Disturbance Attenuation Research Articles

ELLIPSOIDAL DESIGN OF SLIDING MODE CONTROL FOR CAR ACTIVE SUSPENSION SYSTEM

The issue of state feedback sliding mode control (SMC) for a particular kind of active quarter-car suspension system is discussed in this work. The suspension systems' dynamic system is built with the three control objectives of maximal actuator control force, suspension deflection, and ride comfort in mind. The next step is to build the state-feedback controller with the disturbance (vibration) attenuation level in mind to guarantee the asymptotic stability of the closed-loop system. The algorithm for SMC design is introduced. It is predicated on choosing the sliding surface correctly using the invariant ellipsoid approach. The system motion in the sliding mode is guaranteed to be little affected by mismatched disturbances according to the control architecture. Furthermore, the presence of admissible controllers is articulated in terms of LMIs through the use of Lyapunov theory and the linear matrix inequality (LMI) technique. The objective is to create a desired dynamic state-feedback controller when these requirements are met. Lastly, a quarter-car model is used to illustrate how successful a suggested approach is.

Modified TRIAD Method for Solving the Problem of a Moving Object Orientation

Currently, it is relevant to create moving objects orientation systems based on the integration of various types of primary information sensors. One way to solve the orientation problem is to use the TRIAD (Tri-Axial Orientation Determination) method which allows determining the matrix of direction cosines between two coordinates. The traditional TRIAD force method is based on the use of two reference vectors – of gravitational and geomagnetic fields, measured by accelerometers and magnetometers, respectively. The disadvantage of this method is – appearance of additional deviations during the accelerated movement of the object and influence of primary information sensors’ random errors. Modified TRIAD method which is based on measurements of three triads of sensors: magnetometers, accelerometers and gyroscopes was proposed in the article. Estimates of acceleration vectors of gravitational and geomagnetic fields were calculated taking into account gyroscope measurements. Then these estimates were combined with the accelerometers’ and magnetometers’ data. The complex gravitational and geomagnetic fields’ accelerations were used to form the direction cosine matrix by the TRIAD method. The suggested modified method can be used to implement free-form moving objects’ orientation systems, since it is 6–8 times more accurate compared to the classic TRIAD method. Attenuation of random sensor errors and disturbances due to object acceleration can be adjusted by use of weigh factors.

Robust Control of Chat-PM: A Switched Singular System with Mode-dependent Bounded Nonlinearity

This paper is concerned with the robust control of the composite hybrid aerial-terrestrial precise manipulator (Chat-PM), a quadrotor-based robot with aerial/terrestrial movement and payload transportation capabilities. Given the distinct dynamics between aerial and terrestrial locomotion modes, Chat-PM is modeled as a type of switched singular system. A nonlinear term is contained in the system to model the coupling between translational and rotational movements of Chat-PM, which is proven to be norm-bounded and mode-dependent. Besides, the estimation error of the robotic arm's interaction force/torque is treated as the disturbance of the system. By means of the mixed H2/H∞ approach, numerically testable stability criterion are obtained, based on which the existence conditions for controllers with satisfactory transient and disturbance attenuation performance are provided. Compared to traditional studies assuming the nonlinearity term to be mode-independent, the conservatism in the controller design is reduced. Experimental results are provided to demonstrate the effectiveness of the proposed approach.

Modified preview repetitive control with equivalent-input-disturbance based on two-dimensional model

This paper deals with the problem of two-dimensional (2D) system-based modified preview repetitive control (MPRC) with equivalent-input-disturbance (EID) for continuous-time systems. First, an EID-based MPRC law is used to improve the tracking performance and disturbance attenuation. Next, by using a new equality constraint, a continuous-discrete 2D system is established and the design problem of the EID-based MPRC law is transformed into the stability problem of the 2D system. Then, we employ the 2D system theory together with the linear matrix inequality (LMI) approach, a sufficient condition is derived to ensure the stability of the closed-loop system. By solving an LMI, the gains of the controller and state observer can be obtained. Finally, two numerical examples are provided to illustrate the effectiveness and superiority of the developed controller design technique.

New Predictor‐Based Anti‐Disturbance Control for Discrete‐Time Systems With Input Delay Subject to Time‐Varying Uncertainties

ABSTRACTIn this article, a new predictor‐based anti‐disturbance control scheme is proposed for discrete‐time systems with input delay subject to time‐varying uncertainties, by only using the system output measurement. A high‐order extended state observer is firstly constructed to simultaneously estimate the system state, unknown disturbance and its high‐order difference with specification. Two alternative designs of the system state and disturbance predictors are then presented to construct an advanced anti‐disturbance control scheme. The disturbance attenuation performance of the resulting closed‐loop system is quantitatively analyzed. Moreover, a matrix‐inequality‐based sufficient condition is established to evaluate robust stability of the closed‐loop system under time‐varying uncertainties. Finally, a benchmark example is adopted to demonstrate the effectiveness and advantages of the proposed control schemes over the existing methods.

Finite-time bounded security control of networked switched stochastic systems

This paper is dedicated to discussing stochastic finite-time bounded control of switched stochastic systems with feedback information sampled by an event-triggered mechanism and transmitted by network. Besides the incomplete information because of sample, data loss and corruption during information transmission caused by hybrid cyber attacks including DoS attacks and false-data injection attacks, are also taken into consideration. Based on the above incomplete information, state feedback controllers are established to fulfil stochastic finite-time boundedness of switched stochastic systems and make it have a certain disturbance attenuation capability, i.e. have a finite-time L 2 -gain, successfully. Sufficient conditions are cast into a convex optimisation problem which can be easily solved by fixing some parameters. Simulation results are employed to verify the validity of results.

Performance Optimization with LPV Synthesis for Disturbance Attenuation in Planar Motors

Optimizing the performance of motion control systems with variations in nonlinear parameters is not an easy task. To accomplish this task, it is important to design the controller using the linear system approach. In this study, a linear parameter varying (LPV) control method is proposed in which nonlinearities are treated as parameter variations for planar motors. The proposed control method consists of the force and torque modulation with the commutation scheme and the nonlinear current controller with H∞ state feedback control in the form of LPV synthesis to improve the position-tracking performance. An interpolated gain-scheduling controller based on LPV synthesis is determined by applying H∞ control to a linear matrix inequality technique. An interpolated gain-scheduling controller can attenuate disturbance without disturbance estimation. The effectiveness of the proposed control method is evaluated using simulation results and compared with the conventional proportional–integral–derivative control to verify both improved position-tracking performance and disturbance attenuation.

Q‐learning‐based H∞ control for LPV systems

AbstractIn this paper, a model‐free H∞ control method is developed for discrete‐time linear parameter‐varying (LPV) systems by leveraging Q‐learning, which employs the data encompassing system states, control inputs, exogenous disturbance, and time‐varying convex hull rather than the dynamical model known a priori. A policy iteration algorithm presented via linear matrix inequality formed by such collected data is developed with convergence guarantee. Our analysis demonstrates that, in the presence of sufficiently rich disturbances, the attenuation level converges to a lower value than the traditional H∞ control solution derived from an exact LPV model. A numerical example is demonstrated to verify the stabilization, disturbance attenuation, adaptivity, and convergence of the H∞ attenuation level. Moreover, a continuous stirred tank reactor (CSTR) approximated by a discrete‐time LPV system is employed to show the practical potential of the developed model‐free approach.

A reservoir computing approach in active noise control for vehicle applications

Reservoir Computing (RC) is an alternative approach to Recurrent Neural Networks (RNN) that can avoid some shortcomings of conventional gradient decent-training that are mainly related to high computational complexity and the reduced ability to "learn" long-range dependencies. In RC the input data are transformed into patterns in a high-dimensional space by an RNN called the "reservoir", which remains unchanged during training. The desired output signal is generated as a linear combination of the neuron's signals from the input-excited reservoir. The linear part of the RC architecture, which is called the "readout" can be trained with a simple learning algorithm such as linear regression. In this work an RC architecture is proposed for the attenuation of acoustic disturbances encountered in vehicle cabins such as aircrafts and yachts. At first, a dataset of acoustic pressure measurements from real-world cabins was used to generate the readout and reservoir layers. Subsequent computer simulations demonstrated that the proposed architecture can successfully attenuate such acoustic disturbances. Finally, the performance of RC in Active Noise Cancellation task was compared with both the linear FxLMS algorithm and a conventional RNN, showing that it has several advantages regarding acoustic pressure reduction combined with relatively low computational complexity.

Dynamic modeling and modification of ternary semicontinuous distillation without a middle vessel for improved controllability and energy performance

Ternary distillation conventionally requires two sequential columns. In a process intensification technique, the second column is eliminated by operating the first column in a cyclic manner, where the intermediate component is withdrawn periodically from a side stream. This process, called semicontinuous distillation (SCD) without a middle vessel, can lower the separation costs significantly. However, it exhibits controllability challenges due to the periodic recycling of the side stream. In this work, the process controllability is improved by adding a surge tank in the side stream recycle path. The modification increases significantly the process robustness without changing the operation recipe. Also, the modified SCD can lower maintenance costs as the manipulated variables experience milder oscillations. Moreover, it is shown to have a faster startup than the original design, yielding about 13 % energy saving per feed processed and processing about 25 % more feed during the startup compared with the original design. The case studies show the surge tank volume should be chosen based on the trade-off between attenuation of undesired disturbances and slow-down of desired control actions. Also presented in this article is detailed hybrid discrete-continuous dynamic modeling of the SCD and how the resulting model is implemented in the open-source software OpenModelica.

Decentralized Robust Power System Stabilization Using Ellipsoid-Based Sliding Mode Control

Power systems are naturally prone to numerous uncertainties. Power system functioning is inherently unpredictable, which makes the networks susceptible to instability. Rotor-angle instability is a critical problem that, if not effectively resolved, may result in a series of failures and perhaps cause blackouts (collapse). The issue of state feedback sliding mode control (SMC) for the excitation system is addressed in this work. Control is decentralized by splitting the global system into several subsystems. The effect of the rest of the system on a particular subsystem is considered a disturbance. The next step is to build the state feedback controller with the disturbance attenuation level in mind to guarantee the asymptotic stability of the closed-loop system. The algorithm for SMC design is introduced. It is predicated on choosing the sliding surface correctly using the invariant ellipsoid approach. According to the control architecture, the system motion in the sliding mode is guaranteed to only be minorly affected by mismatched disturbances in power systems. Furthermore, the proposed controllers are expressed in terms of Linear Matrix Inequalities (LMIs) using the Lyapunov theory. Lastly, an IEEE test system is used to illustrate how successful the suggested approach is.

Observer-based quantized control for networked interconnected PDE systems with actuator failures

This paper proposes an observer-based quantized controller for parabolic partial differential equation systems interconnected by a nonlinear coupling protocol. First, a Markov jump model is introduced to describe various randomly occurring actuator failures, and an observer-based pointwise controller is designed under the averaged measurement scheme. Furthermore, taking into account the limitation of network communication resources, a quantization method is adopted to relieve bandwidth pressure. In addition, stability conditions of the closed-loop system with ℋ∞ disturbance attenuation performance are derived by utilizing appropriate Lyapunov functional and inequality techniques. Ultimately, the proposed method is applied to the Fisher equation to verify its feasibility and effectiveness.

A compact meta-learned neuro-fuzzy technique for noise-robust nonlinear control

Neuro-fuzzy systems show promise for adaptive control but can become complex due to the need to learn many parameters. This paper presents a resilient nonlinear controller that combines a simplified neuro-fuzzy system (Simp_NFS) and simplified neural network (Simp_NN) with only two meta-learnable parameters. This architecture enables fast and stable adaptation in uncertain nonlinear discrete-time systems. Simp_NFS utilizes interpretable hyperplane-based rules without antecedent parameters, simplifying the learning process to consequent weights. Simp_NN reduces complexity by replacing hidden-output weights with their mean. The hybrid auto-adaptive controller (HAC) combines the advantages of Simp_NFS and Simp_NN, significantly reducing the number of adaptive parameters compared to standard neuro-fuzzy methods for real-time control with limited resources. Simp_NFS provides structural adaptivity to handle uncertainties, while Simp_NN ensures reliable disturbance attenuation. The stability of HAC is proven using Lyapunov analysis. Extensive testing on challenging single-input single-output (SISO) and multi-input multi-output systems (MIMO) demonstrates that HAC improves performance by up to 82.55% compared to existing techniques. Key innovations include an ultra-compact meta-learned architecture, transparent online evolution of hyperplane clusters, and enhanced modeling capability for nonlinear uncertain systems. This interpretable neuro-fuzzy approach could enhance autonomy and safety by maintaining model transparency. The implementation of HAC is publicly available on GitHub at https://github.com/m-ferdaus/HAC.

Hidden Markov model‐based filtering for 2‐D Markov jump Roesser systems subject to analog fading channels

AbstractThis article considers the filtering problem for 2‐D Markov jump Roesser systems, in which the measurement signal is transmitted via analog fading channels. The time‐varying amplitude attenuation of the analog fading channels is described by the fading gain modeled as a random variable. The hidden Markov model is used to deal with the mode mismatch phenomenon between the plant and the filter, then a full‐order asynchronous filter is designed for 2‐D Markov jump Roesser systems under analog fading channels. The purpose is to introduce a filter to guarantee that the filtering error system is not only asymptotically mean square stable, but also satisfies the disturbance attenuation performance. An improved decoupling method is proposed to acquire the 2‐D filter parameters such that the conservatism of the 2‐D filter designed law can be effectively reduced. Finally, a verification example and Darboux equation are given to illustrate the validity of the introduced asynchronous filter design law.

Disturbance attenuation for neutral descriptor semi‐Markovian jump systems with saturation nonlinearity and time delays

AbstractThis article examines the construction of the tracking control as well as the disturbance attenuation for a class of neutral descriptor semi‐Markovian jump systems involving input time delays. On the basis of Matausek–Micic modified Smith prediction technique, a modified equivalent‐input‐disturbance and multiperiodic modified repetitive controller has been developed for obtaining the required results. In order to reject external disturbances actively, the modified equivalent‐input‐disturbance technique is utilized. To be more explicit, the modification of the Smith predictor block and integration of the transfer function provide accurate estimate, efficient attenuation of external disturbance and proper compensation for input time delays. While constructing the controller gain matrices, the specified matrix inequalities are solved concurrently. At last, the potential and applicability of the framed control law are illustrated through the simulated findings.

Dynamic event‐triggered control of 2‐D continuous systems in Roesser model

AbstractIn this paper, a dynamic event‐triggered control problem is discussed for 2‐D continuous systems by the Roesser model. In order to reduce communication frequency and avoid dependence on global information, a dynamic event‐triggered mechanism is constructed, which is more flexible than some existing event‐triggered schemes with fixed event‐triggered thresholds. Utilizing the dynamic event‐triggered mechanism, a state feedback controller is designed. By constructing a 2‐D Lyapunov function, sufficient conditions expressed in terms of linear matrix inequalities (LMIs) are firstly established such that the 2‐D system is asymptotically stable with a disturbance attenuation performance. It is also proved that the Zeno phenomenon is excluded. Finally, two examples are provided to illustrate the effectiveness of the proposed method.

[formula omitted] filtering for uncertain discrete-time singular switched positive systems with time delay and output quantization

This paper focuses on the design problem of an l1 filter for singular switched positive systems with time delay and uncertainty. In order to enhance resource efficiency and information transmission security, the quantization mechanism is introduced when designing the filter. Furthermore, sufficient conditions for causality, regularity, positivity, and exponential stability of the filtering error system are provided under the constraint of mode-dependent minimum dwell time (MDMDT). These conditions are obtained by selecting an appropriate co-positive Lyapunov function and are presented in the form of linear programming (LP). Subsequently, the disturbance attenuation performance is further analyzed, and a design methodology for the corresponding filter is outlined. Finally, a numerical example is presented to verify the validity of the theoretical results.

Data-driven fault detection for closed-loop T-S fuzzy systems with unknown system dynamics and its application to aero-engines

This paper considers the fault detection problem of closed-loop Takagi-Sugeno (T-S) fuzzy systems with unknown system dynamics. However, the unknown dynamics make the model-based detection methods being infeasible. To tackle this problem, a detection scheme is designed directly by using input/state data, and disturbance attenuation as well as fault sensitivity performance are then introduced within data-driven framework such that a multiobjective optimization problem is formulated to compute the parameters of detector. Moreover, to remove the existing limitation that sensor faults must occur, corresponding design conditions of detector are developed by considering the characteristics of fault frequency. In particular, a linearization approach via searching the minimum singular value of unknown matrix is further developed to handle the nonconvex problem caused by introducing the fault sensitivity performance. Finally, an aero-engine system is used to show the effectiveness and advantages of the developed method.

H−/H∞ filter‐based fault detection application to PEM fuel cell air‐feed system

AbstractThis paper focuses on diagnosing faults and disturbances in the polymer electrolyte membrane (PEM) fuel cell air‐feed system. The main objective is to design a robust filter, specifically of the type, to improve fault detection and attenuate perturbations. The norm is employed to assess the fault sensitivity performance of the detection process, while the norm guarantees the robustness of the proposed filter against disturbances. The approach utilized in this work is based on Lyapunov stability analysis, and the filter matrices are determined by solving a convex optimization problem formulated as linear matrix inequalities (LMIs). To illustrate the effectiveness of our results, a simulation is conducted under various disturbance and faulty scenarios. By integrating robust filtering techniques and addressing fault detection and disturbance attenuation, this study offers valuable insights for enhancing the performance and reliability of the PEM fuel cell air‐feed system.

Effective Nonlinear Predictive and CTC-PID Control of Rigid Manipulators

Effective nonlinear control of manipulators with dynamically coupled arms, like those with direct drives, is the subject of the paper. The main proposal of the paper are model-based predictive control (MPC) algorithms, with nonlinear state-space models and most recent disturbance attenuation technique. This technique makes controller design and calculations simpler, avoiding necessity of dynamic modeling of disturbances or resorting to additional techniques like SMC. The core of the paper are computationally effective MPC-NPL (Nonlinear Prediction and Linearization) algorithms, where computations at every sample are divided into two parts: prediction of initial trajectories using nonlinear model, then optimization using simplified linearized model. For a comparison a known CTC-PID algorithm, which is also model-based, is considered. It is applied in standard form and also proposed in more advanced CTC-PID2dof version. For all algorithms a comprehensive comparative simulation study is performed, for a direct drive manipulator under disturbances. Additional contribution of the paper is an investigation of influence of sampling period length and computational delay time on performance of the algorithms, which is practically important when using model-based algorithms and fast sampling.