This study presents a generalized model predictive torque control (GMPTC) scheme that applies to all types of synchronous machines (SMs), which are used for vehicle traction. The GMPTC problem is formulated to simultaneously minimize the torque error and the performance index (PI) while satisfying the voltage and current constraints. The PI can be defined by any function to be minimized to enhance the SM drive's performance, such as copper loss and inverter loss. When the torque command is not achievable due to either of the constraints, the problem is defined differently to maximize the torque magnitude under the two constraints. The GMPTC problem, a nonlinear optimization problem, is solved based on either a continuous control set (CCS) or finite control set (FCS) using the augmented Lagrangian method. The GMPTC scheme guarantees optimal operation under all operating regions, including the maximum torque per ampere, flux weakening, maximum current, and maximum torque per voltage, even without additional controllers. The GMPTC method is practical and easy to implement because it involves one optimization problem with a small number (four to five) of tuning parameters. This aspect differs from existing model predictive control schemes for SMs that involve an additional optimization, usually solved offline, to obtain optimal references for the stator currents or stator flux linkages. The effectiveness of the proposed GMPTC method is demonstrated numerically and experimentally by controlling interior permanent magnet synchronous machines based on both the CCS and FCS.

In this paper, a neuro-adaptive controller with weight norm constraints is proposed for uncertain Euler-Lagrange systems. The boundedness of the weights in the neuro-adaptive controller design is important to prevent excessively large control inputs and system instability. To ensure the boundedness of the weights, the weight norm constraints are imposed as inequality constraints in the weight adaptation. The adaptation law is derived based on the constrained optimization method. The stability of the proposed controller is analyzed in the sense of Lyapunov, ensuring the boundedness of the tracking error and weight estimation. For the comparative study, two benchmark controllers and the proposed controller were evaluated through a numerical simulation of a two-link manipulator system and compared in terms of tracking performance and parameter dependency. The comparative study verified that the proposed controller has better tracking performance and lower parameter dependency.

This study presents a constrained optimizationbased neuro-adaptive controller (CoNAC) for uncertain Euler-Lagrange systems subject to weight norm and input constraints. A deep neural network (DNN) is employed to approximate the ideal stabilizing control law, compensating for lumped system uncertainties while addressing both types of constraints. The weight adaptation laws are formulated through a constrained optimization problem, ensuring first-order optimality conditions at steady state. The controller's stability is rigorously analyzed using Lyapunov theory, guaranteeing bounded tracking errors and DNN weights. Numerical simulations comparing CoNAC with three benchmark controllers demonstrate its effectiveness in tracking error regulation and satisfaction of constraints.

Synchronous machines (SMs) are characterized as nonlinear dynamic systems, with stator flux linkages being critical for controller design. Among the developed methods for flux linkage estimation, the disturbance observer-based flux linkage estimator (DOB-FLE) is recognized as the state-of-theart. However, DOB-FLE faces challenges in ensuring exponential convergence during transient states. To address this, this paper introduces an extended state observer-based flux linkage estimator (ESO-FLE), which represents an advancement over DOB-FLE. This novel approach utilizes extended states to model the nonlinear disturbance term as time-varying ramp signals, offering a contrast to the constant assumption employed in DOB-FLE. It concurrently estimates both the extended states and the flux linkages through an ESO. Simulation results from a 35-kW SM drive demonstrate that ESO-FLE provides superior transient performance under dynamic conditions compared to DOB-FLE.

This study presents a physics-informed online learning method for modeling the flux linkages of synchronous machines (SMs). The approach trains neural networks (NNs) to learn the physical laws governing the flux linkages while adhering to the physical constraints inherent in the model. The learning rules are designed to satisfy the first-order optimality conditions with quadratic convergence. The proposed method can be used for both online state estimation and online model learning, ensuring fast convergence for local behavior and gradual, comprehensive convergence for global behavior, respectively.

Increasing estimation accuracy and reducing noise sensitivity are challenging trade-offs in designing disturbance observers (DOBs). The DOB gain tuning process for overcoming this trade-off is not straightforward, nor does it guarantee optimal performance for the resulting DOBs. This paper presents a dynamic gain DOB that intelligently adjusts its gain based on deep reinforcement learning (DRL) to overcome this tradeoff. First, a variable gain DOB is designed by modifying the conventional DOB. The variable gain DOB can exponentially estimate a constant disturbance with a varying gain. Then, DRL is used to train a dynamic gain adjuster for the variable gain DOB. A case study demonstrated that the proposed dynamic gain DOB increases its gain only when needed (i.e., when the estimation error is significant) and otherwise decreases the gain to reduce noise. Comparison with the conventional DOB of various constant gains shows that the proposed DOB achieves superior performance.

A tuning-parameter-free and matrix-inversionfree solution of nonlinear programming (NLP) problems is proposed. The key idea is to design an update law based on Lyapunov analysis to satisfy the first-order necessary conditions for optimality. To this aim, first, the Lyapunov function is defined as the summation of the norms of these conditions. Then, the desired optimization variables and Lagrange multipliers, which minimize the Lyapunov function the most, are found analytically, thereby rapidly approaching the necessary conditions. The proposed method neither requires tuning parameters nor matrix inversions; thus, it can be implemented easily with less iterations and computational load than conventional methods, such as sequential quadratic programming (SQP) and augmented Lagrangian method (ALM). The effectiveness of the proposed method is applied to and validated by using it to solve a nonlinear model predictive torque control (NMPTC) problem in electrical drives. The results are compared with those of SQP and ALM.

Cooperative on-ramp merging control for connected and automated vehicles (CAVs) can significantly enhance traffic flow and fuel efficiency at highway merging points. However, in mixed traffic scenarios where CAVs coexist with human-driven vehicles (HDVs), the unpredictable behavior of HDVs poses challenges to safety and coordination. While many cooperative merging strategies focus on individual CAV control, fewer have addressed the coordination of multiple CAVs in such settings. This study introduces an optimization-based cooperative merging strategy for all CAVs within a control zone, considering interactions with HDVs of uncertain intentions. A key innovation is the strategic influence of CAVs on HDV behavior by slowing down the CAV preceding HDVs, thereby allowing other CAVs on the adjacent road to merge in front of the HDVs with reduced uncertainty. The optimal slowdown pattern is identified by evaluating CAV throughput across various candidate patterns, with dynamic optimization applied at each time a new vehicle enters the control zone to effectively manage HDV uncertainties. Experimental results from various mixed-traffic scenarios show that the proposed strategy reduces the average travel time delay (ATTD) by up to 54% compared to the first-in-first-out (FIFO) benchmark.