Incremental Dissipativity based Control of Discrete-Time Nonlinear Systems via the LPV Framework

Unlike for Linear Time-Invariant (LTI) systems, for nonlinear systems, there exists no general framework for systematic convex controller design which incorporates performance shaping. The Linear Parameter-Varying (LPV) framework sought to bridge this gap by extending convex LTI synthesis results such that they could be applied to nonlinear systems. However, recent literature has shown that naive application of the LPV framework can fail to guarantee the desired asymptotic stability guarantees for nonlinear systems. Incremental dissipativity theory has been successfully used in the literature to overcome these issues for Continuous-Time (CT) systems. However, so far no solution has been proposed for output-feedback based incremental control for the Discrete-Time (DT) case. Using recent results on convex analysis of incremental dissipativity for DT nonlinear systems, in this paper, we propose a convex output-feedback controller synthesis method to ensure closed-loop incremental dissipativity of DT nonlinear systems via the LPV framework. The proposed method is applied on a simulation example, demonstrating improved stability and performance properties compared to a standard LPV controller design.Comment: Accepted to 60th Conference on Decision and Control, Austin, 202

Stability analysis and control of discrete-time systems with delay

The research presented in this thesis considers the stability analysis and control of discrete-time systems with delay. The interest in this class of systems has been motivated traditionally by sampled-data systems in which a process is sampled periodically and then controlled via a computer. This setting leads to relatively cheap control solutions, but requires the discretization of signals which typically introduces time delays. Therefore, controller design for sampled-data systems is often based on a model consisting of a discrete-time system with delay. More recently the interest in discrete-time systems with delay has been motivated by networked control systems in which the connection between the process and the controller is made through a shared communication network. This communication network increases the flexibility of the control architecture but also introduces effects such as packet dropouts, uncertain time-varying delays and timing jitter. To take those effects into account, typically a discrete-time system with delay is formulated that represents the process together with the communication network, this model is then used for controller design While most researchers that work on sampled-data and networked control systems make use of discrete-time systems with delay as a modeling class, they merely use these models as a tool to analyse the properties of their original control problem. Unfortunately, a relatively small amount of research on discrete-time systems with delay addresses fundamental questions such as: What trade-off between computational complexity and conceptual generality or potential control performance is provided by the different stability analysis methods that underlie existing results? Are there other stability analysis methods possible that provide a better trade-off between these properties? In this thesis we try to address these and other related questions. Motivated by the fact that almost every system in practice is subject to constraints and Lyapunov theory is one of the few methods that can be easily adapted to deal with constraints, all results in this thesis are based on Lyapunov theory. In Chapter 2 we introduce delay difference inclusions (DDIs) as a modeling class for systems with delay and discuss their generality and advantages. Furthermore, the two standard stability analysis results for DDIs that make use of Lyapunov theory, i.e., the Krasovskii and Razumikhin approaches, are considered. The Krasovskii approach provides necessary and sufficient conditions for stability while the Razumikhin approach provides conditions that are relatively simple to verify but conservative. An important conclusion is that the Razumikhin approach makes use of conditions that involve the system state only while those corresponding to the Krasovskii approach involve trajectory segments. Therefore, only the Razumikhin approach yields information about DDI trajectories directly, such that the corresponding computations can be executed in the low-dimensional state space of the DDI dynamics. Hence, we focus on the Razumikhin approach in the remainder of the thesis. In Chapter 3 it is shown that by considering each delayed state as a subsystem, the behavior of a DDI can be described by an interconnected system. Thus, the Razumikhin approach is found to be an exact application of the small-gain theorem, which provides an explanation for the conservatism that is typically associated with this approach. Then, inspired by the relation of DDIs to interconnected systems, we propose a new Razumikhin-type stability analysis method that makes use of a stability analysis result for interconnected systems with dissipative subsystems. The proposed method is shown to provide a trade-off between the conceptual generality of the Krasovskii approach and the computationally convenience of the Razumikhin approach. Unfortunately, these novel Razumikhin-type stability analysis conditions still remain conservative. Therefore, in Chapter 4 we propose a relaxation of the Razumikhin approach that provides necessary and sufficient conditions for stability. Thus, we obtain a Razumikhin-type result that makes use of conditions that involve the system state only and are non-conservative. Interestingly, we prove that for positive linear systems these conditions equivalent to the standard Razumikhin approach and hence both are necessary and sufficient for stability. This establishes the dominance of the standard Razumikhin approach over the Krasovskii approach for positive linear discrete-time systems with delay. Next, in Chapter 5 the stability analysis of constrained DDIs is considered. To this end, we study the construction of invariant sets. In this context the Krasovskii approach leads to algorithms that are not computationally tractable while the Razumikhin approach is, due to its conservatism, not always able to provide a suitable invariant set. Based on the non-conservative Razumikhin-type conditions that were proposed in Chapter 4, a novel invariance notion is proposed. This notion, called the invariant family of sets, preserves the conceptual generality of the Krasovskii approach while, at the same time, it has a computational complexity comparable to the Razumikhin approach. The properties of invariant families of sets are analyzed and synthesis methods are presented. Then, in Chapter 6 the stabilization of constrained linear DDIs is considered. In particular, we propose two advanced control schemes that make use of online optimization. The first scheme is designed specifically to handle constraints in a non-conservative way and is based on the Razumikhin approach. The second control scheme reduces the computational complexity that is typically associated with the stabilization of constrained DDIs and is based on a set of necessary and sufficient Razumikhin-type conditions for stability. In Chapter 7 interconnected systems with delay are considered. In particular, the standard stability analysis results based on the Krasovskii as well as the Razumikhin approach are extended to interconnected systems with delay using small-gain arguments. This leads, among others, to the insight that delays on the channels that connect the various subsystems can not cause the instability of the overall interconnected system with delay if a small-gain condition holds. This result stands in sharp contrast with the typical destabilizing effect that time delays have. The aforementioned results are used to analyse the stability of a classical power systems example where the power plants are controlled only locally via a communication network, which gives rise to local delays in the power plants. A reflection on the work that has been presented in this thesis and a set of conclusions and recommendations for future work are presented in Chapter 8

Energy Shaping Control for Stabilization of Interconnected Voltage Source Converters in Weakly-Connected AC Microgrid Systems

With the ubiquitous installations of renewable energy resources such as solar and wind, for decentralized power applications across the United States, microgrids are being viewed as an avenue for achieving this goal. Various independent system operators and regional transmission operators such as Southwest Power Pool (SPP), Midcontinent System Operator (MISO), PJM Interconnection and Electric Reliability Council of Texas (ERCOT) manage the transmission and generation systems that host the distributed energy resources (DERs). Voltage source converters typically interconnect the DERs to the utility system and used in High voltage dc (HVDC) systems for transmitting power throughout the United States. A microgrid configuration is built at the 13.8kV 4.75MVA National Center for Reliable Energy Transmission (NCREPT) testing facility for performing grid-connected and islanded operation of interconnected voltage source converters. The interconnected voltage source converters consist of a variable voltage variable frequency (VVVF) drive, which powers a regenerative (REGEN) load bench acting as a distributed energy resource emulator. Due to the weak-grid interface in islanded mode testing, a voltage instability occurs on the VVVF dc link voltage causing the system to collapse. This dissertation presents a new stability theorem for stabilizing interconnected voltage source converters in microgrid systems with weak-grid interfaces. The new stability theorem is derived using the concepts of Dirac composition in Port-Hamiltonian systems, passivity in physical systems, eigenvalue analysis and robust analysis based on the edge theorem for parametric uncertainty. The novel stability theorem aims to prove that all members of the classes of voltage source converter-based microgrid systems can be stabilized using an energy-shaping control methodology. The proposed theorems and stability analysis justifies the development of the Modified Interconnection and Damping Assignment Passivity-Based Control (Modified IDA-PBC) method to be utilized in stabilizing the microgrid configuration at NCREPT for mitigating system instabilities. The system is simulated in MATLAB/SimulinkTM using the Simpower toolbox to observe the system’s performance of the designed controller in comparison to the decoupled proportional intergral controller. The simulation results verify that the Modified-IDA-PBC is a viable option for dc bus voltage control of interconnected voltage source converters in microgrid systems

A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version

Discrete-time Contraction Analysis and Controller Design for Nonlinear Processes

Shifting away from the traditional mass production approach, the process industry is moving towards more agile, cost-effective and dynamic process operation (next-generation smart plants). This warrants the development of control systems for nonlinear chemical processes to be capable of tracking time-varying setpoints to produce products with different specifications as per market demand and deal with variations in the raw materials and utility (e.g., energy). This thesis aims to develop controllers to achieve time-varying setpoints tracking using contraction theory. Through the differential dynamic system framework, the contraction conditions for discrete-time systems, which ensure the exponential convergence between system responses and feasible time-varying references, are derived. The discrete-time differential dissipativity condition is further developed, which can be used for disturbance rejection control designs. Computationally tractable equivalent conditions are then derived and additionally transformed into an Sum of Squares programming problem, such that a discrete-time control contraction metric and stabilising feedback controller can be jointly obtained. Synthesis and implementation details of the resulting contraction-based controller are provided, which can achieve offset-free tracking of feasible time-varying references. To do contraction analysis and control design for systems with uncertainties, which are often complex and difficult, neural networks are used. It involves training and constructing a neural network embedded contraction-based controller. Learning algorithms of uncertain system model parameters are developed. The resulting control scheme is capable of achieving efficient offset-free tracking of time-varying references, with a full range of model uncertainties, without the need for controller structure redesign as the reference or uncertain parameter changes. This neural network based approach also ensures process stability during online simultaneous control and learning of uncertain parameters. To further improve the economics of contraction-based controller, a nonlinear model predictive control approach is developed. Contraction condition is imposed as a constraint on the optimisation problem for model predictive control with an economic cost function, utilising Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade off between the rate of target trajectory convergence and economic benefit (away from the desired process objective)

Structured Neural-PI Control with End-to-End Stability and Output Tracking Guarantees

We study the optimal control of multiple-input and multiple-output dynamical systems via the design of neural network-based controllers with stability and output tracking guarantees. While neural network-based nonlinear controllers have shown superior performance in various applications, their lack of provable guarantees has restricted their adoption in high-stake real-world applications. This paper bridges the gap between neural network-based controllers and the need for stabilization guarantees. Using equilibrium-independent passivity, a property present in a wide range of physical systems, we propose neural Proportional-Integral (PI) controllers that have provable guarantees of stability and zero steady-state output tracking error. The key structure is the strict monotonicity on proportional and integral terms, which is parameterized as gradients of strictly convex neural networks (SCNN). We construct SCNN with tunable softplus-$\beta$ activations, which yields universal approximation capability and is also useful in incorporating communication constraints. In addition, the SCNNs serve as Lyapunov functions, giving us end-to-end performance guarantees. Experiments on traffic and power networks demonstrate that the proposed approach improves both transient and steady-state performances, while unstructured neural networks lead to unstable behaviors.Comment: arXiv admin note: text overlap with arXiv:2206.0026