This brief presents a unified control framework that integrates a feedback linearization (FL) controller in the inner loop with an adaptive data-enabled policy optimization (DeePO) controller in the outer loop to balance an autonomous bicycle. While the FL controller stabilizes and partially linearizes the inherently unstable and nonlinear system, its performance is compromised by unmodeled dynamics and time-varying characteristics. To overcome these limitations, the DeePO controller is introduced to enhance adaptability and robustness. The initial control policy of DeePO is obtained from a finite set of offline, persistently exciting (PE) input and state data. To improve stability and compensate for system nonlinearities and disturbances, a robustness-promoting regularizer refines the initial policy, while the adaptive section of the DeePO framework is enhanced with a forgetting factor to improve adaptation to time-varying dynamics. The proposed FL-DeePO approach is evaluated through simulations and real-world experiments on an instrumented autonomous bicycle. Results demonstrate its superiority over the FL-only approach and a reinforcement learning (RL) controller, achieving more precise tracking of the reference lean angle and lean rate.

This paper addresses the control of the state of charge (SoC) of a Battery Energy Storage System (BESS) in a microgrid, considering uncertainties in load and Renewable Energy Sources (RES) generated power estimations. To achieve this objective, we propose RubPC, a novel rule-based Model Predictive Control (MPC). We partition the feasible operation space of the microgrid into two subzones, referred to as the white and yellow zones. The yellow zone represents the boundary space between the feasible and unfeasible operation spaces. In RubPC, we initially implement MPC on a predefined optimization window to determine the optimal SoC of the BESS, aiming to keep the microgrid within the white zone. Noting that mismatches between estimated and actual load and generated power may lead to constraint violations, we introduce a rule-based controller as a supervisory control. This controller monitors the microgrid's state, and if the microgrid enters the yellow zone, it adjusts the control to maintain the microgrid within the white zone. We validate our proposed method by simulating it using data from an electrified quarry site in Sweden.

This paper introduces a novel approach, which we refer to as hybrid moving controller, designed to ensure closed-loop stability while eliminating the requirement for synchronization between the plant and control unit. In our proposed method, the controller is time-varying and moves the closed-loop eigenvalues along radial trajectories originating from the origin. The sequence of controllers is assumed to be kept confidential from potential adversaries. Given that this moving controller renders the overall closed-loop system time-varying, maintaining the eigenvalues within the unit circle alone is insufficient to guarantee stability. As a result, we explore stability through the lens of contraction theory and present criteria for the sequence of controllers to ensure stability.

In this work, we address the problem of balancing an autonomous bicycle using direct data-driven control. Firstly, we demonstrate that a direct implementation of data-driven approaches may not guarantee reliable performance, and is highly dependent on how the parameters are selected. To address this issue, we make the reasonable assumption that we have access to some inaccurate information about the system. We use this inaccurate information to design a feedback linearization, based on a simplified point mass model of the bicycle, which does not accurately represent the dynamics of the system. Next, we suggest an inner and outer-loop control strategy. In the inner loop, we implement the aforementioned feedback linearization controller. Subsequently, in the outer loop, we consider the combination of the autonomous bicycle and the feedback controller as a black box, and we design a direct data-driven controller from acquired data. We use a SolidWorks model of a real autonomous bicycle to evaluate the performance of our proposed control approach and to compare it with the direct data-driven controller design derived from acquired data of the bicycle without feedback linearization. The results show that our proposed strategy significantly improves the performance and makes the data-driven control approach more reliable across a broader range of parameter choices compared to a data-driven controller designed based on data from the system without feedback linearization. Finally, we show that introducing an additional integral-like state further enhances the system’s performance.

The federated learning (FL) paradigm aims to distribute the computational burden of the training process among several computation units, usually called agents or workers, while preserving private local training datasets. This is generally achieved by resorting to a server-worker architecture where agents iteratively update local models and communicate local parameters to a server that aggregates and returns them to the agents. However, the presence of adversarial agents, which may intentionally exchange malicious parameters or may have corrupted local datasets, can jeopardize the FL process. Therefore, we propose selective trimmed average (SETA), which is a resilient algorithm to cope with the undesirable effects of a number of misbehaving agents in the global model. SETA is based on properly filtering and combining the exchanged parameters. We mathematically prove that the proposed algorithm is resilient against data and local model poisoning attacks. Most resilient methods presented so far in the literature assume that a trusted server is in hand. In contrast, our algorithm works both in server-worker and shared memory architectures, where the latter excludes the necessity of a trusted server. The theoretical findings are corroborated through numerical results on MNIST dataset and on multiclass weather dataset (MWD).

This paper deals with resilient and privacy-preserving control to optimize the daily operation costs of networked Battery Energy Storage Systems (BESS) in a multi-agent network vulnerable to various types of cyber-attacks. First, we formulate the optimization problem by defining the objective function and the local and coupling constraints. Next, we introduce a novel resilient decentralized control and optimization algorithm that can mitigate the effects of cyber-attacks, specifically false data injection attacks and hijacking, to enhance the network's resilience. The proposed method is based on filtering out outlier Lagrange multipliers in a suitable dual problem. Our proposed algorithm has two main advantages compared to the existing literature. Firstly, it can solve problems where the coupling constraint is not restricted to the average or a function of the average of decision variables. Secondly, our algorithm extends the well-known dual decomposition and Lagrange multiplier method to the decentralized control problem of BESSs. In the proposed algorithm presented in this paper, only the data relevant to the dual problem is exchanged among the agents. Noticing that the data of the dual problem does not contain any private information, mitigating privacy concerns associated with our proposed algorithm. We formally prove the convergence of our algorithm to a feasible and sub-optimal solution. Additionally, simulations demonstrate the effectiveness of our results.Note to Practitioners-Optimal coordinated control of BESSs increases the power system's reliability and reduces costs. With the expansion of the use of small-scale BESSs in household customers, it is possible to considerably increase the free capacity of power networks by optimally controlling these small BESSs. The methods published so far to solve such problems either share the private information of each BESS or are not resilient to failures or false data injection due to cyber-attacks. Therefore, these approaches are not favored in practical applications. Considering this practical motivation, in this paper, we present a decentralized algorithm to control a large set of BESSs in a platform vulnerable to various types of cyber-attacks without compromising privacy.

This manuscript presents a novel distributed greedy framework applicable to a network of thermostatically controlled loads (TCLs) to desynchronize the network’s aggregated power consumption. Compared to the existing literature, our proposed framework offers two distinct novelties. First, our proposed algorithm relaxes the restrictive assumptions associated with the communication graph among TCLs. To elaborate, our algorithm only requires a connected graph to execute control, a condition less demanding than its counterpart algorithms that mandate a star architecture, $K$ -regular graphs, or undirected connected graphs. Second, a significant novel feature is the relaxation of the obligation to share private information, such as each unit’s local power consumption and appliance temperatures, either with a central coordinator or neighboring TCLs. The findings presented in this brief are validated through simulations conducted over a network comprising $1000$ TCLs.

This letter considers resilient decentralized constrained optimization in multi-agent systems where some agents due to cyberattacks become adversaries. We show that the proposed method is resilient despite the persistent influence of up to F anonymous adversaries in the complete graphs. Our approach provides a better approximation of the optimal solution than the current literature. If the agents' objectives are 2F redundant, then the algorithm converges to the optimal solution. In addition to current literature, we consider a constrained optimization problem. Finally, we present numerical simulations to corroborate the theoretical analysis.

In this paper we propose a distributed optimization and control protocol to optimize the behavior of a network of domestic Battery Energy Storage Systems (BESS) which offers several advantages with respect to privacy protection due to its control and information sharing architecture. We use the Lagrange multipliers in our optimization protocol. The model of the network of BESS includes the possibility of local power generation and power transfer to the grid. The proposed method enables autonomous decision making for each BESS. Information regarding the state of charge and related constraints of each BESS is not shared, thus increasing the protection of the user privacy. Numerical results are provided to validate the proposed approach.

In this article a novel soft variable structure control method is proposed to reduce the settling time of liner time-invariant multi-input systems with constrained inputs. The method is a generalized pole placement technique which extends the desired pole locations to radial pole paths to reduce settling time while satisfying input constraints. The control signal obtained by this method is soft and is therefore suited to industrial applications. The paper provides a test for the stability of the control system based on the multivariable circle criterion and the Kalman-Yakubovich-Popov lemma. Simulation results where the method is applied to an aviation example, and comparison with pole placement, demonstrate how the method significantly increases the speed of response in spite of the input constraints.

SummaryThis article proposes a novel robust feedback linearization control scheme for affine uncertain nonlinear systems subject to matched uncertainties and constraints on the control input. In this method, instead of placing the linearized system poles at exact locations, radial paths in the open left‐hand plane are selected to freely move the poles so as to enhance as much as possible the speed of response while guaranteeing satisfaction of input signal constraints. The stability of our proposed method is analyzed by means of the multivariable circle criterion and the Kalman‐Yakubovich‐Popov lemma. Simulation results demonstrate how the method significantly increases the speed of response compared to fixed pole placements.