in which actor NNs take appropriate control behaviors by interacting with the external environment, and critic NNs evaluate the control performance and generate a reinforcement signal to actor NNs for facilitating the improvement of subsequent behaviors. Stability analysis shows that the proposed optimal formation controller achieves semi‐global uniform ultimate boundedness of closed‐loop adaptive systems with prescribed performance. Comparative simulation results illustrate the effectiveness and superiority of the presented control algorithm.

Finite-Time Fuzzy Adaptive PPC for Nonstrict-Feedback Nonlinear MIMO Systems.

This article addresses the issue of the fuzzy adaptive prescribed performance control (PPC) design for nonstrict feedback multiple input multiple output (MIMO) nonlinear systems in finite time. Unknown nonlinear functions are handled via fuzzy-logic systems (FLSs). By combining the adaptive backstepping control algorithm and the nonlinear filters, a novel dynamic surface control (DSC) method is proposed, which can not only avoid the computational complexity issue but also improve the control performance in contrast to the traditional DSC control methods. Furthermore, to make the tracking errors have the prescribed performance in finite time, a new Lyapunov function is constructed by considering the transform error constraint. Based on the designed Lyapunov functions, it is proved that all the signals of the controlled systems are semiglobal practical finite-time stability (SGPFS). Finally, a simulation example is provided to illustrate the feasibility and validity of the put forward control scheme.

Data-driven approach for time-delay estimation of industrial processes

An accurate estimate of time delay is of crucial importance for control tasks such as performance assessment and controller design. In this paper, a novel data-driven approach to time-delay estimation is developed for a process subject to industrial background disturbances, in which the closed-loop output data under the routine operating conditions is only required. The practical solutions are proposed to estimate the time delay based on the estimated impulse response of the closed loop that is estimated online by utilizing the output data. Without relying on system identification and any prior knowledge of the process as well, the time delay is estimated directly for a large time delayed process; while for a small time delayed process, the time delay is estimated by means of the stationarilized filter, the pre-filter and the loop filter. The effectiveness of the proposed approach is validated by various numerical and industrial examples including a distillation column, a petroleum refinery heating furnace and a ceramic dryer.

The observability and controllability metrics of power system oscillations and the applications

Power system oscillation has caused many serious accidents in renewable energy grid connection system. The concept of observability and controllability in control theory has been widely applied to power system oscillation study. In this paper, observability metric (OM) and controllability metric (CM) are defined from the perspective of oscillation modes, acting as a novel quantification method to quantify the observability and controllability of power system with distinct or repeated eigenvalues. Furthermore, in order to compare the reflection degree and control effect of signals in different oscillation modes, the mode comprehensive observability metric (MCOM) and comprehensive controllability metric (MCCM) are proposed. The proposed method shows clearer relationship between controllability/observability and oscillation modes by combining the information of conjugate eigenvalues together. The advantages of metrics are illustrated by comparing with theoretical derivations and calculation results of three traditional methods: participation factor, residue method and geometric measures. Finally, the metrics are applied to a subsynchronous damping controller (SSDC) design for better performance in oscillation monitoring and suppression. With the small-signal model and corresponding time-domain simulation, the effectiveness of the proposed method is verified.

Assessment of Low-Voltage Motor Ride Through: A Comparison of Solutions for Critical Processes in the Oil and Gas Industry

Voltage sags initiated by utility transmission system faults cause nuisance trips of low-voltage (LV) motors, due to contactors dropping off as a result of the undervoltage condition. Since voltage disturbances by utility systems cannot be prevented, it is essential to mitigate the impact of LV motors trips on critical process systems in the oil and gas industry, such as lubrication and shaft-sealing systems. To address this challenge, there is a tendency in the industry to modify standard LV motor control schemes to hold contactors closed during voltage sags to maintain process continuity. This approach will be evaluated against restarting schemes and sag correction devices in terms of their impact on motor performance and motor controller design. A simulation model was developed using Power Systems CAD to assess motor performance and subsequent current and torque profiles associated with various ride-through solutions. The reliability and safety aspects of modifications associated with different ride-through solutions were evaluated and benchmarked against relevant international standards and industrial practices.

H ∞ Load Frequency Control for Power Systems Under Communication Delays: An Event-Triggered Dynamic Output Feedback Scheme

In this brief, the problem of the load frequency control for power systems suffered from communication delays is discussed based on a dynamic output feedback scheme. An output-based event detection mechanism, where the error signal is defined as the difference between the latest transmitted data and the average value of the currently sampled data and the latest transmitted data, is introduced to reduce the triggering rate while ensuring the control performance. Combining such an event-triggered control scheme with dynamic output feedback control strategy, the concerned closed-loop system is established. After that, with the aid of Lyapunov-Krasovskii functional and appropriate matrix decoupling technique, some sufficient criteria for the performance analysis and controller design are deduced. Finally, the availability of the developed control strategy is verified through a simulation example with two cases.

Explicit damping specification in PI controller design for desired control performance of wind power conversion system with permanent magnet generator

Explicit damping specification in PI controller design for desired control performance of wind power conversion system with permanent magnet generator

A State Space Modeling Method for Aero-Engine Based on AFOS-ELM

State space models (SSMs) are important for multi-variable performance analysis and controller design of aero-engines. In order to solve the problems of the traditional state space modeling methods that rely on component-level models (CLMs) and cannot be carried out in real time, an aero-engine state space modeling method based on adaptive forgetting factor online sequential extreme learning machine (AFOS-ELM) is proposed in this paper. The structure of the extreme learning machine (ELM) is determined according to the form of the state space model, and the inverse-free ELM algorithm is used to automatically select the appropriate number of hidden nodes to improve the efficiency of offline initialization. The focus of the ELM on current operation performance is enhanced by the adaptive renewed forgetting factor, which reduces the impact of aero-engine history and deviated data on the current output and improves the accuracy of the model. Then, according to the analytical equation of the ELM model, the state space model of an aero-engine at each sampling time is obtained by using the partial derivative method. The simulation results based on engine test data show that the real-time performance and accuracy of the state space model established online in this paper can meet the needs of aero-engine control system requirement.