In face of an increasing number of automotive cyber-physical threat scenarios, the issue of adversarial destabilization of the lateral motion of target vehicles through direct attacks on their steering systems has been extensively studied. A more subtle question is whether a cyberattacker can destabilize the target vehicle lateral motion through improper engagement of the vehicle brakes and/or anti-lock braking systems (ABS). Motivated by such a question, this paper investigates the impact of cyber-physical attacks that exploit the braking/ABS systems to adversely affect the lateral motion stability of the targeted vehicles. Using a hybrid physical/dynamic tire-road friction model, it is shown that if a braking system/ABS attacker manages to continuously vary the longitudinal slips of the wheels, they can violate the necessary conditions for asymptotic stability of the underlying linear time-varying (LTV) dynamics of the lateral motion. Furthermore, the minimal perturbations of the wheel longitudinal slips that result in lateral motion instability under fixed slip values are derived. Finally, a real-time algorithm for monitoring the lateral motion dynamics of vehicles against braking/ABS cyber-physical attacks is devised. This algorithm, which can be efficiently computed using the modest computational resources of automotive embedded processors, can be utilized along with other intrusion detection techniques to infer whether a vehicle braking system/ABS is experiencing a cyber-physical attack. Numerical simulations in the presence of realistic CAN bus delays, destabilizing slip value perturbations obtained from solving quadratic programs on an embedded ARM Cortex-M3 emulator, and side-wind gusts demonstrate the effectiveness of the proposed methodology.

A flatness-based control approach which is implemented in successive loops is used to solve the control problem for the multivariable and nonlinear dynamics of industrial robotic manipulators and autonomous vehicles. The state-space model of these robotic systems is separated into two subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input-output linearized flat systems. The state variables of the second subsystem become virtual control inputs for the first subsystem. In turn exogenous control inputs are applied to the first subsystem. The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of a 3-DOF industrial rigidlink robotic manipulator, (ii) control of a 3-DOF autonomous underwater vessel.

A nonlinear optimal (H-infinity) control method is developed for the Lotka-Volterra dynamical system. First, differential flatness properties are proven. The state-space description undergoes linearization, at each sampling instance, with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. Next, for the approximately linearized model of the system a stabilizing H-infinity feedback controller is designed. To compute the controller’s gains an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm. Global stability properties are proven through Lyapunov analysis. Finally, the nonlinear optimal control method is compared against a flatness-based control approach implemented in successive loops.

Voltage-source inverter-fed Permanent Magnet Synchronous Machines are widely used in industry (for instance for the actuation of robotic and mechatronic systems, of cranes, in water pumping stations) as well as in transportation systems (for the traction of trains and electric vehicles). The present article proposes a nonlinear optimal control approach for voltage source inverter-fed Permanent Magnet Synchronous Machines (VSI-PMSMs). The nonlinear dynamic model of VSI-PMSMs undergoes approximate linearization around a temporary operating point which is recomputed at each iteration of the control method. This temporary operating point is defined by the present value of the voltage source inverter-fed PMSM state vector and by the last sampled value of the machine’s control inputs vector. The linearization relies on Taylor series expansion and on the calculation of the system’s Jacobian matrices. For the approximately linearized model of the voltage source inverter-fed PMSM an H-infinity feedback controller is designed. This controller stands for the solution of the nonlinear optimal control problem for the voltage source inverter-fed PMSM under model uncertainty and external perturbations. For the computation of the controller’s feedback gain an algebraic Riccati equation is iteratively solved at each time-step the control method. The global asymptotic stability properties of the control method are proven through Lyapunov analysis.

Fault diagnosis in energy conversion systems is performed with the use of neural networks and statistical decision making. An energy conversion system comprising a solar power unit, a DC-DC converter and a DC motor is considered and the related condition monitoring problem is solved. A neural network is used to model the dynamics of this energy conversion system after processing its input and output measurements, being accumulated at different operating conditions. The considered neural model is trained with the use of first-order gradient algorithms and consists of a hidden layer of Gauss-Hermite polynomial activation functions and of an output layer with linear weights. The neural network and the resulting model represents the fault-free functioning of the energy conversion system. At a next stage, the measurements of the real output of the energy conversion system are compared against the estimated outputs which are provided by the neural model. This provides, the residuals sequence. It holds that the sum of the squares of the residuals' vectors, multiplied with the inverse of the associated covariance matrix, stands for a stochastic variable (statistical test) which follows the χ2distribution. One can have a precise and almost infallible decision making tool about the appearance of faults in the energy conversion system, by selecting the 96% or the 98% confidence intervals of this distribution. When the upper or lower bound of the confidence interval are persistently exceeded one can conclude that the system has been subject to a fault. Finally, fault isolation can be also accomplished, by applying the statistical test into subspaces of the energy conversion system's state-space model.

Cyber-physical systems (CPSs) security has become a critical research topic as more and more CPS applications are making increasing impacts in diverse industrial sectors. Due to the tight interaction between cyber and physical components, CPS security requires a different strategy from the traditional information technology (IT) security. In this paper, we propose a machine learning-based attack detection (AD) scheme, as part of our overall CPS security strategies. The proposed scheme performs AD at the physical layer by modeling and monitoring physics or physical behavior of the physical asset or process. In developing the proposed AD scheme, we devote our efforts on intelligently deriving salient signatures or features out of the large number of noisy physical measurements by leveraging physical knowledge and using advanced machine learning techniques. Such derived features not only capture the physical relationships among the measurements but also have more discriminant power in distinguishing normal and attack activities. In our experimental study for demonstrating the effectiveness of the proposed AD scheme, we consider heavy-duty gas turbines of combined cycle power plants as the CPS application. Using the data from both the high-fidelity simulation and several real plants, we demonstrate that our proposed AD scheme is effective in early detection of attacks or malicious activities.

1A nonlinear H-infinity (optimal) control approach is developed for the problem of the control of the spherical rolling robot. The solution of such a control problem is a nontrivial case due to underactuation and strong nonlinearities in the system's state-space description. The dynamic model of the robot undergoes approximate linearization around a temporary operating point which is recomputed at each timestep of the control method. The linearization relies on Taylor series expansion and on the computation of the system's Jacobian matrices. For the linearized dynamics of the spherical robot an H-infinity controller is designed. To compute the controller's feedback gains an algebraic Riccati equation in solved at each iteration of the control algorithm. The global asymptotic stability properties of the control method are proven through Lyapunov analysis. Finally, for the implementation of sensorless control for the spherical rolling robot, the H-infinity Kalman Filter is used as a robust state estimator.

A nonlinear optimal control approach is developed for the UAV and suspended load system. The dynamic model of the UAV and payload system undergoes approximate linearization. This makes use of Taylor series expansion around a temporary operating point which recomputed at each iteration of the control method. The linearization procedure relies on the computation of the Jacobian matrices of the state-space model of the system. Next, an H-infinity feedback controller is designed for the approximately linearized model. The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the UAV and payload system, under model uncertainties and external perturbations. To compute the controller's feedback gains an algebraic Riccati equation is solved at each time-step of the control algorithm. The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the UAV and payload system, under moderate variations of the control inputs. Finally, Lyapunov analysis is used to prove the global stability properties of the control scheme.

Control of the milling process of mining products (ore milling) is a non-trivial problem due to being related with a strongly nonlinear and multivariable state-space model. To provide an efficient solution to this problem, in this article a nonlinear optimal (H-infinity) control method is developed. In the considered nonlinear optimal control method, the dynamic model of the mining products' mill undergoes first approximate linearization with the use of Taylor series expansion and with the computation of the associated Jacobian matrices. The linearization point (temporary equilibrium) is recomputed at each time step of the control method and comprises the present value of the system's state vector and the last value of the control inputs' vector that was exerted on it. For the linearized description of the mill's functioning the optimal control problem is solved by applying an H-infinity controller. The feedback gain is computed again at each iteration of the control algorithm through the solution of an algebraic Riccati equation. The stability of the control scheme is confirmed through Lyapunov analysis. First, it is shown that the control method satisfies the H-infinity tracking performance, and this signifies elevated robustness against model uncertainty and external perturbations. Next, under moderate conditions, it is proven that the control loop is globally asymptotically stable.

The article proposes a method for diagnosing faults and cyberattacks in electric power generation units that consist of a wind-turbine and of an asynchronous (DFIG) generator. The method relies on a differential flatness theory-based implementation of the nonlinear Kalman Filter, known as Derivative-free nonlinear Kalman Filter. The estimated outputs provided by the Kalman filter are subtracted from the real outputs measured from the power unit, thus generating the residuals sequence. It is proven that the sum of the squares of the residuals vectors, weighted by the inverse of the residuals covariance matrix, stands for a stochastic variable that follows the χ2distribution. By exploiting the statistical properties of the χ2distribution one can define confidence intervals which allow for deciding at a high certainty level about the appearance of a fault or cyberattack in the wind-power system.