Physics-guided neural networks for feedforward control with input-to-state stability guarantees

Currently, there is an increasing interest in merging physics-based methods and artificial intelligence to push performance of feedforward controllers for high-precision mechatronics beyond what is achievable with linear feedforward control. In this paper, we develop a systematic design procedure for feedforward control using physics-guided neural networks (PGNNs) that can handle nonlinear and unknown dynamics. PGNNs effectively merge physics-based and NN-based models, and thereby result in nonlinear feedforward controllers with higher performance and the same reliability as classical, linear feedforward controllers. In particular, conditions are presented to validate (after training) and impose (before training) input-to-state stability (ISS) of PGNN feedforward controllers. The developed PGNN feedforward control framework is validated on a real-life, high-precision industrial linear motor used in lithography machines, where it reaches a factor 2 improvement with respect to conventional mass-friction feedforward

Synthesis of distributed robust H-Infinity controllers for interconnected discrete time systems

This paper presents an algorithm for the synthesis of robust distributed controllers for interconnected linear discrete-time systems. For a network of interconnected uncertain linear time-invariant systems, the distributed controller achieves robust stability and a guaranteed level of robust performance in a well-defined H∞ sense. The setting of this paper is in discrete time. Based on the theory of dissipative dynamical systems, conditions for the analysis of robust stability and robust performance of networks are derived in terms of feasibility tests of linear matrix inequalities. From these conditions, computationally tractable synthesis conditions are derived. An iterative D-K type of synthesis algorithm is proposed that yields a robust distributed controller. Convergence properties of the algorithm are inferred

Switched LQG control for linear systems with multiple sensing methods

In many control contexts, such as vision-based control, data-processing methods are needed to distill information from measurement data (such as images). These data-processing methods introduce several undesirable effects such as delays, measurement inaccuracies and possible absence of information, which limit closed-loop performance. Typically, a single processing method with an appropriate compromise between these effects is chosen in practice. Instead of settling for a compromise using only one fixed processing method, we propose to break the design trade-off by switching on-line between several data-processing methods having different delay, accuracy, and data-loss characteristics. We provide a modeling framework for sensing and data-processing methods that is suitable for control applications and incorporates the characteristics of the undesirable effects mentioned above. Using the models provided by the framework, we provide explicit policies for switching on-line between sensing methods with different characteristics based on a modified rollout strategy. Our approach formally guarantees that an LQG-type infinite horizon performance is better than, or at least not worse than, non-switching approaches. The advantages of the proposed methodology are further highlighted via a numerical example

On synthesis of stabilizing distributed controllers with an application to power systems

This paper presents synthesis results for distributed controllers for interconnected linear time-invariant systems. The setting of the paper is in discrete time. A parameterization of the closed-loop system is used for interconnected systems and distributed static state feedback controllers with an interconnection structure that can be chosen arbitrary in the design phase. Based on conditions for closed-loop stability using centralized static state feedback, computationally tractable synthesis procedures are derived that yield a distributed controller. The synthesis procedures involve convex optimization problems in the form of linear matrix inequalities (LMIs) which are solved in a centralized way. Suggestions are made to reduce the number of independent variables in the problems. An algorithm is presented that uses these synthesis procedures to find a controller with a distributed structure or eliminates candidate controller structures using heuristics. A complexity analysis for the synthesis procedures is incorporated. The synthesis algorithm is illustrated on examples of electric power systems, proving feasibility of the synthesis procedures for real-life applications

Event- and deadline-driven control of a self-localizing robot with vision-induced delays

Control based on vision data is a growing field of research and it is widespread in industry. The amount of data in each image and the processing needed to obtain control-relevant information from this data lead to significant delays with a large variability in the control loops. This often causes performance deterioration since in many cases the delay variability is not explicitly addressed in the control design. In this paper, we approach this problem by applying the ideas of recently developed model-based control design methods, which are tailored to address stochastic delays directly, to the motion control of an omnidirectional robot with a vision-based self-localization algorithm. The completion time or delay of the Random Sample Consensus (RANSAC) based localization algorithm is identified as a stochastic random variable with significant variability, illustrating the practical difficulties with data processing. Our main aim is to show that the novel deadline-driven and event-driven control designs significantly outperform a traditional periodic control implementation for a stochastic optimal control performance index

Switching data-processing methods in a control loop : trade-off between delay and probability of data acquisition

Many control applications, such as vision-based control, require data-processing methods to distill sensor information. This data-processing introduces several undesired effects in the control loop, such as delays, the probability of not acquiring information, and measurement inaccuracies. Often, these effects obey a trade-off. For example, the probability of acquiring control-relevant information, related to the probability of data-loss, is typically higher if a larger delay is allowed. While a single processing method with a reasonable trade-off is typically selected, we propose instead a solution to switch between data-processing methods with different delays and corresponding data-loss probabilities. We prove that the proposed method achieves a better LQG-type performance when compared to the individual methods. A simulation considering a second-order system illustrates the advantages of the proposed method

Switched LQG control for linear systems with multiple sensing methods

\u3cp\u3eIn many control contexts, such as vision-based control, data-processing methods are needed to distill information from measurement data (such as images). These data-processing methods introduce several undesirable effects such as delays, measurement inaccuracies and possible absence of information, which limit closed-loop performance. Typically, a single processing method with an appropriate compromise between these effects is chosen in practice. Instead of settling for a compromise using only one fixed processing method, we propose to break the design trade-off by switching on-line between several data-processing methods having different delay, accuracy, and data-loss characteristics. We provide a modeling framework for sensing and data-processing methods that is suitable for control applications and incorporates the characteristics of the undesirable effects mentioned above. Using the models provided by the framework, we provide explicit policies for switching on-line between sensing methods with different characteristics based on a modified rollout strategy. Our approach formally guarantees that an LQG-type infinite horizon performance is better than, or at least not worse than, non-switching approaches. The advantages of the proposed methodology are further highlighted via a numerical example.\u3c/p\u3

Event- and deadline-driven control of a self-localizing robot with vision-induced delays

\u3cp\u3eControl based on vision data is a growing field of research and it is widespread in industry. The amount of data in each image and the processing needed to obtain control-relevant information from this data lead to significant delays with a large variability in the control loops. This often causes performance deterioration since in many cases the delay variability is not explicitly addressed in the control design. In this paper, we approach this problem by applying the ideas of recently developed model-based control design methods, which are tailored to address stochastic delays directly, to the motion control of an omnidirectional robot with a vision-based self-localization algorithm. The completion time or delay of the Random Sample Consensus (RANSAC) based localization algorithm is identified as a stochastic random variable with significant variability, illustrating the practical difficulties with data processing. Our main aim is to show that the novel deadline-driven and event-driven control designs significantly outperform a traditional periodic control implementation for a stochastic optimal control performance index.\u3c/p\u3

Self-triggered and event-driven control for linear systems with stochastic delays

\u3cp\u3eDelays are often present in embedded and networked control loops and represent one of the main sources of performance limitations. In this paper, we propose two aperiodic control strategies to optimize closed-loop performance in the presence of stochastic delays: (i) a self-triggered strategy, in which the deadline to drop data is decided on-line based on the current state; (ii) an event-driven strategy, whereby the control input is updated immediately after the delayed data becomes available, leading in general to faster but time-varying control loops. These schemes are designed and analyzed using a standard LQG framework, which allows for assessing and comparing closed-loop performance. We establish that our self-triggered strategy always achieves a better closed-loop performance than periodic control with an optimal sampling period. Moreover, we provide examples where the event-driven strategy outperforms the self-triggered strategy and examples where the opposite is observed.\u3c/p\u3