We propose a new method for controlled system synthesis on non-deterministic automata, which includes the synthesis for deadlock-freeness, as well as invariant and reachability expressions. Our technique restricts the behavior of a Kripke-structure with labeled transitions, representing the uncontrolled system, such that it adheres to a given requirement specification in an expressive modal logic. while all non-invalidating behavior is retained. This induces maximal permissiveness in the context of supervisory control. Research presented in this paper allows a system model to be constrained according to a broad set of liveness, safety and fairness specifications of desired behavior, and embraces most concepts from Ramadge-Wonham supervisory control, including controllability and marker-state reachability. Synthesis is defined in this paper as a formal construction, which allowed a careful validation of its correctness using the Coq proof assistant.
Research by D'Ippolito and others @cite_11 , @cite_18 is based upon the framework of the world machine model for the synthesis of liveness properties, stated in fluent temporal logic. A distinction is made between controlled and monitored behavior, and between system goals and environment assumptions @cite_11 . A controller is then derived from a winning strategy in a two-player game between original and required behavior, as expressed in terms of the notion of generalized reactivity, as introduced in @cite_11 . Research in @cite_11 also emphasizes the fact that pruning-based synthesis is not adequate for control of non-deterministic models, and it defines synthesis of liveness goals under a maximality criterion, referred to as best-effort controller. However, this maximality requirement is trace-based and is therefore not able to signify inclusion of all possible infinite behaviors. In addition, some results in @cite_11 are based upon the assumption of a deterministic plant specification.
@cite_18 @cite_11
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We present a novel technique for synthesising behaviour models that works for an expressive subset of liveness properties and conforms to the foundational requirements engineering World Machine model, dealing explicitly with assumptions on environment behaviour and distinguishing controlled and monitored actions. This is the first technique that conforms to what is considered best practice in requirements specifications: distinguishing prescriptive and descriptive assertions. Most previous attempts at using synthesis of behavioural models were restricted to handling only safety properties. Those that did support liveness were inadequate for synthesis of operational event based models as they did not include the bespoke distinction between system goals and environment assumptions. We present SGR(1), a novel synthesis technique and methodological guidelines for automatically constructing event-based behavior models. Our approach works for an expressive subset of liveness properties, distinguishes between controlled and monitored actions, and differentiates system goals from environment assumptions. We show that assumptions must be modeled carefully in order to avoid synthesizing anomalous behavior models. We characterize nonanomalous models and propose assumption compatibility, a sufficient condition, as a methodological guideline. Controller synthesis for hybrid systems that satisfy temporal specifications expressing various system properties is a challenging problem that has drawn the attention of many researchers. However, making the assumption that such temporal properties are deterministic is far from the reality. For example, many of the properties the controller has to satisfy are learned through machine learning techniques based on sensor input data. In this paper, we propose a new logic, Probabilistic Signal Temporal Logic (PrSTL), as an expressive language to define the stochastic properties, and enforce probabilistic guarantees on them. We further show how to synthesize safe controllers using this logic for cyber-physical systems under the assumption that the stochastic properties are based on a set of Gaussian random variables. One of the key distinguishing features of PrSTL is that the encoded logic is adaptive and changes as the system encounters additional data and updates its beliefs about the latent random variables that define the safety properties. We demonstrate our approach by synthesizing safe controllers under the PrSTL specifications for multiple case studies including control of quadrotors and autonomous vehicles in dynamic environments. Currently, the field of sensory-motor neuroscience lacks a computational model that can replicate real-time control of biological brain. Due to incomplete neural and anatomical data, traditional neural network training methods fail to model the sensory-motor systems. Here we introduce a novel modeling method based on stochastic optimal control framework which is well suited for this purpose. Our controller is implemented with a recurrent neural network (RNN) whose goal is approximating the optimal global control law for the given plant and cost function. We employ a risk-sensitive objective function proposed by Jacobson (1973) for robustness of controller. For maximum optimization efficiency, we introduce a step response sampling method, which minimizes complexity of the optimization problem. We use conjugate gradient descent method for optimization, and gradient is calculated via Pontryagins maximum principle. In the end, we obtain highly stable and robust RNN controllers that can generate infinite varieties of attractor dynamics of the plant, which are proposed as building blocks of movement generation. We show two such examples, a point attractor based and a limit-cycle based dynamics.