Neural network-based practical/ideal integral sliding mode control

This letter deals with the design of a novel neural network based integral sliding mode (NN-ISM) control for nonlinear systems with uncertain drift term and control effectiveness matrix. Specifically, this letter extends the classical integral sliding mode control law to the case of unknown nominal model. The latter is indeed reconstructed by two deep neural networks capable of approximating the unknown terms, which are instrumental to design the so-called integral sliding manifold. In this letter, the ultimate boundedness of the system state is formally proved by using Lyapunov stability arguments, thus providing the conditions to enforce practical integral sliding modes. The possible generation of ideal integral sliding modes is also discussed. Moreover, the effectiveness of the proposed NN-ISM control law is assessed in simulation relying on the classical Duffing oscillator

Freeway traffic control via second-order sliding modes generation

The paper deals with the design of a Suboptimal Second-Order Sliding Mode (SSOSM) control algorithm for local ramp metering of freeway systems. Indeed, sliding mode control is well-known for its robustness in front of uncertain terms and it perfectly fits to solve the control problem in case of traffic systems. Moreover, the proposed control law is able to steer the so-called sliding variable, chosen as the error between the density of the cell in the vicinity of the ramp and its reference value, to zero in a finite time. The traffic flow is modeled by means of the macroscopic second-order METANET model and the approach is finally assessed in simulation with satisfactory results

Robust multi-model predictive control via integral sliding modes

This letter presents a novel optimal control approach for systems represented by a multi-model, i.e., a finite set of models, each one corresponding to a different operating point. The proposed control scheme is based on the combined use of model predictive control (MPC) and first order integral sliding mode control. The sliding mode control component plays the important role of rejecting matched uncertainty terms possibly affecting the plant, thus making the controlled equivalent system behave as the nominal multi-model. A min-max multi-model MPC problem is solved using the equivalent system without further robustness oriented add-ons. In addition, the MPC design is performed so as to keep the computational complexity limited, thus facilitating the practical applicability of the proposal. Simulation results show the effectiveness of the proposed control approach

Sliding mode based droop control strategies for parallel-connected inverters in railway vehicles

This paper deals with the design of sliding mode based droop control strategies for parallel-connected inverters in railway vehicles. Indeed, the presence of auxiliary devices, which can be connected and disconnected at any time instant, makes the introduction of parallel modules an efficient solution. Among the possible techniques, droop control represents an efficient and easy-to-implement approach. However, each inverter is affected by load variations, nonlinearities and unavoidable modelling uncertainties, thus making the use of sliding mode controllers perfectly adequate for this kind of application. More specifically, relying on a sliding surface designed on the basis of a voltage-current droop characteristic, two second order sliding mode (SOSM) control algorithms, belonging to the class of Super-Twisting and Suboptimal SOSM control, are proposed

Higher-Order Sliding Mode design with Bounded Integral Control generation

In this paper uncertain continuous-time nonlinear systems affine in the control variable and with saturated actuators are considered. The finite-time regulation problem of the system output to zero is then solved by proposing a generic Higher-Order Sliding Mode (HOSM) controller equipped with a novel mechanism to encounter the saturation limits, thus extending previous results on saturated control inputs valid only in case of specific r-order sliding mode algorithms. The so-called Bounded Integral Control (BIC) method is reformulated into the framework of continuous HOSM, so as to replace the traditional integrator used to generate the continuous signal directly fed into the plant. Stability conditions for tuning the proposed algorithm are provided, and a numerical example finally assesses the effectiveness of the proposed technique. (C) 2022 Elsevier Ltd. All rights reserved

Self-Configuring Robot Path Planning With Obstacle Avoidance via Deep Reinforcement Learning

This letter proposes a hybrid control methodology to achieve full body collision avoidance in anthropomorphic robot manipulators. The proposal improves classical motion planning algorithms by introducing a Deep Reinforcement Learning (DRL) approach trained ad hoc for performing obstacle avoidance, while achieving a reaching task in the operative space. More specifically, a switching mechanism is enabled whenever a condition of proximity to the obstacles is met, thus conferring to the dual-mode architecture a self-configuring capability in order to cope with objects unexpectedly invading the workspace. The proposal has been finally tested relying on a realistic robot manipulator simulated in a V-REP environment

Simultaneous design of passive and active spacecraft attitude control using black-box optimization

This paper investigates the simultaneous design of active attitude control and passive attitude compensation mechanism for a spacecraft to satisfy practically-motivated mission objectives and constraints. The expressions of these fitness-related metrics with respect to the design variables are not analytically available, due to the nontrivial interactions between the spacecraft components and the interactions with the environment. Thus, such functions can only be approximately learned from data derived from simulations. We approach this difficult design problem using a black-box optimization (BBO)-based approach, which combines learning and optimizing the objective and constraint functions by design of experiments. The proposed BBO-based approach is assessed in the context of a 3U CubeSat system design with both a passive magnetic attitude compensation and an active reaction wheel-based control, tested on a simulator considering orbital and environmental dynamics. Simulation results and statistical tests compared to other design methods show the capability of the BBO-based approach to provide a design with the best tracking performance while at the same time satisfying ground station communication requirements and power budget

Switched adaptation strategies for integral sliding mode control: Theory and application

Integral sliding mode (SM) control is an interesting approach, as it can maintain the good chattering alleviation property of higher‐order SMs while making the reaching phase less critical and keeping the controlled system trajectory on a suitably selected sliding manifold since the initial time instant. In order to make such a method more robust and to improve its flexibility by the adaptation of its parameters to the current system condition, in this paper, a switched strategy is proposed. Specifically, the suboptimal Second‐order SM algorithm is considered as a basis in its integral formulation, and the switching strategy is designed by partitioning the so‐called auxiliary system state space in a finite number of regions. The proposed method allows one to improve the transient performance by adapting the gains through these regions, thus implying an energy saving capability. The proposal is theoretically analyzed and, in order to test its performance, the control of the lateral dynamics of ground vehicles is used as a case study. Specifically, yaw‐rate tracking is considered, as it is made difficult by parametric uncertainties and nonlinear effects that arise especially with large steering angles. Extensive simulation tests are carried out using standard validation maneuvers, which favorably witness the performance of the new control algorithm