Search

This paper presents a gate driver for a GaN-based half-bridge structure operating in a buck converter with input voltage >40 V or a boost converter with output voltage >30 V. Two 500 pF on-chip capacitors are utilized to construct three-level gate drivers, providing a near- $V_{{\text {DD}}}$ negative voltage for gate of the rectifier switch to eliminate the induced pulse on the gate from the high dv / dt slew rate of $V_{\text {X}}$ when the main switch is turned on. The dead time controller tunes the delay of the gate signal of the rectifier switch by sensing the slope of $V_{\text {X}}$ , thus the near-optimal zero-voltage switching can be achieved with deviation < 3 ns. The GaN driver is implemented with a 0.18- $\mu \text{m}$ BCD process. The efficiencies can be improved by 8.33% and 6.87% at light load in a buck and a boost converter due to the dead-time control. The peak efficiencies of 20 V–12 V and 12 V–18 V conversions are 86.37% and 84.39%, respectively. [ABSTRACT FROM AUTHOR]

This paper is concerned with bipartite tracking for double-integrator networked systems with signed communication graphs, where both cooperative and antagonistic interactions coexist. A finite-time bipartite tracking framework is established, where followers track either the state or the opposite state of a leader. Different from some conventional results with convergence over an infinite time horizon, the finite-time convergence in this paper is achieved in an accurate manner. Under structurally balanced signed graphs, an integral sliding mode based finite-time bipartite tracking controller is proposed. The construction of an integral sliding mode variable is to ensure that the system dynamics is driven onto a sliding surface in finite-time. On the sliding surface, neighbouring states are used together with the homogeneous technique to guarantee that bipartite tracking is achieved in finite-time. To further realize fixed-time bipartite tracking, a controller is designed by using the integral sliding mode and the bi-limit homogeneous concept. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed controllers. [ABSTRACT FROM AUTHOR]

This paper presents a high-performance solar energy harvesting system with improved adaptive maximum power point tracking (AMPPT) method utilizing neural network (NN) model as assistance. Under the guidance of the negative feedback control (NFC) model, a BJT based voltage control oscillator with three off-chip reconfigurable resistors is designed in this paper to improve the reusability of the solar energy harvesting system. Meanwhile, the AMPPT accuracy has much improvement by co-simulation of MATLAB/Simulink and Virtuoso with the help of NN model of Photovoltaic (PV) Cell. The complete system with output voltage of 4.2V to power the battery is designed and fabricated in $0.18~\mu \text{m}$ CMOS technology. According to the test results, the system can track the maximum power points (MPP) successfully with average voltage tracking errors of 0.23% (0.01-0.51%) of PV cell 1 and 0.29% (0.01-0.5%) of PV cell 2 when the light intensity changes from 3000lux to 10000lux. Without power hungry current sensor or voltage sensor and other complicated control circuits, the peak efficiency is about 89.39% @ 3000lux. [ABSTRACT FROM AUTHOR]

This paper presents the design of an optimal controller for solving tracking problems subject to unmeasurable disturbances and unknown system dynamics using reinforcement learning (RL). Many existing RL control methods take disturbance into account by directly measuring it and manipulating it for exploration during the learning process, thereby preventing any disturbance induced bias in the control estimates. However, in most practical scenarios, disturbance is neither measurable nor manipulable. The main contribution of this article is the introduction of a combination of a bias compensation mechanism and the integral action in the Q-learning framework to remove the need to measure or manipulate the disturbance, while preventing disturbance induced bias in the optimal control estimates. A bias compensated Q-learning scheme is presented that learns the disturbance induced bias terms separately from the optimal control parameters and ensures the convergence of the control parameters to the optimal solution even in the presence of unmeasurable disturbances. Both state feedback and output feedback algorithms are developed based on policy iteration (PI) and value iteration (VI) that guarantee the convergence of the tracking error to zero. The feasibility of the design is validated on a practical optimal control application of a heating, ventilating, and air conditioning (HVAC) zone controller. [ABSTRACT FROM AUTHOR]

In this study, the probabilistic-constrained $\mathcal {H}_{\infty }$ tracking control problem is addressed for a class of stochastic nonlinear systems subject to measurement outliers, stochastic nonlinearities and DoS attacks. The considered systems have the following characters: 1) an unequal redundant-channel communication protocol is adopted to resist the DoS attacks and enhance the reliability of packet transmission. Different from some existing redundant-channel approaches, the considered two channels have different transmission abilities. 2) The measurement outliers (e.g., some unexpected large amplitude disturbances) are concerned and a saturation-function-based observer is proposed to avert the side effects from measurement outliers. 3) The DoS jamming attacks are considered in this paper, and an aperiodic DoS model is utilized. 4) Both sector-bounded nonlinear function and stochastic nonlinear function are involved in the considered systems, which makes the considered systems more general. To compensate the negative effect of the DoS jamming attacks and enhance the reliability of the packet transmission, a novel redundant-channel-based switching protocol is proposed to schedule data transmission when the plant suffers malicious cyber attacks. The primary objective of the present study is to design tracking controller such that the prescribed $\mathcal {H}_{\infty }$ control performance is reached and probabilistic constraints on the tracking error are satisfied simultaneously. To achieve this objective, a probabilistic-constrained $\mathcal {H}_{\infty }$ tracking control algorithm is established to obtain tracking controller gains and minimize the prescribed set of the constraint. Finally, two simulation examples are used to validate the practicability of our devised strategy. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of bipartite consensus tracking for multi-agent systems with both cooperative and antagonistic interactions. These interactions among vertices in a signed digraph are first investigated with the signs of the weighted products of associated paths in their augmented undirected graph. To improve the convergence rate, a finite-time protocol and a fixed-time protocol are developed to solve the bipartite consensus tracking problem with a finite-time and a settling time independent of any initial states, respectively. Moreover, an M-matrix based method is well proposed to estimate all parameters in the constructed Lyapunov function, therefore, the upper bounds of the settling times for finite-time and fixed-time cases can be analytically determined. Finally, numerical examples are provided to demonstrate the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

Owing to the flexibility of coupling structure, low standing losses and low Electromagnetic Interference (EMI), capacitive power transfer (CPT) has drawn much attention as one of the new wireless power transfer (WPT) technologies. However, to date, the load effect on the system performance has not been analyzed comprehensively, and the CPT system is usually fully tuned (tank network resonant frequency fully tuned to the fundamental components of the nominal switching frequency), which is challenging to maintain practically. Based on these considerations, this paper first provides a detailed analysis of how the load can affect the performance of a CPT system. An optimal load resistance is shown to exist for a CPT system with a non-fully/partially tuned LCC tank (tank network resonant frequency is designed to be lower than the fundamental components of the switching frequency). By dynamically transforming the load to its optimal value, the system can transfer maximum power to the load while maintaining zero voltage switching (ZVS) operation. Then, a controlled buck-boost converter based on perturbation and observation algorithm is introduced to the system to obtain the optimal equivalent resistance against the actual load variations via tracking the maximum power transfer point. The final prototype has demonstrated that a maximum power of 10 W is obtained, with an end-to-end power efficiency of about 70% over a wide range of load variations from $5~\Omega $ to 1 $\text{k}\Omega $. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the asymptotic output tracking control problem of probabilistic Boolean control networks. Firstly, based on the largest control invariant subset and the limit theory of Markov chains, the conditions are proposed to judge whether the output trajectory of a probabilistic Boolean control network can asymptotically track a constant reference signal via the state feedback control and output feedback control, respectively. Next, based on an augmented system, similar criteria are obtained to judge whether the output of a reference Boolean network is asymptotically trackable by the output trajectory of a probabilistic Boolean control network under the state feedback control and output feedback control, respectively. The corresponding controller design methods are given. Two examples are shown to illustrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This paper investigates the bipartite tracking problem of networked robotic systems (NRSs) subject to input disturbances, discrete communications and signed directed graphs. Two new classes of hierarchical hybrid control algorithms (HHCAs), which involve both continuous and discontinuous signals in a uniform framework, are designed to solve the aforementioned problem in the model-independent control manner, i.e., without using the prior information of the system model. Besides, with the help of the Lyapunov statement and hybrid system theory, we establish several sufficient conditions for guaranteeing the convergence of the proposed hybrid algorithms. Finally, numerical examples are presented to illustrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This paper presents a complex frequency domain approach to investigate the cluster tracking performance of linear heterogeneous multi-agent networks. Firstly, a designed graph matrix is given to eliminate the interaction influence between coupled subnetworks with different tracking signals. On this basis, the cluster tracking performance analysis problem is decomposed into two parts, i.e., stability analysis and steady-state performance analysis of multi-agent networks. Secondly, based on the network structure, a minimum separable subgraph set is proposed to transform the stability of the multi-agent network into the stability of the separable subnetworks. Further, a signal flow diagram is introduced to solve the stability of the separable subnetworks. Thirdly, in view of Final-value theorem, a necessary and sufficient condition is proposed for achieving the cluster tracking control for the closed-loop stable multi-agent network. This result provides a strong theoretical basis for the flexible design of controllers due to that it quantitatively reveals the inherent law of steady-state performance for multi-agent networks. Finally, an algorithm is established to calculate the steady-state error of a general directed multi-agent network. Some simulations are provided to demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper addresses the trajectory tracking control problem for underactuated unmanned aerial vehicles (UAVs), with specific focus on vehicles with thrust-vectoring capabilities. According to the different actuation mechanisms, the most common UAV platforms can achieve only a partial decoupling of attitude and position tasks. Since position tracking is of utmost importance for applications involving aerial vehicles, we propose a control scheme in which position tracking is the primary objective. To this end, this paper exploits the concept of attitude planner, a dynamical system through which the desired attitude reference is processed to guarantee the satisfaction of the primary objective: The attitude tracking task is considered as a secondary objective, which can be realized as long as the desired trajectory satisfies specific trackability conditions. [ABSTRACT FROM AUTHOR]

In this paper, the adaptive neural network (NN) tracking control problem is addressed for robot manipulators subject to dead-zone input. The control objective is to design an adaptive NN controller to guarantee the stability of the systems and obtain good performance. Different from the existing results, which used NN to approximate the nonlinearities directly, NNs are employed to identify the originally designed virtual control signals with unknown nonlinear items in this paper. Moreover, a sequence of virtual control signals and real controller are designed. The adaptive backstepping control method and Lyapunov stability theory are used to prove the proposed controller can ensure all the signals in the systems are semiglobally uniformly ultimately bounded, and the output of the systems can track the reference signal closely. Finally, the proposed adaptive control strategy is applied to the Puma 560 robot manipulator to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

In this article, we first show that the main results claimed in the paper by Bikas and Rovithakis (2021) involve some errors as the selection conditions for the design parameters are not sufficient to ensure the boundedness of all the closed-loop signals, then we provide the corresponding corrections under the same controller, so that the underlying quantized control problem can be tackled. [ABSTRACT FROM AUTHOR]

In this paper, the consensus tracking problem is investigated for a class of continuous switched stochastic nonlinear multiagent systems with an event-triggered control strategy. For continuous stochastic multiagent systems via event-triggered protocols, it is rather difficult to avoid the Zeno behavior by the existing methods. Thus, we propose a new protocol design framework for the underlying systems. It is proven that follower agents can almost surely track the given leader signal with bounded errors and no agent exhibits the Zeno behavior by the given control scheme. Finally, two numerical examples are given to illustrate the effectiveness and advantages of the new design techniques. [ABSTRACT FROM AUTHOR]

The simultaneous presence of parameter variations, time-varying disturbances, and delay in the control loop of an $n{\text{th}}$ -order (possibly unstable) dynamical system makes formal tracking control design difficult. Existing model-based control methodologies can handle only a subset of the above-mentioned complexities, and hence, become insufficient to accurately track the desired trajectory. In this paper, a prediction-based adaptive robust control framework is proposed for high performance control of dynamical systems subject to the above-mentioned complexities, which involves the following. First, prediction-based projection-type adaptation laws with model compensation to reduce the effect of parameter uncertainties under delay and time-varying disturbances. Second, a robust prediction scheme that factors in both unknown parameters and disturbance uncertainties under delay to handle the resulting unmatched disturbances. Third, prediction-based continuous robust feedback to attenuate the cumulative effect of disturbance terms due to uncertain prediction. The controller guarantees semiglobal, exponential convergence of the tracking error with an ultimate error bound proportional to delay, disturbance, and controller gain. The controller effectiveness is demonstrated with an illustrative flight control example and compared with the baseline adaptive robust controller. [ABSTRACT FROM AUTHOR]

In this paper, a learning-based robust tracking control scheme is proposed for a quadrotor unmanned aerial vehicle system. The quadrotor dynamics are modeled including time-varying and coupling uncertainties. By designing position and attitude tracking error subsystems, the robust tracking control strategy is conducted by involving the approximately optimal control of associated nominal error subsystems. Furthermore, an improved weight updating rule is adopted, and neural networks are applied in the learning-based control scheme to get the approximately optimal control laws of the nominal error subsystems. The stability of tracking error subsystems with time-varying and coupling uncertainties is provided as the theoretical guarantee of learning-based robust tracking control scheme. Finally, considering the variable disturbances in the actual environment, three simulation cases are presented based on linear and nonlinear models of quadrotor with competitive results to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new framework for model-predictive control (MPC) with generalized zone tracking. The proposed zone MPC tracks a generalized target set of system state and input which is not necessarily control-invariant. In this context, the classical MPC theory no longer applies because the target zone may not be stable in the sense of Lyapunov. We extend LaSalle's invariance principle and develop new theories for stability analysis of zone MPC. It is proved that under the zone MPC design, the system converges to the maximal control invariant set in the target zone. Sufficient conditions for asymptotic stability of the maximal control-invariant set are also discussed. By tracking the generalized target zone, the proposed zone MPC is able to: (i) yield smaller zone tracking errors than all existing methods which essentially track some steady-state subset of the target zone, and (ii) allow more admissible operations and release more degrees of freedom to achieve other economic objectives. Further discussions are made on extending the prediction horizon of the zone MPC based on an auxiliary control law as well as handling a secondary economic objective via a second-step economic optimization. [ABSTRACT FROM AUTHOR]

In this paper, a novel model-free adaptive control (MFAC) algorithm based on a dual successive projection (DuSP)-MFAC method is proposed, and it is analyzed using the introduced DuSP method and the symmetrically similar structures of the controller and its parameter estimator of MFAC. Then, the proposed DuSP-MFAC scheme is successfully implemented in an autonomous car “Ruilong” for the lateral tracking control problem via converting the trajectory tracking problem into a stabilization problem by using the proposed preview-deviation-yaw angle. This MFAC-based lateral tracking control method was tested and demonstrated satisfactory performance on real roads in Fengtai, Beijing, China, and through successful participation in the Chinese Smart Car Future Challenge Competition held in 2015 and 2016. [ABSTRACT FROM AUTHOR]

Structural support vector machine (SSVM) is popular in the visual tracking field as it provides a consistent target representation for both learning and detection. However, the spatial distribution of feature is not considered in standard SSVM-based trackers, therefore leading to limited performance. To obtain a robust discriminative classifier, this paper proposes a novel tracking framework that spatially regularizes SSVM, which yields a new spatially regularized SSVM (SRSSVM). We utilize the spatial regularization prior to penalize the learning classifier with the same size as the target region. The location of classifier spatially located far from the center of region is assigned large weight and vice versa. Then, it is introduced into the SSVM model as a regularization factor to learn the robust discriminative model. Furthermore, an optimizing algorithm with dual coordination descent is presented to efficiently solve the SRSSVM tracking model. Our proposed SRSSVM tracking method has low computational cost like the traditional linear SSVM tracker while can significantly improve the robustness of the discriminative classifier. The experimental results on three popular tracking benchmark data sets show that the proposed SRSSVM tracking method performs favorably against the state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of sparse online linear classification in changing environments. We first analyze the tracking performance of standard online linear classifiers, which use gradient descent for minimizing the regularized hinge loss. The derived shifting bounds highlight the importance of choosing appropriate step sizes in the presence of concept drifts. Notably, we show that a better adaptability to concept drifts can be achieved using constant step sizes rather than the state-of-the-art decreasing step sizes. Based on these observations, we then propose a novel sparse approximated linear classifier, called sparse approximated linear classification (SALC), which uses a constant step size. In essence, SALC simply rounds small weights to zero for achieving sparsity and controls the truncation error in a principled way for achieving a low tracking regret. The degree of sparsity obtained by SALC is continuous and can be controlled by a parameter which captures the tradeoff between the sparsity of the model and the regret performance of the algorithm. Experiments on nine stationary data sets show that SALC is superior to the state-of-the-art sparse online learning algorithms, especially when the solution is required to be sparse; on seven groups of nonstationary data sets with various total shifting amounts, SALC also presents a good ability to track drifts. When wrapped with a drift detector, SALC achieves a remarkable tracking performance regardless of the total shifting amount. [ABSTRACT FROM AUTHOR]

This paper investigates the position and attitude tracking control problem of a quadrotor unmanned aerial vehicle subject to modeling uncertainties and actuator failures. A comprehensive mathematical model reflecting the nonlinearity and state-space coupling of the dynamics as well as actuation faults and external disturbances is derived. By combining the radial basis function neural networks (NNs) with virtual parameter estimating algorithms, an indirect NN-based adaptive fault-tolerant control scheme is developed, which exhibits several attractive features as compared with most existing methods: 1) it is not only robust and adaptive to nonparametric uncertainties but also tolerant to unexpected actuation faults; 2) it ensures stable tracking without the need for precise information on system model; and 3) it only involves one lumped parameter adaptation, thus is structurally simpler and computationally less expensive, rendering the resultant scheme less demanding in programming and more affordable for onboard implementation. The effectiveness and benefits of the proposed method are confirmed via computer simulation. [ABSTRACT FROM AUTHOR]

This paper is concerned with the target tracking of underactuated autonomous surface vehicles with unknown dynamics and limited control torques. The velocity of the target is unknown, and only the measurements of line-of-sight range and angle are obtained. First, a kinematic control law is designed based on an extended state observer, which is utilized to estimate the uncertain target dynamics due to the unknown velocities. Next, an estimation model based on a single-hidden-layer neural network is developed to approximate the unknown follower dynamics induced by uncertain model parameters, unmodeled dynamics, and environmental disturbances. A bounded control law is designed based on the neural estimation model and a saturated function. The salient feature of the proposed controller is twofold. First, only the measured line-of-sight range and angle are used, and the velocity information of the target is not required. Second, the control torques are bounded with the bounds known as a priori. The input-to-state stability of the closed-loop system is analyzed via cascade theory. Simulations illustrate the effectiveness of the proposed bounded controller for tracking a moving target. [ABSTRACT FROM AUTHOR]

The article investigates an adaptive practical fixed-time tracking control of nonlinear systems with prescribed boundary constraints. The main control objective is that the system output variable tracks the desired signal at fixed time, and the tracking error can also converge to the prescribed boundary constraints. Primarily, a new coordinate transformation is introduced, which will be utilized in the proposed controller. Next, based on the backstepping scheme, the sufficient conditions for the existence of the adaptive practical fixed-time tracking controller are presented. Meanwhile, the fuzzy logic systems are employed to approximate to unknown parts in the controlled system. Theoretical analysis shows that the tracking error converges to the prescribed boundary constraints at fixed time. At the same time, all the signals in the closed-loop controlled system are bounded and the controlled system is practically fixed-time stable. At last, simulation results verify the effectiveness of the proposed scheme by comparing the cases with/wihtout prescribed boundary constraints. [ABSTRACT FROM AUTHOR]

An active electronic interface for maximizing the power extracted from a resonant piezoelectric vibration harvester by means of a multi-variable Maximum Power Point Tracking (MPPT) technique is presented. The input voltage of the electronic interface is dynamically adapted in order to ensure that the maximum power is extracted even if the vibration characteristics change with time. The digital control unit updates the amplitude, the phase and the frequency of such an input voltage without requiring the high-frequency sampling and elaboration of the harvester current or voltage. The unit exploits only the information on the vibration frequency kept from a small piezo accelerometer and a low-frequency sampling of the DC current. The implemented algorithm is as simple as the widespread Perturb & Observe algorithm, but it allows the extraction of significantly more power from the harvester. The power increase for a piezoelectric harvester with the proposed electronic interface is investigated both theoretically and experimentally. By using a commercially available piezoelectric harvester, the maximum power tracking performance of the proposed active interface is experimentally compared with that of a usual AC/DC converter with Perturb & Observe MPPT control. [ABSTRACT FROM AUTHOR]