Based on the available documents, the author has analysed the connections between converts to Islam and the Christian part of their families, and the way in which the conversions infl uenced the lives of converts and their Christian relatives. Adopting another religion did not mean abandoning the old habits and Christian traditions. Regardless of the change of religion and the new religious practices they had to follow, there were those who continued living in accordance with the Christian tradition. Th e converts’ contacts with their Christian families contributed to that considerably. Th e diff erence in name and religion did not break family ties even when families were separated.

In preference learning, it is beneficial to incorporate monotonicity constraints for learning utility functions when there is prior knowledge of monotonicity. We present a novel method for learning utility functions with monotonicity constraints using Gaussian process regression. Data is provided in the form of pairwise comparisons between items. Using conditions on monotonicity for the predictive function, an algorithm is proposed which uses the weighted average between prior linear and maximum a posteriori (MAP) utility estimates. This algorithm is formally shown to guarantee monotonicity of the learned utility function in the dimensions desired. The algorithm is tested in a Monte Carlo simulation case study, in which the results suggest that the learned utility by the proposed algorithm performs better in prediction than the standalone linear estimate, and enforces monotonicity unlike the MAP estimate.

We study the design of state observers for nonlinear networked control systems (NCSs) that are implemented over WirelessHART (WH). WH is a wireless communication protocol for process automation applications. It is characterised by its multi-hop structure, slotted communication cycles, and simultaneous transmission over different frequencies. We present a solution based on the emulation approach. That is, given an observer designed with a specific stability property in the absence of communication constraints, we implement it over a WH network and we provide sufficient conditions on the latter, to preserve the stability property of the observer. In particular, we provide explicit bounds on the maximum allowable transmission interval. We assume that the plant dynamics and measurements are affected by noise and we guarantee an input-to-state stability property for the corresponding estimation error system.

Many natural and engineered systems exhibit a singularly perturbed structure where different time scales inherently lead to difficulties in the design of observers for the system. In our related work [1], we have shown that, under appropriate assumptions, an observer designed for the slow part of the system can be applied and results in semi-global practical asymptotical (SPA) stability of the estimation error. In this paper, we show that assumptions from [1] hold for two classes of plants and nonlinear observers. In fact, we show that the provided framework in [1] covers current results in the literature and also other cases that are not covered by existing results. Hence, we demonstrate that we generalise existing results in the literature.

This paper presents an analytical framework to design and analyze hybrid extremum seeking controllers for plants with hybrid dynamics. The extremum seeking controllers are characterized by a hybrid dither generator, a hybrid Jacobian estimator, and a hybrid dynamic optimizer. This structure allows us to consider a family of novel extremum seeking controllers that have not been studied in the literature before. Moreover, the hybrid extremum seeking controllers can be applied to plants with hybrid dynamics generating well-defined response maps. A convergence result is established for the closed -loop system by using singular perturbation theory for hybrid dynamical systems with hybrid boundary layers.

We consider supervisory control of nonlinear systems which are implemented on digital networks. In particular, two candidate controllers are orchestrated by a supervisor to stabilize the origin of the plant by following a dwell time logic, i.e. evaluating a control-mode switching rule at instants which are at least spaced by some dwell time interval. The plant, the controllers and the supervisor communicate via a network and the transmissions are triggered by a mechanism at the discrete sampling instants, which leads to periodic event-triggered control. Thus, there are possibly two kinds of events generated at the sampling instants: the control-mode switching event to activate another control law and the transmission event to update the control input. We propose a systematic design procedure for periodic event-triggered supervisory control for nonlinear systems. We start from a supervisory control scheme which robustly stabilizes the system in the absence of the network. We then implement it over the network and design event-triggering rules to preserve its stability properties. In particular, for each candidate controller, we provide a lower bound for the control-mode dwell time, design criterion to generate transmission events and present an explicit bound on the maximum sampling period with which the triggering rules are evaluated, to ensure stability of the whole system. We show that there exist relationships among the control-mode dwell time, a parameter used to define the transmission event-triggering condition and the bound of the sampling period. An example is given to illustrate the results.

A popular design framework for networked control systems (NCSs) is the emulation-based approach combined with hybrid dynamical systems analysis techniques. In the rich literature regarding this framework, various bounds on the maximal allowable transmission interval (MATI) are provided to guarantee stability properties of the NCS using Lyapunov-based arguments for hybrid systems. In this work, we provide a generalization of these Lyapunov-based proofs, showing how the existing results for the MATI can be improved by only considering a different, more general hybrid Lyapunov function, while not altering the conditions in the analysis itself.

Estimation of physical variables of nonlinear systems with two-time scales is a hard task to address. Whilst nonlinear systems exhibiting a singularly perturbed structure are common in engineering applications, current observer design results apply only to a specific class of plants and observers. We consider a broader class of plants and observers to generalise existing results on observer design for slow states of nonlinear singularly perturbed systems. Under reasonable assumptions, it is shown that the estimation error can be made semi-globally practically asymptotically stable in the singular perturbation parameter. This subsequently leads to appropriate conditions for the observer design for slow variables that guarantee satisfactory estimation error performance in the full system.

A secure nonlinear networked control system (NCS) design using semi-homomorphic encryption, namely, Paillier encryption is studied. Under certain assumptions, control signal computation using encrypted signal directly is allowed by semi-homomorphic encryption. Thus, the security of the NCSs is further enhanced by concealing information on the controller side. However, additional technical difficulties in the design and analysis of NCSs are induced compared to standard NCSs. In this paper, the stabilization of a nonlinear discrete time NCS is considered. More specifically, sufficient conditions on the encryption parameters that guarantee stability of the NCS are provided, and a trade-off between the encryption parameters and the ultimate bound of the state is shown.

We investigate the stabilization of perturbed nonlinear systems using output-based periodic event-triggered controllers. Thus, the communication between the plant and the controller is triggered by a mechanism, which evaluates an output- and input-dependent rule at given sampling instants. We address the problem by emulation. Hence, we assume the knowledge of a continuous-time output feedback controller, which robustly stabilizes the system in the absence of network. We then implement the controller over the network and model the overall system as a hybrid system. We design the event-triggered mechanism to ensure an input-to-state stability property. An explicit bound on the maximum allowable sampling period at which the triggering rule is evaluated is provided. The analysis relies on the construction of a novel hybrid Lyapunov function. The results are applied to a class of Lipschitz nonlinear systems, for which we formulate the required conditions as linear matrix inequalities. The effectiveness of the scheme is illustrated via simulations of a nonlinear example.

Discounted costs are considered in many fields, like reinforcement learning, for which various algorithms can be used to obtain optimal inputs for finite horizons. The related literature mostly concentrates on optimality and largely ignores stability. In this context, we study stability of general nonlinear discrete- time systems controlled by an optimal sequence of inputs that minimizes a finite-horizon discounted cost computed in a receding horizon fashion. Assumptions are made related to the stabilizability of the system and its detectability with respect to the stage cost. Then, a Lyapunov function for the closed-loop system with the receding horizon controller is constructed and a uniform semiglobal stability property is ensured, where the adjustable parameters are both the discount factor and the horizon length. Uniform global exponential stability is guaranteed by strengthening the initial assumptions, in which case explicit bounds on the discount factor and the horizon length are provided. We compare the obtained bounds in the particular cases where there is no discount or the horizon is infinite, respectively, with related results in the literature and we show our bounds improve existing ones on the examples considered.

Dynamic adaptive streaming over HTTP (DASH) is widely adopted for video transport by major content providers. However, the inherent high variability in both encoded video and network rates represents a key challenge for designing efficient adaptation algorithms. Accommodating such variability in the adaptation logic design is essential for achieving a high user quality of Experience (QoE). In this paper, we present ARBITER+ as a novel adaptation algorithm for DASH. ARBITER+ integrates different components that are designed to ensure a high video QoE while accommodating inherent system variabilities. These components include a tunable adaptive target rate estimator, hybrid throughput sampling, controlled switching, and short-term actual video rate tracking. We extensively evaluate the streaming performance using real video and cellular network traces. We show that ARBITER+ components work in harmony to balance temporal and visual QoE aspects. Additionally, we show that ARBITER+ enjoys a noticeable QoE margin in comparison to state-of-the-art adaptation approaches in various operating conditions. Furthermore, we show that ARBITER+ also achieves the best application-level fairness when a group of mobile video clients shares a cellular base station.

Identification of a complete dynamic network affected by sensor noise using the prediction error method is often too complex. One of the reasons for this complexity is the requirement to minimize a non-convex cost function, which becomes more difficult with more complex networks. In this paper, we consider serial cascade networks affected by sensor noise. Recently, the Weighted Null-Space Fitting method has been shown to be appropriate for this setting, providing asymptotically efficient estimates without suffering from non-convexity; however, applicability of the method was subject to some conditions on the locations of sensors and excitation signals. In this paper, we drop such conditions, proposing an extension of the method that is applicable to general serial cascade networks. We formulate an algorithm that describes application of the method in a general setting, and perform a simulation study to illustrate the performance of the method, which suggests that this extension is still asymptotically efficient.