Publications
113 results found
Yu S, Pan X, Chen B, et al., 2023, A real-time robust ecological-adaptive cruise control strategy for battery electric vehicles, IEEE Transactions on Transportation Electrification, ISSN: 2332-7782
This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy. The problem is formulated in the space-domain with a realistic description of the nonlinear electric powertrain model and motion dynamics to yield a convex optimal control problem (OCP). The OCP is solved by a novel robust model predictive control (RMPC) method handling various disturbances due to modelling mismatch and inaccurate leading vehicle information. The RMPC problem is solved by semi-definite programming relaxation and single linear matrix inequality (sLMI) techniques for further enhanced computational efficiency. The performance of the proposed real-time robust ecological-adaptive cruise control (REACC) method is evaluated using an experimentally collected driving cycle. Its robustness is verified by comparison with a nominal MPC which is shown to result in speed-limit constraint violations. The energy economy of the proposed method outperforms a state-of-the-art time-domain RMPC scheme, as a more precisely fitted convex powertrain model can be integrated into the space-domain scheme. The additional comparison with a traditional constant distance following strategy (CDFS) further verifies the effectiveness of the proposed REACC. Finally, it is verified that the REACC can be potentially implemented in real-time owing to the sLMI and resulting convex algorithm.
Xia J-Y, Li S, Huang J-J, et al., 2023, Metalearning-based alternating minimization algorithm for nonconvex optimization, IEEE Transactions on Neural Networks and Learning Systems, Vol: 34, Pages: 5366-5380, ISSN: 1045-9227
In this article, we propose a novel solution for nonconvex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of subproblems corresponding to each variable and then iteratively optimizes each subproblem using a fixed updating rule. However, due to the intrinsic nonconvexity of the original optimization problem, the optimization can be trapped into a spurious local minimum even when each subproblem can be optimally solved at each iteration. Meanwhile, learning-based approaches, such as deep unfolding algorithms, have gained popularity for nonconvex optimization; however, they are highly limited by the availability of labeled data and insufficient explainability. To tackle these issues, we propose a meta-learning based alternating minimization (MLAM) method that aims to minimize a part of the global losses over iterations instead of carrying minimization on each subproblem, and it tends to learn an adaptive strategy to replace the handcrafted counterpart resulting in advance on superior performance. The proposed MLAM maintains the original algorithmic principle, providing certain interpretability. We evaluate the proposed method on two representative problems, namely, bilinear inverse problem: matrix completion and nonlinear problem: Gaussian mixture models. The experimental results validate the proposed approach outperforms AM-based methods.
Xu W, Higgins M, Wang J, et al., 2023, Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach, IEEE TRANSACTIONS ON SMART GRID, Vol: 14, Pages: 3176-3188, ISSN: 1949-3053
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- Citations: 2
Georgiou A, Furqan T, Jaimoukha I, et al., 2023, Computationally efficient robust model predictive control for uncertain system using causal state-feedback parameterization, IEEE Transactions on Automatic Control, Vol: 68, Pages: 3822-3829, ISSN: 0018-9286
This paper investigates the problem of robustmodel predictive control (RMPC) of linear-time-invariant (LTI)discrete-time systems subject to structured uncertainty andbounded disturbances. Typically, the constrained RMPCproblem with state-feedback parameterizations is nonlinear(and nonconvex) with a prohibitively high computationalburden for online implementation. To remedy this, a novelapproach is proposed to linearize the state-feedback RMPCproblem, with minimal conservatism, through the use ofsemidefinite relaxation techniques. The proposed algorithmcomputes the state-feedback gain and perturbation onlineby solving a linear matrix inequality (LMI) optimization that,in comparison to other schemes in the literature is shownto have a substantially reduced computational burdenwithout adversely affecting the tracking performance of thecontroller. Additionally, an offline strategy that providesinitial feasibility on the RMPC problem is presented. Theeffectiveness of the proposed scheme is demonstratedthrough numerical examples from the literature.
Feng Z, Yu M, Evangelou SA, et al., 2023, Mu-synthesis PID control of full-car with parallel active link suspension under variable payload, IEEE Transactions on Vehicular Technology, Vol: 72, Pages: 176-189, ISSN: 0018-9545
This paper presents a combined μ -synthesis PID control scheme, employing a frequency separation paradigm, for a recently proposed novel active suspension, the Parallel Active Link Suspension (PALS). The developed μ -synthesis control scheme is superior to the conventional H∞ control, previously designed for the PALS, in terms of ride comfort and road holding (higher frequency dynamics), with important realistic uncertainties, such as in vehicle payload, taken into account. The developed PID control method is applied to guarantee good chassis attitude control capabilities and minimization of pitch and roll motions (low frequency dynamics). A multi-objective control method, which merges the aforementioned PID and μ -synthesis-based controls is further introduced to achieve simultaneously the low frequency mitigation of attitude motions and the high frequency vibration suppression of the vehicle. A seven-degree-of-freedom Sport Utility Vehicle (SUV) full car model with PALS, is employed in this work to test the synthesized controller by nonlinear simulations with different ISO-defined road events and variable vehicle payload. The results demonstrate the control scheme's significant robustness and performance, as compared to the conventional passive suspension as well as the actively controlled PALS by conventional H∞ control, achieved for a wide range of vehicle payload considered in the investigation.
Yu S, Pan X, Georgiou A, et al., 2023, A Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles, IEEE International Conference on Mechatronics (ICM), Publisher: IEEE
Xu W, Jaimoukha IM, Teng F, 2023, Robust Moving Target Defence Against False Data Injection Attacks in Power Grids, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, Vol: 18, Pages: 29-40, ISSN: 1556-6013
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- Citations: 2
Feng Z, Yu M, Evangelou S, et al., 2022, Feedforward PID control of full-car with parallel active link suspension for improved chassis attitude stabilization, IEEE Conference on Control Technology and Applications (CCTA 2022), Publisher: IEEE
PID control is commonly utilized in an active suspension system to achieve desirable chassis attitude, where, due to delays, feedback information has much difficulty regulating the roll and pitch behavior, and stabilizing the chassis attitude, which may result in roll over when the vehicle steersat a large longitudinal velocity. To address the problem of the feedback delays in chassis attitude stabilization, in this paper, a feedforward control strategy is proposed to combine with a previously developed PID control scheme in the recently introduced Parallel Active Link Suspension (PALS). Numerical simulations with a nonlinear multi-body vehicle model areperformed, where a set of ISO driving maneuvers are tested. Results demonstrate the feedforward-based control scheme has improved suspension performance as compared to the conventional PID control, with faster speed of response in brakein a turn and step steer maneuvers, and surviving the fishhook maneuver (although displaying two-wheel lift-off) with 50 mph maneuver entrance speed at which conventional PID control rolls over.
Xu W, Jaimoukha IM, Teng F, 2022, Physical Verification of Data-Driven Cyberattack Detector in Power System: An MTD Approach
Stealthy false data injection attacks (FDIAs) have been shown to compromise power system state estimation. The data-driven detector is a promising way to counter FDIAs. However, it suffers from low interpretability and thus introduces uncontrollable false alarms, which has been overlooked by the literature. This paper proposes to utilise moving target defence (MTD) as an additional physics layer to verify the decisions made by the data-driven detector. First, the data-driven anomaly detector is extended to identify part of the attack vector through iterative normality projection. Second, a novel MTD algorithm is formulated to maintain the high detection rate of the data-driven detector on the identified attack vector with minimum usage. The proposed algorithm is thoroughly tested under the IEEE bus-14 system.
Yu S, Pan X, Georgiou A, et al., 2022, Robust Model Predictive Control Framework for Energy-Optimal Adaptive Cruise Control of Battery Electric Vehicles, European Control Conference (ECC), Publisher: IEEE, Pages: 1728-1733
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- Citations: 1
Luo W, Zhang C, Jaimoukha IM, 2022, Sensor Failure-Tolerant Observer Design With Regional Pole Placement, IEEE CONTROL SYSTEMS LETTERS, Vol: 6, Pages: 2102-2107, ISSN: 2475-1456
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- Citations: 1
Georgiou A, Evangelou S, Jaimoukha I, et al., 2021, Tracking control for directional drilling systems using robust feedback model predictive control, 1st Virtual IFAC World Congress, Publisher: Elsevier, Pages: 11974-11981, ISSN: 2405-8963
A rotary steerable system (RSS) is a drilling technology which has been extensively studied and used for over the last 20 years in hydrocarbon exploration and it is expected to drill complex curved borehole trajectories. RSSs are commonly treated as dynamic robotic actuator systems, driven by a reference signal and typically controlled by using a feedback loop control law. However, due to spatial delays, parametric uncertainties and the presence of disturbances in such an unpredictable working environment, designing such control laws is not a straightforward process. Furthermore, due to their inherent delayed feedback, described by delay differential equations (DDE), directional drilling systems have the potential to become unstable given the requisite conditions. This paper proposes a Robust Model Predictive Control (RMPC) scheme for industrial directional drilling, which incorporates a simplified model described by ordinary differential equations (ODE), taking into account disturbances and system uncertainties which arise from design approximations within the formulation of RMPC. The stability and computational efficiency of the scheme are improved by a state feedback strategy computed offline using Robust Positive Invariant (RPI) sets control approach and model reduction techniques. A crucial advantage of the proposed control scheme is that it computes an optimal control input considering physical and designer constraints. The control strategy is applied in an industrial directional drilling configuration represented by a DDE model and its performance is illustrated by simulations.
Georgiou A, Tahir F, Evangelou S, et al., 2021, Robust moving horizon state estimation for uncertain linear systems using linear matrix inequalities, 59th IEEE Conference on Decision and Control - CDC 2020, Publisher: IEEE, Pages: 2900-2905
This paper investigates the problem of state estimation for linear-time-invariant (LTI) discrete-time systems subject to structured feedback uncertainty and bounded disturbances. The proposed Robust Moving Horizon Estimation (RMHE) scheme computes at each sample time tight bounds on the uncertain states by solving a linear matrix inequality (LMI) optimization problem based on the available noisy input and output data. In comparison with conventional approaches that use offline calculation for the estimation, the suggested scheme achieves an acceptable level of performance with reduced conservativeness, while the online computational time is maintained relatively low. The effectiveness of the proposed estimation method is assessed via a numerical example.
Hu C, Jaimoukha IM, 2021, New iterative linear matrix inequality based procedure for <i>H</i><sub>2</sub> and <i>H<sub>∞</sub></i> state feedback control of continuous-time polytopic systems, INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Vol: 31, Pages: 51-68, ISSN: 1049-8923
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- Citations: 6
Feng Z, Yu M, Cheng C, et al., 2020, Uncertainties Investigation and mu-Synthesis Control Design for a Full Car with Series Active Variable Geometry Suspension, International Federation of Automatic Control
Hu C, Liu C, Jaimoukha IM, 2020, Computation of Invariant Tubes for Robust Output Feedback Model Predictive Control, 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, Publisher: ELSEVIER, Pages: 7063-7069, ISSN: 2405-8963
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- Citations: 1
Hu C, Jaimoukha IM, 2020, New LMI Characterizations for <i>H</i><sub>∞</sub>-norm Guaranteed Cost Computation of Linear Systems with Polytopic Uncertainties, 59th IEEE Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 3957-3962, ISSN: 0743-1546
Hu C, Jaimoukha IM, 2020, Robust <i>H</i><sub>2</sub> and <i>H</i><sub>∞</sub> State Feedback Control for Discrete-time Polytopic Systems Using an Iterative LMI Based Procedure, 18th European Control Conference (ECC), Publisher: IEEE, Pages: 621-626
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- Citations: 1
Liu C, Tahir F, Jaimoukha IM, 2019, Full-complexity polytopic robust control invariant sets for uncertain linear discrete-time systems, International Journal of Robust and Nonlinear Control, Vol: 29, Pages: 3587-3605, ISSN: 1049-8923
This paper presents an algorithm for the computation of full‐complexity polytopic robust control invariant (RCI) sets, and the corresponding linear state‐feedback control law. The proposed scheme can be applied for linear discrete‐time systems subject to additive disturbances and structured norm‐bounded or polytopic uncertainties. Output, initial condition, and performance constraints are considered. Arbitrary complexity of the invariant polytope is allowed to enable less conservative inner/outer approximations to the RCI sets whereas the RCI set is assumed to be symmetric around the origin. The nonlinearities associated with the computation of such an RCI set structure are overcome through the application of Farkas' theorem and a corollary of the elimination lemma to obtain an initial polytopic RCI set, which is guaranteed to exist under certain conditions. A Newton‐like update, which is recursively feasible, is then proposed to yield desirable large/small volume RCI sets.
Zhang C, Jaimoukha IM, 2017, Fault-Tolerant Controller Design with A Tolerance Measure for Systems with Actuator and Sensor Faults, American Control Conference (ACC), Publisher: IEEE, Pages: 4129-4134, ISSN: 0743-1619
Ahmad MI, Benner P, Jaimoukha I, 2016, Krylov subspace methods for model reduction of quadratic-bilinear systems, IET Control Theory and Applications, Vol: 10, Pages: 2010-2018, ISSN: 1751-8644
The authors propose a two sided moment matching method for model reduction of quadratic-bilinear descriptor systems. The goal is to approximate some of the generalised transfer functions that appear in the input–output representation of the non-linear system. Existing techniques achieve this by utilising moment matching for the first two generalised transfer functions. In this study, they derive an equivalent representation that simplifies the structure of the generalised transfer functions. This allows them to extend the idea of two sided moment matching to higher subsystems which was difficult in the previous approaches. Numerical results are given for some benchmark examples of quadratic-bilinear systems.
Zhang C, Jaimoukha IM, Segundo Sevilla, 2016, Fault-tolerant observer design with a tolerance measure for systems with sensor failure, 2016 American Control Conference, Publisher: IEEE, Pages: 7523-7528, ISSN: 0743-1619
A fault-tolerant switching observer design methodology is proposed. The aim is to maintain a desired level of closed-loop performance under a range of sensor fault scenarios while the fault-free nominal performance is optimized. The range of considered fault scenarios is determined by a minimum number p of assumed working sensors. Thus the smaller p is, the more fault tolerant is the observer. This is then used to define a fault tolerance measure for observer design. Due to the combinatorial nature of the problem, a semidefinite relaxation procedure is proposed to deal with the large number of fault scenarios for systems that have many vulnerable sensors. The procedure results in a significant reduction in the number of constraints needed to solve the problem. Two numerical examples are presented to illustrate the effectiveness of the fault-tolerant observer design.
Liu C, Jaimoukha I, 2015, The Computation of Full-complexity Polytopic Robust Control Invariant Sets, 54th Conference on Decision and Control, Publisher: IEEE, Pages: 6233-6238
This paper considers the problem of evaluating robust control invariant (RCI) sets for linear discrete-time systems subject to state and input constraints as well as additive disturbances. An RCI set has the property that if the system state is inside the set at any one time, then it is guaranteed to remain in the set for all future times using a pre-defined state feedback control law. This problem is important in many control applications. We present a numerically efficient algorithm for the computation of full-complexity polytopic RCI sets. Farkas' Theorem is first used to derive necessary and sufficient conditions for the existence of an admissible polytopic RCI set in the form of nonlinear matrix inequalities. An Elimination Lemma is then used to derive sufficient conditions, in the form of linear matrix inequalities, for the existence of the solution. An optimization algorithm to approximate maximal RCI sets is also proposed. Numerical examples are given to illustrate the effectiveness of the proposed algorithm.
Segundo Sevilla FR, Jaimoukha I, Chaudhuri B, et al., 2015, A semidefinite relaxation procedure for fault-tolerant observer design, IEEE Transactions on Automatic Control, Vol: 60, Pages: 3332-3337, ISSN: 0018-9286
A fault-tolerant observer design methodology is proposed. The aim is to guarantee a minimum level of closed-loop performance under all possible sensor fault combinations while optimizing performance under the nominal, fault-free condition. A novel approach is proposed to tackle the combinatorial nature of the problem, which is computationally intractable even for a moderate number of sensors, by recasting the problem as a robust performance problem, where the uncertainty set is composed of all combinations of a set of binary variables. A procedure based on an elimination lemma and an extension of a semidefinite relaxation procedure for binary variables is then used to derive sufficient conditions (necessary and sufficient in the case of one binary variable) for the solution of the problem which significantly reduces the number of matrix inequalities needed to solve the problem. The procedure is illustrated by considering a fault-tolerant observer switching scheme in which the observer outputs track the actual sensor fault condition. A numerical example from an electric power application is presented to illustrate the effectiveness of the design.
Tahir F, Jaimoukha IM, 2015, Low-complexity polytopic invariant sets for linear systems subject to norm-bounded uncertainty, IEEE Transactions on Automatic Control, Vol: 60, Pages: 1416-1421, ISSN: 0018-9286
We propose a novel algorithm to compute low-complexity polytopic robust control invariant (RCI) sets, along with the corresponding state-feedback gain, for linear discrete-time systems subject to norm-bounded uncertainty, additive disturbances and state/input constraints. Using a slack variable approach, we propose new results to transform the original nonlinear problem into a convex/LMI problem whilst introducing only minor conservatism in the formulation. Through numerical examples, we illustrate that the proposed algorithm can yield improved maximal/minimal volume RCI set approximations in comparison with the schemes given in the literature.
Zhang Z, Jaimoukha IM, 2014, On-line fault detection and isolation for linear discrete-time uncertain systems, Automatica, Vol: 50, Pages: 513-518, ISSN: 0005-1098
This work proposes a robust fault detection and isolation (FDI) scheme for linear discrete-time systems subject to faults, bounded additive disturbances and norm-bounded structured uncertainties. FDI is achieved by computing, on-line, upper and lower bounds on the fault signal such that a fault is regarded as having occurred when its upper bound is smaller than zero or lower bound is larger than zero. Linear Matrix Inequality (LMI) optimization techniques are used to obtain the bounds. Furthermore, a subsequent-state-estimation technique, together with an estimation horizon update procedure, is proposed, which allows the on-line FDI process to be repeated in a moving horizon procedure. Theapproach is also extended to solve the fault detection (FD) problem of obtaining lower bounds on the total fault signal energy within the estimation horizon. The scheme gives the best estimates of the fault signals given the information available and is sufficiently flexible to incorporate other information that may be available, such as bounds on the disturbance energy. Thus our scheme is immune to false alarms if the system and disturbance are within the uncertainty description. Moreover, we propose a new robustness result to obtain the bounds, which is an extension of current techniques for handling model uncertainties.Finally, the approach is verified using two numerical examples.
Segundo Sevilla FR, Jaimoukha I, Chaudhuri B, et al., 2014, Fault-tolerant Control Design to Enhance Damping of Inter-areaOscillations in Power Grids, International Journal in Robust and Nonlinear Control, Accepted
In this paper, passive and active approaches for the design of fault-tolerant controllers (FTCs) are presented. The FTCs are used to improve the damping of inter-area oscillations in a power grid. The effectiveness of using a combination of local and remote (wide area) feedback signals is first demonstrated. The challenge is then to guarantee a minimum level of dynamic performance following a loss of remote signals. The designs are based on regional pole placement using linear matrix inequalities. First, a passive FTC is proposed. It is shown that the computation of the controller reduces to the solution of bilinear matrix inequalities. An iterative procedure is then used to design the controller. Next, as an alternative to active, time-varying controllers,one for each fault scenario, we propose an approach for the design of a ’minimal switching’ FTC in which only one controller is designed, but where a simple switch is incorporated into the controller structure. A case study in a linear and nonlinear Nordic equivalent system is presented to show that the closed-loop response using a conventional control design could deteriorate the performance or even destabilize the system if the remote signals are lost and to demonstrate the effectiveness of the proposed FTC designs.
Tahir F, Jaimoukha IM, Tahir F, et al., 2013, Causal state-feedback parameterizations in robust model predictive control, Automatica, Vol: 49, Pages: 2675-2682
In this paper, we investigate the problem of nonlinearity (and non-convexity) typically associated with linear state-feedback parameterizations in the Robust Model Predictive Control (RMPC) for uncertain systems. In particular, we propose two tractable approaches to compute an RMPC controller–consisting of both a causal, state-feedback gain and a control-perturbation component–for linear, discrete-time systems involving bounded disturbances and norm-bounded structured model-uncertainties along with hard constraints on the input and state. Both the state-feedback gain and the control-perturbation are explicitly considered as decision variables in the online optimization while avoiding nonlinearity and non-convexity in the formulation. The proposed RMPC controller–computed through LMI optimizations–is responsible for steering the uncertain system state to a terminal invariant set. Numerical examples from the literature demonstrate the advantages of the proposed scheme
Kiskiras J, Jaimoukha IM, Halikias GD, et al., 2013, An explicit state-space solution to the one-block super-optimal distance problem, Mathematics of Control, Signals and Systems (MCSS), Vol: 25, Pages: 167-196
An explicit state-space approach is presented for solving the super-optimal Nehari-extension problem. The approach is based on the all-pass dilation technique developed in [JL93] which offers considerable advantages compared to traditional methods relying on a diagonalisation procedure via a Schmidt pair of the Hankel operator associated with the problem. As a result, all derivations presented in this work rely only on simple linear-algebraic arguments. Further, when the simple structure of the one-block problem is taken into account, this approach leads to a detailed and complete state-space analysis which clearly illustrates the structure of the optimal solution and allows for the removal of all technical assumptions (minimality, multiplicity of largest Hankel singular value, positive-definiteness of the solutions of certain Riccati equations) made in previous work [LHG89],[HLG93]. The advantages of the approach are illustrated with a numerical example. Finally, the paper presents a short survey of super-optimization, the various techniques developed for its solution and some of its applications in the area of modern robust control.
Tahir F, Jaimoukha IM, Tahir F, et al., 2013, Robust Feedback Model Predictive Control of Constrained Uncertain Systems, Journal of Process Control, Vol: 23, Pages: 189-200
We propose a novel procedure for the solution to the problem of robust model predictive control (RMPC) of linear discrete time systems involving bounded disturbances and model-uncertainties along with hard constraints on the input and state. The RMPC (outer) controller - responsible for steering the uncertain system state to a designed invariant (terminal) set - has a mixed structure consisting of a state-feedback component as well as a control-perturbation. Both components are explicitly considered as decision variables in the online optimization and the nonlinearities commonly associated with such a state-feedback parameterization are avoided by adopting a sequential approach in the formulation. The RMPC controller minimizes an upper bound on an H2/H_infinity-based cost function. Moreover, the proposed algorithm does not require any offline calculation of (feasible) feedback gains for the computation of the RMPC controller. The optimal Robust Positively invariant set and the inner controller - responsible for keeping the state within the invariant set - are both computed in one step as solutions to an LMI optimization problem. We also provide conditions which guarantee the Lyapunov stability of the closed-loop system. Numerical examples, taken from the literature, demonstrate the advantages of the proposed scheme.
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