Robust Multi-input Multi-output Research Articles

Cross-Coupled Dynamics and MPA-Optimized Robust MIMO Control for a Compact Unmanned Underwater Vehicle

A compact, 3-degrees-of-freedom (DoF), low-cost, remotely operated unmanned underwater vehicle (UUV), or MicroROV, is custom-designed, developed, instrumented, and interfaced with a PC for real-time data acquisition and control. The nonlinear equations of motion (EoM) are developed for the under-actuated, open-frame, cross-coupled MicroROV utilizing the Newton-Euler approach. The cross-coupling between heave and yaw motion, an important dynamic of a class of compact ROVs that is barely reported, is investigated here. This work is thus motivated towards developing an understanding of the physics of the highly coupled compact ROV and towards developing model-based stabilizing controllers. The linearized EoM aids in developing high-fidelity experimental data-driven transfer function models. The coupled heave-yaw transfer function model is improved to an auto-regressive moving average with exogenous input (ARMAX) model structure. The acquired models facilitate the use of the multi-parameter root-locus (MPRL) technique to design baseline controllers for a cross-coupled multi-input, multi-output (MIMO) MicroROV. The controller gains are further optimized by employing an innovative Marine Predator Algorithm (MPA). The robustness of the designed controllers is gauged using gain and phase margins. In addition, the real-time controllers were deployed on an onboard embedded system utilizing Simulink′s automatic C++ code generation capabilities. Finally, pool tests of the MicroROV demonstrate the efficacy of the proposed control strategy.

Robust MIMO Controller Design for VSC-Based Microgrids: Sequential Loop Closing Concept and Quantitative Feedback Theory

Like other multi-input multi-output (MIMO) systems, adverse effects of interactions among control loops on the system performance is one of the most challenging issues in the control of microgrids (MGs). In this paper, a robust MIMO controller is proposed for the MGs to solve the problem of power sharing among its distributed generators systematically. To this end, the relative gain array (RGA) matrix is used to address the problem of input-output pairing, and adverse effects of interactions among the control loops are decreased by designing proper pre-compensators. The sequential loop closing technique is employed, and the quantitative feedback theory (QFT) is used to achieve robust stability, the robust tracking performance, and the desired level of disturbance attenuation for all the control loops. The simulation and experimental results reveal the desirable and acceptable performance of the proposed control strategy.

Robust multi-input multi-output adaptive fuzzy terminal sliding mode control of deep brain stimulation in Parkinson\u2019s disease: a simulation study

Deep brain stimulation (DBS) has become an effective therapeutic solution for Parkinson’s disease (PD). Adaptive closed-loop DBS can be used to minimize stimulation-induced side effects by automatically determining the stimulation parameters based on the PD dynamics. In this paper, by modeling the interaction between the neurons in populations of the thalamic, the network-level modulation of thalamic is represented in a standard canonical form as a multi-input multi-output (MIMO) nonlinear first-order system with uncertainty and external disturbances. A class of fast and robust MIMO adaptive fuzzy terminal sliding mode control (AFTSMC) has been presented for control of membrane potential of thalamic neuron populations through continuous adaptive DBS current applied to the thalamus. A fuzzy logic system (FLS) is used to estimate the unknown nonlinear dynamics of the model, and the weights of FLS are adjusted online to guarantee the convergence of FLS parameters to optimal values. The simulation results show that the proposed AFTSMC not only significantly produces lower tracking errors in comparison with the classical adaptive fuzzy sliding mode control (AFSMC), but also makes more robust and reliable outputs. The results suggest that the proposed AFTSMC provides a more robust and smooth control input which is highly desirable for hardware design and implementation.

Designing a robust controller for a lower limb exoskeleton to treat an individual with crouch gait pattern in the presence of actuator saturation

Crouch gait is a gait anomaly observed in youngsters with cerebral palsy (CP). Rehabilitation robots are useful for treating individuals with crouch gait. Multiple factors have impact on crouch, including contracture, spasticity, weak motor control, and muscle feebleness, which make the designing and controlling of these exoskeletons for this population a challenging job. A harsh kinematic trajectory enforced by an exoskeleton control strategy may place individuals with spasticity at a high risk of muscle tissue injury. Therefore, in this article, a multi-input multi-output (MIMO) control method is proposed to reduce this risk and improve crouch gait pattern. A constrained control law is used in the model since high power demands may threaten the wearer. In addition, the controller needs to be robust enough against external disturbances and uncertainties. Thus, a nonlinear disturbance observer (NDO) is presented to compute the wearer-generated muscular torque and the uncertainties in the modeling. In addition, a robust constrained MIMO backstepping sliding controller (CMBSC) based on NDO is used to deal with the effect of actuator saturation and uncertainties. A simulation test was used to validate the proposed model and controller. The results of Simulation confirmed the efficiency of the proposed control method when applied to crouch gait with subject specific gait reference. Then, some experimental tests were undertaken to validate the efficiency of the proposed controller.

Multivariable State-Feedback Controller Design for PV Inverter Connected to a Weak Grid

This paper presents a robust multi-input multi-output (MIMO) state-feedback control scheme for a photovoltaic (PV) inverter connected to a weak grid. For a weak grid, the point of common coupling (PCC) voltage is very sensitive to the power disturbances and it is dynamically coupled to the PLL dynamics. So far, most of the control methods do not take into accounts these couplings. Therefore, in this paper, the MIMO controller was designed taking into account the dynamics of the phase-locked loop (PLL) and coupling effects between PCC voltage and the active power to enhance the system’s robustness. As result, the robust performance of the PV inverter interfaced to a weak grid was yielded. In addition, the sensitivity of the system to PLL was eliminated, allowing the use of larger PLL bandwidth even in a very weak grid. Based on the eigenvalues analysis method, a comparative study between the proposed control method and the conventional vector control method was performed. The proposed method is verified with simulations in PLECS and real-time simulations in the RT Box. The results show that the proposed MIMO control method preserves the system stability robustness against any change of grid strength, generated power and PLL bandwidth.

Robust Single-Carrier Frequency-Domain Equalization for Broadband MIMO Systems With Imperfect Channel Estimation

Single-carrier frequency-domain equalization (SC-FDE) with multi-input multi-output (MIMO) has been recognized as an alternative technology to orthogonal frequency-division multiplexing with MIMO for broadband wireless communication systems because of its single carrier transmission advantages. Conventional SC-FDE MIMO schemes are designed under the assumption of perfect channel estimation. In this paper, we investigate the robust SC-FDE MIMO design for systems with imperfect channel estimation. Based on a statistical model for channel estimation, the optimal equalization coefficients for the robust SC-FDE MIMO schemes with both parallel interference cancellation and successive interference cancellation (SIC) are derived with the objective of minimizing the sum of the multiple data streams’ mean square errors (sum-MSE). We propose an optimal ordering algorithm in the sense of minimum sum-MSE for the SC-FDE MIMO scheme with SIC, and further propose a low complexity sub-optimal ordering algorithm. The bit-error-rate (BER) performance in the uncoded case is analyzed and a tight BER approximation is derived. Numerical results show that the proposed robust SC-FDE MIMO schemes achieve 1.5dB~4dB gains in $ E_{b}/N_{0}$ over the conventional non-robust schemes.

Robust MIMO precoding for cognitive multiuser relay networks with imperfect channel state information

In this paper, a novel robust precoder with imperfect channel state information(CSI)is proposed for multi-input multi-output(MIMO)cognitive multiuser networks equipped with relays. In the proposed model, the secondary users(SUs)are allowed to share the spectrum with the primary users(PUs)when the interference temperature( IT)is below a specific threshold. The transmitting strategy of relays is amplify-and-forward(AF), and the CSI error is characterized in terms of spherical uncertainty region. A minmax problem for the transmit power of the relays is considered when the mean square error(MSE)of SUs and the IT of PU meet their corresponding thresholds, and it is transformed into a semi-definite programming(SDP)problem to search for the solution. Numerical simulations demonstrate the effectiveness of the proposed precoder.

Investigation of Multi-Input Multi-Output Robust Control Methods to Handle Parametric Uncertainties in Autopilot Design.

Some level of uncertainty is unavoidable in acquiring the mass, geometry parameters and stability derivatives of an aerial vehicle. In certain instances tiny perturbations of these could potentially cause considerable variations in flight characteristics. This research considers the impact of varying these parameters altogether. This is a generalization of examining the effects of particular parameters on selected modes present in existing literature. Conventional autopilot designs commonly assume that each flight channel is independent and develop single-input single-output (SISO) controllers for every one, that are utilized in parallel for actual flight. It is demonstrated that an attitude controller built like this can function flawlessly on separate nominal cases, but can become unstable with a perturbation no more than 2%. Two robust multi-input multi-output (MIMO) design strategies, specifically loop-shaping and μ-synthesis are outlined as potential substitutes and are observed to handle large parametric changes of 30% while preserving decent performance. Duplicating the loop-shaping procedure for the outer loop, a complete flight control system is formed. It is confirmed through software-in-the-loop (SIL) verifications utilizing blade element theory (BET) that the autopilot is capable of navigation and landing exposed to high parametric variations and powerful winds.

Robust multi-objective control of hybrid renewable microgeneration systems with energy storage

Microgeneration technologies are positioned to address future building energy efficiency requirements and facilitate the integration of renewables into buildings to ensure a sustainable, energy-secure future. This paper explores the development of a robust multi-input multi-output (MIMO) controller applicable to the control of hybrid renewable microgeneration systems with the objective of minimising the electrical grid utilisation of a building while fulfilling the thermal demands. The controller employs the inverse dynamics of the building, servicing systems, and energy storage with a robust control methodology. These inverse dynamics provides the control system with knowledge of the complex cause and effect relationships between the system, the controlled inputs, and the external disturbances, while an outer-loop control ensures robust, stable control in the presence of modelling deficiencies/uncertainty and unknown disturbances. Variable structure control compensates for the physical limitations of the systems whereby the control strategy employed switches depending on the current utilisation and availability of the energy supplies. Preliminary results presented for a system consisting of a micro-CHP unit, solar PV, and battery storage indicate that the control strategy is effective in minimising the interaction with the local electrical network and maximising the utilisation of the available renewable energy.

Data-based and analytic modelling for model-based control of a flexible beam

This paper evaluates and compares data-based and analytic modelling for model- based active vibration control based on the performance of the closed loop system for a simply supported flexible beam. The thin beam is controlled utilizing collocated piezo patch actuators and sensors. An electrodynamic shaker introduces a disturbance force in the direction of oscillation. The data-based design plant is identified using N4SID. The analytically obtained design plant is based on a one-dimensional finite element (FE) model. The comparison of the two entirely different modelling approaches is validated using a variety of different optimal & robust multi-input multi-output (MIMO) control design methods.

High Dynamic Engine-Dynamometer Identification and Control

In this paper, a high dynamic test-bed control problem for high dynamic vehicle simulation is studied in order to improve the tracking of speed and torque. As the system is nonlinear and square (2x2) with two inputs and two outputs, a multi-input multi-output (MIMO) system-identification problem is discussed. The test-bed data are processed using a fast frequency domain methodology to build a nominal linear plant and a perturbation model. Based on this perturbed plant, the CRONE Control-System Design approach (a fractional order of differentiation based methodology) is used to design a robust MIMO controller, which maintains performance around a wide set of operating points. Finally, experimental results show the relevance of the proposed methodology.

Robust MIMO Precoding for Several Classes of Channel Uncertainty

The full potential of multi-input multi-output (MIMO) communication systems relies on exploiting channel state information at the transmitter (CSIT), which is, however, often subject to some uncertainty. In this paper, following the worst-case robust philosophy, we consider a robust MIMO precoding design with deterministic imperfect CSIT, formulated as a maximin problem, to maximize the worst-case received signal-to-noise ratio or minimize the worst-case error probability. Given different types of imperfect CSIT in practice, a unified framework is lacking in the literature to tackle various channel uncertainty. In this paper, we address this open problem by considering several classes of uncertainty sets that include most deterministic imperfect CSIT as special cases. We show that, for general convex uncertainty sets, the robust precoder, as the solution to the maximin problem, can be efficiently computed by solving a single convex optimization problem. Furthermore, when it comes to unitarily-invariant convex uncertainty sets, we prove the optimality of a channel-diagonalizing structure and simplify the complex-matrix problem to a real-vector power allocation problem, which is then analytically solved in a waterfilling manner. Finally, for uncertainty sets defined by a generic matrix norm, called the Schatten norm, we provide a fully closed-form solution to the robust precoding design, based on which the robustness of beamforming and uniform-power transmission is investigated.

Inversion-based robust feedforward–feedback two-degree-of-freedom control approach for multi-input multi-output systems with uncertainty

In this study, an inversion-based robust feedforward–feedback two-degree-of-freedom (2DOF) control approach is proposed to improve the tracking performance and robustness for multi-input multi-output (MIMO) systems with uncertainty. The proposed approach enhances trajectory tracking precision by robust-inversion feedforward control and combats the adverse effects caused by system uncertainty, coupling and disturbance by H∞ mixed sensitivity robust MIMO feedback control. The main contributions of this paper are as follows: (i) a combination of robust diagonal dominance-based static pre-compensation and inversion-based robust feedforword–feedback 2DOF control, (ii) extending the single-input single-output (SISO) robust-inversion-based feedforward control to MIMO system by introducing the optimal coefficients which not only consider the uncertainties of diagonal transfer functions, but also take the non-diagonal elements in MIMO transfer function matrix into account, (iii) a systematic integration of H∞ mixed sensitivity robust feedback control and inversion feedforward control. A numerical example and simulation results illustrate a better performance obtained by the proposed control approach compared with H∞ mixed sensitivity robust feedback control and 2DOF control approach combined with the regular SISO robust-inversion-based feedforward control and robust feedback control.

A High-Integrity Multivariable Robust Control With Application to a Process Control Rig

This brief presents a systematic approach for the design of a robust decoupling precompensator using an approximate right inverse (ARI) of a system, where the problem of finding an ARI is presented as an L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -gain minimization problem. Furthermore, new LMIs are presented to analyze worstcase L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -gain for an uncertain system. These LMIs use extra variables to eliminate product terms between system state matrices and the Lyapunov matrix. This elimination enables the use of a parameter dependent Lyapunov function in a systematic way. These LMIs are extended to synthesis both constant and dynamic precompensators as well. Using the synthesis and the analysis LMIs, a combined genetic-LMI-algorithm is also presented to find a suitable precompensator that achieves diagonal dominance for systems with input uncertainties. Some previously presented LMIs for pole clustering are also modified to make them compatible with newly presented LMIs. The proposed approach is applied to the design of a high integrity robust multiinput multioutput controller for a process control rig which consists of a temperature and a flow rate control loop. The system has an input uncertainty of about 20%. It is shown that the closed-loop system poses a high integrity while being robust with respect to input uncertainties. The controller is also applied to the real plant to verify that the proposed algorithm and the desired results are obtained.

Robust control of sound in an active acoustic grillage

This study evaluates the results of robust control of an active acoustic grillage (AAG). The AAG is an array of sound sources and microphones forming an acoustic barrier trapping sound but allowing air and light to pass through. Due to strong coupling between the cells of the AAG and resulting acoustic ‘‘feedback,’’ a multivariable H∞-synthesis is used for the controller design to yield a stable and robust closed loop system. The multi-input–multi-output (MIMO) design is compared with a linear-quadratic-Gaussian (LQG) design and a classical SISO approach that neglects the cross-coupling between the cells. System modeling, H∞-synthesis including the weight selection, simulation and implementation issues are discussed. The H∞-controller is tested on a four-cell grillage installed on the rectangular duct with cross section 50×50 cm. The robust controller provides 10–12-dB attenuation in the frequency range of 100–300 Hz that is superior to performance achieved with LQG or SISO methods. The controller implementation includes additional features like calibration and real-time monitoring. The work demonstrates benefits of robust MIMO design in noise control systems with multiple sensors and actuators where cross coupling is significant.