Robust Control Scheme Research Articles

Path tracking control of high‐speed intelligent vehicles considering model mismatch

AbstractThe precision of path tracking in high‐speed intelligent vehicles is significantly influenced by model mismatch arising from factors like parameter uncertainty, model simplification, external disturbances, and other sources. In this paper, we propose a novel robust control strategy that integrates the compensation function observer (CFO) with the model predictive control (MPC) method, utilizing an optimized vehicle dynamics model (opt‐model) to address this challenge, called OCMPC. Initially, we establish the opt‐model to design predictive model by leveraging suspension kinematics and compliance (K&C) data collected from a miniature pure electric vehicle. Remarkably, the opt‐model exhibits improved accuracy compared to the conventional vehicle dynamics model (con‐model) while preserving the same degrees of freedom (DOF). Next, we incorporate CFO into the path tracking process of high‐speed intelligent vehicles, enabling dynamic real‐time observation of the model mismatch between the prediction model and the actual vehicle. CFO can capture the dynamics of the vehicle, including nonlinearities and uncertainties, without placing a heavy computing burden on the controller. This observed mismatch is subsequently employed for feed‐forward compensation, facilitating the attainment of optimal control values. Ultimately, we validate the effectiveness of our proposed method in enhancing path tracking accuracy for high‐speed intelligent vehicles through co‐simulation using Simulink and Carsim.

A novel learning-based robust model predictive control strategy and case study for application in optimal control of FCEVs

With the gradual implementation of carbon-neutral policies, fuel cell electric vehicles (FCEVs) have gained widespread recognition as a promising solution. Due to multi-source electromechanical coupling, the powertrain of FCEVs constitutes a complex nonlinear system, necessitating an advanced energy management strategy for effective control to enhance economic performance. In this study, a novel learning-based robust model predictive control (LRMPC) is proposed for a 4WD FCEV, integrating a high-confidence velocity estimation model and an accurate state observation model to enhance energy-saving potential, robustness, and real-time application performance under various driving conditions. To enhance robustness and adaptability, deep forest with superior feature fusion abilities is employed to generate future velocity reference datasets based on FCEV operating scenarios. To improve state observation accuracy and control effectiveness, a novel built-in state observation model based on machine learning algorithms is established, leveraging knowledge of nonlinear systems within the FCEV powertrain. Subsequently, to enhance the real-time applicability of LRMPC, an explicit control scheme for multidimensional mapping based on explicit data tables is utilized for accurate state transformation, incorporating input features such as vehicle states and component states. Simulation evaluations and hardware-in-the-loop tests demonstrate the improved economic performance and real-time application ability of LRMPC, showcasing its promising performance.

Integrating of LMIs with Buckingham Π theorem for control of uncertain oscillating systems

This paper introduces a novel framework for robust controller synthesis in oscillating systems by synergistically combining generalized linear matrix inequalities (GLMIs) with the Buckingham Π theorem. The primary goal is to enhance the design of robust controllers for systems with uncertain parameters through the exploitation of dimensionless groups derived from the Buckingham Π theorem. By establishing a dimensionless state-space representation, the proposed approach demonstrates adaptability to a broad spectrum of similar systems. The LMI-based formulation facilitates the systematic design of controllers capable of effectively mitigating the impact of parametric uncertainties. Simulation results for an uncertain inverted pendulum and a vehicle active suspension system validate the superior performance of the proposed method compared to conventional control techniques, such as MPC, LQG, SMC, PID, and LQR. For the uncertain inverted pendulum system, the proposed robust control scheme achieves a cart position stabilization in 5.35 s and pendulum stabilization in 6.3 s, which is faster than standard control algorithms. In the context of the vehicle active suspension system, the performance indices for sprung mass acceleration, suspension deflection, and tire deflection are 0.19, 0.47, and 0.07, respectively. These values indicate significant reductions when compared to conventional passive suspension systems and standard robust control methodologies.

Salmonella pathogenesis-based In-silico design and immunoinformatic analysis of multi-epitope vaccine constructs in broiler veterinary medicine

Salmonellosis, a zoonotic gastrointestinal disease, presents a significant global health burden with a high incidence rate. Transmission primarily occurs through the consumption of contaminated poultry products, although water and contact with asymptomatic animals are also vectors. The disease’s pervasiveness has prompted international health organizations to advocate for robust prevention and control strategies. This study focuses on the in-silico design of a multi-epitope vaccine targeting Salmonella enterica serovar Typhimurium’s fimH protein, a fimbriae component crucial for bacterial adhesion and pathogenicity. The vaccine construct was developed by identifying and synthesizing non-allergenic, antigenic, and non-toxic epitopes for both Cytotoxic T Lymphocytes and Helper T Lymphocytes. Adjuvants were incorporated to enhance immunogenicity, and the vaccine’s structure was modeled using advanced bioinformatics tools. The proposed vaccine demonstrated promising antigenicity and immunogenicity profiles, with a favorable physical-chemical property analysis. The vaccine’s structures, designed by computational analysis, suggests high likelihood to native protein configurations. Antigenicity and allergenicity assessments validate the vaccine’s immunogenic potential and hypoallergenic nature. Physicochemical evaluations indicate favorable stability and solubility profiles, essential for vaccine efficacy. This comprehensive approach to vaccine design expressed in Chlorella vulgaris holds promises for effective salmonellosis control. The multi-epitope vaccine, designed through meticulous in-silico methods, emerges as a promising candidate for controlling salmonellosis. Its strategic construction based on the fimH protein epitopes offers a targeted approach to elicit a robust immune response, potentially curbing the spread of this disease in poultry.

Fixed-Time Robust Fractional-Order Sliding Mode Control Strategy for Grid-Connected Inverters Based on Weighted Average Current

Fixed-Time Robust Fractional-Order Sliding Mode Control Strategy for Grid-Connected Inverters Based on Weighted Average Current

MR fluid-Based threshold-feedback overload-protection system for miniature turbine generator

Abstract Miniature turbine generators, which can supply power to miniature mechatronical system by converting natural energy, are promising for future applications in micro-aircraft, ammunition, and missiles. However, turbine generators are at the risk of rotor wear and circuit failure under high wind-penetration conditions. As the feature size decreases, conventional sensors can hardly be integrated in microsystems, making control more difficult. In addition, microspace demands higher practicality of control strategies. Existing complex control units of large-scale systems are not applicable to microsystems. Therefore, a safe and robust control strategy that incorporates practical applications should be considered. This study aims to bridge the gap between the phase-transition properties of magnetorheological (MR) materials and control methods in a microspace. A MR fluid-based threshold-feedback overload-protection strategy is proposed for the miniature turbine generators. This strategy realises the sensorless control of miniature turbine generators by directly capturing the output frequency. To accurately describe the control characteristic of the overload-protection system, a dynamic model of the rotating shaft is proposed. A series of threshold voltages (Ur = 4.0, 5.0 and 6.0 V) is used to test the controllability of the overload-protection system. Experimental results show that the rotational speed of the miniature turbine generator is effectively controlled under hurricane-force wind conditions (v=40 m/s). Consequently, this study has developed a control strategy to solve the overload failure of miniature turbine generators. Under low wind speeds, the miniature turbine generator starts reliably. When the wind speed exceeds the threshold value, the miniature turbine generator is protected from overload failure. We believe that this work is invaluable for the functional expansion and performance improvement of miniature turbine generators.

Reliability-based robust control strategy via LMI approach for TMDs and ATMDs with uncertain parameters

This paper presents an integrated reliability-based H∞ robust control. This method consists of the H∞ control method and the Hasofer-Lind and Rackwitz-Fiessler (HL-RF) strategy. In this respect, the uncertainties in the system should be taken into account by using the random variables, which may arise due to various factors Such as the absence of understanding of the properties of materials, mistakes in design, etc. Also, considering the uncertainties in the system is important because it may cause wide changes in the responses of a system. For this reason, the reliability method must be carefully examined. To reduce the responses of the structure, TMDs and ATMDs are used. The parameters of the dampers (mass, stiffness, damping) are considered with uncertainty. An iterative HL-RF algorithm is used for structure equipped with TMDs to deal with uncertainties. To find the appropriate control force of TAMDs, the combination of the H∞ control strategy based on the linear matrix inequality utilized and the HL-RF method algorithm is expressed. Two types of dampers are vertically distributed on the different floors to increase the reliability of the structure under earthquake excitations. The proposed strategy indicated that the system's responses have been provided greater value by considering the uncertainty that shows the importance of this issue. According to the obtained values, the presented algorithm has performed well. Also, the proper reliability index is acquired by using the HL-RF method.

Robust data-driven predictive control for unknown linear time-invariant systems

This paper presents a new robust data-driven predictive control scheme for unknown linear time-invariant systems by using input-state-output or input–output data based on whether the state is measurable. To remove the need for the persistently exciting (PE) condition of a sufficiently high order on pre-collected data, a set containing all systems capable of generating such data is constructed. Then, at each time step, an upper bound on a given objective function is derived for all systems in the set, and a feedback controller is designed to minimize this bound. The optimal control gain at each time step is determined by solving a set of linear matrix inequalities. We prove that if the synthesis problem is feasible at the initial time step, it remains feasible for all future time steps. Unlike current data-driven predictive control schemes based on behavioral system theory, our approach requires less stringent conditions for the pre-collected data, facilitating easier implementation. The effectiveness of our proposed methods is demonstrated through application to an unknown and unstable batch reactor.

Robust control scheme for the mixed platoon system with time-varying information topologies and inaccurate state information

Robust control scheme for the mixed platoon system with time-varying information topologies and inaccurate state information

Advancing wind energy: adaptive neural network control for optimized permanent magnet synchronous generators

Our contribution in this paper is to design an adaptive neural network (NN) based control of a permanent magnet synchronous generator in order to overcome the effect of structured and unstructured uncertainties and external disturbances, in addition to ensuring the stability of the system, the proposed robust control strategy compensates adaptively for significant uncertainties present in uncertain nonlinear systems. First, we present the vector control of PMSG model applying two tests: with and without constrains, and then, the neural network control with PI controllers. However, conventional controllers suffer from limitations due to the presence of these high uncertainties. Therefore, we aim to improve the efficiency of artificial neural control by using dynamic compensators instead of PI controllers. The estimated states are used as inputs to the neural network (NN). Simulations of the proposed control algorithm are conducted then compared to the previous controllers: vector control and neural network control with PI controllers. Furthermore, using dynamic compensators in artificial neural control achieved both efficiency and optimality. Results have been successfully confirmed via computer simulations using MATLAB.

The Distributed Adaptive Bipartite Consensus Tracking Control of Networked Euler–Lagrange Systems with an Application to Quadrotor Drone Groups

Actuator faults and external disturbances, which are inevitable due to material fatigue, operational wear and tear, and unforeseen environmental impacts, cause significant threats to the control reliability and performance of networked systems. Therefore, this paper primarily focuses on the distributed adaptive bipartite consensus tracking control problem of networked Euler–Lagrange systems (ELSs) subject to actuator faults and external disturbances. A robust distributed control scheme is developed by combining the adaptive distributed observer and neural-network-based tracking controller. On the one hand, a new positive definite diagonal matrix associated with an asymmetric Laplacian matrix is constructed in the distributed observer, which can be used to estimate the leader’s information. On the other hand, neural networks are adopted to approximate the lumped uncertainties composed of unknown matrices and external disturbances in the follower model. The adaptive update laws are designed for the unknown parameters in neural networks and the actuator fault factors to ensure the boundedness of estimation errors. Finally, the proposed control scheme’s effectiveness is validated through numerical simulations using two types of typical ELS models: two-link robot manipulators and quadrotor drones. The simulation results demonstrate the robustness and reliability of the proposed control approach in the presence of actuator faults and external disturbances.

Fault-tolerant control of underactuated unmanned surface vessels in presence of deception and injection attacks

This paper studies the trajectory tracking control problem of underactuated unmanned surface vessels (USVs) under the influence of internal and external uncertainties, actuator faults, and injection and deception attacks. In the control design, we separately designed virtual adaptive laws to compensate for the time-varying gains suffered by the kinematics channel and combined the event-triggered control (ETC) mechanism to establish an online approximator to compensate for the time-varying gains of the kinematics channels and the loss-of-effectiveness (LOE) and bias faults suffered by the system. Combining robust neural damping technology and finite-time disturbance observer (FTDO), the dynamic uncertainties and external disturbances are suppressed. Finally, a novel robust adaptive trajectory tracking control scheme is proposed. The scheme achieves active compensation for heterogeneous uncertainties including internal and external uncertainties, actuator faults, and attack signals. The Lyapunov stability theory analysis shows that all signals of the tracking system are bounded. The simulation results prove the effectiveness of this control scheme.

Robust control design of uncertain nonlinear systems: a self-compensating and self-tuning approach

ABSTRACT The problem of robust stabilisation is considered for a class of uncertain nonlinear systems with linear control term. It is not required that the unforced nominal (nonlinear) system has to be stable and/or has to be known, which means that its Lyapunov function need not be known and/or found for designing a stabilising control scheme to compensate the effect of uncertainty on the systems like some traditional control design approaches. For such a class of uncertain nonlinear systems, a design approach, called a self-compensating and self-tuning one, is presented whereby some robust state feedback control schemes can be easily synthesised. The proposed design approach can result in a linear state feedback control scheme with a time-varying control gain for such a class of uncertain nonlinear systems, which implies the simplicity of the design approach and control results. In addition, the system uncertainty is also not required to have to satisfy the matching conditions.

Resilient robust model predictive load frequency control for smart grids with air conditioning loads

AbstractThis paper investigates the robust model predictive load frequency control problem for smart grids with wind power under cyber attacks. To accommodate intermittent power generation, the demand response of the system is considered by involving the air conditioning loads in the frequency regulation. In addition, the system uncertainties produced by the air conditioning load users and wind turbines when replacing traditional generator sets are considered. By using the cone complementary linearization algorithm and the linear matrix inequality technique, a resilience robust model predictive control strategy with mixed performance indexes is proposed. Furthermore, a rigorous derivation of the recursive feasibility of robust model predictive control is given. Finally, the simulation results of the two‐area load frequency control scheme show that the proposed model predictive control strategy is capable of realizing the load frequency control of the multi‐area smart grid and is robust to the parameter uncertainties and frequency regulation of the system. The results also show that the proposed model predictive control strategy has some resistance to cyber attacks and external disturbances.

Model experimental studies on active heave compensation control strategy for electric-driven offshore cranes

Ship-mounted heave compensation offshore cranes are indispensable for isolating connected payloads from the support vessel during lifting operations under harsh sea conditions. In this paper, an innovative adaptive robust control strategy is presented for the electric-driven active heave compensation (EDAHC) system, combining an equivalent model predictive control (EMPC) method with a bias proportional integral derivative (BPID) framework to effectively mitigate the adverse effects of wave-induced heave motions from the support vessel on the suspended payload. Building upon the inherent field-oriented control in the permanent magnet synchronous motor (PMSM), the BPID-based control structure is introduced, motivated by its prompt responsiveness and robust resistance against model discrepancies and irregular disturbances. Facilitated by a torque compensation mechanism, the EMPC-based control scheme, synthesized with an autoregressive integrated moving average (ARIMA)-based heave motion prediction algorithm, is subsequently developed to achieve nonlinear friction deadzone correction and adaptive regulation of BPID parameters, thereby ensuring optimal performance of the EDAHC system within specified state and input constraints. Comparative experimental tests conducted on a scaled EDAHC testbed validate the superior capabilities of the proposal in station-keeping, position tracking, constraint satisfaction, and robustness against parametric uncertainties.

ε-Nash Equilibrium of Pursuer–Evader–Defender Missile Navigation Dynamic Games

This research is dedicated to developing a min–max robust control strategy for a dynamic game involving pursuers, evaders, and defenders in a multiple-missile scenario. The approach employs neural dynamic programming, utilizing multiple continuous differential neural networks (DNNs). The competitive controller devised addresses the robust optimization of a joint cost function that relies on the trajectories of the pursuer–evader–defender system, accommodating an uncertain mathematical model while adhering to control restrictions. The dynamic programming min–max formulation facilitates robust control by accounting for bounded modeling uncertainties and external disturbances for each game component. The value function of the Hamilton–Jacobi–Bellman (HJB) equation is approximated by a DNN, enabling the estimation of the closed-loop formulation for the joint dynamic game with state restrictions. The controller’s design is grounded in estimating the state trajectory under the worst possible uncertainties and perturbations, providing a robustness factor through the robust neural controller. The learning law class for the time-varying weights in the DNN is generated by studying the HJB partial differential equation for the missile motion for each player in the dynamic game. The controller incorporates the solution of the obtained learning laws and a time-varying Riccati equation, offering an online solution to the control implementation. A recurrent algorithm, based on the Kiefer–Wolfowitz method, adjusts the initial conditions for the weights to satisfy the final condition of the given cost function for the dynamic game. A numerical example is presented to validate the proposed robust control methodology, confirming the optimization solution based on the DNN approximation for Bellman’s value function.

Data-Driven Tube-Based Robust Predictive Control for Constrained Wastewater Treatment Process.

The wastewater treatment process (WWTP) is characterized by unknown nonlinearity and external disturbances, which complicates the tracking control of dissolved oxygen concentration (DOC) within operational constraints. To address this issue, a data-driven tube-based robust predictive control (DTRPC) strategy is proposed to achieve stable tracking control of DOC and satisfy the system constraints. First, a tube-based robust predictive control (TRPC) strategy is designed to deal with system constraints and external disturbances. Specifically, a nominal controller is designed to ensure that the nominal output accurately tracks the set-point under tightened constraints, while an auxiliary feedback controller is designed to suppress disturbances and restore the nominal performance of the disturbed WWTP. Second, two fuzzy neural network identifiers are employed to provide accurate predictive outputs for the control process, overcoming the challenges of modeling the WWTP with strong nonlinearity and unknown dynamics. Third, the generalized multiplier method is utilized to solve the constrained optimization problem to obtain the nominal control law, and the gradient descent algorithm is used to obtain the auxiliary control law. The implementation of this composite controller ensures the satisfaction of the system constraints and the effective suppression of disturbances. Finally, the feasibility and stability of the proposed DTRPC strategy are guaranteed through rigorous theoretical analysis, and its effectiveness is demonstrated through the simulations on the benchmark simulation model No.1.

Tube-based MPC strategy for load frequency control of multi-area interconnected power system with HESS

With the rapid development of power generation technology and the increasing demand from power users, multi-area interconnected power systems have become a development trend. This article proposes a new robust tube-based model predictive control strategy (MPC) for multi-area interconnected power systems with hybrid energy storage systems (HESS), based on the application of HESS to optimize the performance of load frequency control in power systems. The proposed strategy is mainly based on a new robustness constraint, which effectively handles uncertain disturbances within interconnected power system by compressing the disturbance invariant set. This strategy effectively alleviates the problems caused by inconsistency in multi-area interconnection modes. In addition, a sufficient stability criterion is derived to ensure the robust stability of the system, even under uncertain disturbances. By modeling a four-area interconnected power system with HESS, the frequency fluctuations of different methods are compared and discussed in each area. Based on the constructed software-in-the-loop setup, the effectiveness and feasibility of the proposed strategy are verified by experiments.

Hybrid offline–online neural identification-based robust adaptive tracking control for quadrotors

This paper proposes a novel hybrid offline–online neural identification-based robust adaptive control strategy for quadrotors subject to parameter uncertainties and external disturbances. A new method of using hybrid offline–online neural identification is developed to compensate for the residual force and moment caused by parameter uncertainties. Unlike previous methods that ignore the relevance of uncertainties in the time dimension, the proposed neural identification method mines temporal features of states from historical data by introducing long short-term memory (LSTM) networks, resulting in high identification accuracy. Furthermore, an online adaptation update law is designed to optimize the weights of the network estimates for strong robustness. Consequently, based on the identification of the network, a robust tracking controller on SE(3) is constructed, which is capable of attenuating the bounded disturbances by introducing anti-disturbance components. Finally, numerical simulations and experiments in the real physical world are carried out to verify the performance. The experimental results demonstrate that the proposed strategy not only achieves more accurate uncertainty identification in comparison to the existing methods, but also realizes a 44.28% reduction in the root-mean-square error (RMSE) of the position under the lump uncertainties, which illustrates enhanced robustness and generalizability. Video: https://youtu.be/3kIG5fcQaVE.

Robust predictive control strategy for grid‐connected inverters with ultra‐local model based on linear matrix inequality

Robust predictive control strategy for grid‐connected inverters with ultra‐local model based on linear matrix inequality