Adaptive Dynamic Control Research Articles

Adaptive dynamic surface full state constraints control for stochastic Markov jump systems based on event-triggered strategy

This paper aims to investigate the event-triggered adaptive dynamic surface full state constraints control for a class of stochastic nonlinear systems with Markov jumping parameters. Using the backstepping method, we propose two adaptive dynamic surface controllers with average dwell time and the event-triggered strategies simultaneously. The existed assumption of stochastic input-to-state stability (ISS) on the stochastic systems can be avoided by adding a correction terms into the controller to compensate for the measurement error. The method designed in this work can make all signals remain bounded in probability, all the states satisfy the constraints in probability and the tracking error signals eventually converge to the compact set in the sense of mean quartic value (SMQV) for closed-loop stochastic Markov jump nonlinear uncertain system. Furthermore, the designed relative threshold strategy which relies on the control signal reduces the frequency of events triggered. Finally, the validity of put forward method is shown in simulation results.

DTCMS: Dynamic traffic congestion management in Social Internet of Vehicles (SIoV)

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic considering different routes on the road segments. Social relationships are devised on various aspects of travel needs for a safe, agile, and better driving experience. Simulation results illustrate the efficacy of the proposed scheme with high traffic throughput, service rate and reduce the total travelling time, delay time, and average waiting time in comparison with Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm.

H∞ Tracking Control of Uncertain Markovian Hybrid Switching Systems: A Fuzzy Switching Dynamic Adaptive Control Approach.

This article investigates the H∞ stochastic tracking control problem for uncertain fuzzy Markovian hybrid switching systems by using a fuzzy switching dynamic adaptive control approach. The long and the short is to construct multiple piecewise stochastic Lyapunov functions which provide an effective tool for designing hybrid switching law and fuzzy switching dynamic adaptive law. A hybrid switching law, including both stochastic switching and deterministic switching, is designed to represent more general switching scenarios, which can improve the H∞ adaptive tracking performance through offering a running time before stochastic switching for the adaptive control strategy to work well. A fuzzy switching dynamic adaptive control technique is developed such that all signals of the tracking error equation are bounded, and the system state trajectory tracks the reference model state trajectory under a disturbance attenuation level as closely as possible. Finally, an application study verifies the effectiveness of the acquired methods.

Neural Adaptive Dynamic Surface Asymptotic Tracking Control for a Class of Uncertain Nonlinear System

In this paper, by incorporating the neural network into an adaptive dynamic surface control (DSC) framework, a neural adaptive DSC algorithm is developed for a class of uncertain nonlinear system to ensure the asymptotic output tracking. Neural network is used to approximate the unknown nonlinear term in the system such that the requirements for known nonlinear term in control laws design procedure are released. In order to eliminate the boundary layer effects, which are caused by the linear filters at each step in the DSC procedure, the nonlinear filters with the compensation term are designed skillfully. The proposed neural adaptive DSC algorithm not only avoids the inherent problem of “explosion of complexity” in the backstepping procedure, but also has its own advantages: (1) releasing the requirements for known nonlinear term in control laws design procedure; (2) holding the asymptotic output tracking performance. Some simulations are shown to demonstrate the effectiveness and advantages of the proposed controller.

Equilibrium optimizer based multi dimensions operation of hybrid AC/DC grids

This paper develops a recent population-based of Equilibrium Optimizer Algorithm (EOA) for solving the optimal power flow (OPF) problem in hybrid AC/DC power grids. The proposed OPF problem handles several objective functions that reflect multi dimensions economic-technical and environmental operation requirements of modern power systems. The considered objectives incorporate minimizing the total generation costs, generation environmental emissions, total power losses, and the deviations of the bus voltages. These objectives are handled separately and simultaneously to provide the preference capability to the operator objectives. EOA has adaptive dynamic control parameters. It mimics the dynamic and equilibrium states related to the mass balance models where, each concentration of search agent is randomly updated in the sake of reaching the final optimal fitness. Finally, 12 case studies are carried out via the developed EOA, particle swarm optimizer and differential evolution for the modified IEEE 14-, 30-bus test systems and West Delta power system (WDPS) in Egypt. The second-order cone OPF model is considered. Also, adding wind units and their impacts on the OPF solution is assessed for the hybrid AC/DC grid. The simulation results demonstrate the great effectiveness of the developed EOA in solving the OPF in hybrid power systems.

Dual-Rate Adaptive Optimal Tracking Control for Dense Medium Separation Process Using Neural Networks

Dense medium separation (DMS) is of great significance for coal cleaning. The DMS control system always involves dense medium density adjustment and ash content control that are operating on fast and slow time scales, respectively. The inherent time-varying and strongly nonlinear characteristics of the DMS process give rise to challenges for the design of this multitime scale control system. To address this issue, this article proposes a dual-rate adaptive optimal tracking control approach for the DMS system. For the basic loop process, a nonlinear adaptive PI controller containing a neural network (NN)-based unmodeled dynamics compensator is proposed. Then, a lifting technique is used to unify the time scales of the two loops accompanied by formulating a generalized controlled object, whose dynamics is completely unknown. On this basis, a data-driven operation optimization control method that combines adaptive dynamic programming algorithm and reference control is developed, which is implemented using NNs. Finally, the stability of the proposed method is analyzed. The simulation results indicate its effectiveness.

Energy-saving control of long-range autonomous underwater vehicle vertical plane based on human simulating intelligent control method

The long-range autonomous underwater vehicle is a new underwater vehicle with capability of stereoscopic observation of the ocean over a wide range of time series. This article proposed a novel control strategy for the long-range autonomous underwater vehicle considering the energy consumption. The vertical motion model of long-range autonomous underwater vehicle and the mathematical model of energy consumption of motion actuators are established in this article, and the maneuverability simulation experiments were carried out to analyze its motion and energy consumption characteristics. A hybrid controller based on human simulating intelligent control and S-plane control is designed. Considering the moment caused by the asymmetry of the hull in motion, an adaptive dynamic control allocation strategy is designed. Simulation experiments are conducted to demonstrate the performance of the scheme proposed.

Adaptive dynamic programming-based decentralised control for large-scale nonlinear systems subject to mismatched interconnections with unknown time-delay

This paper addresses decentralised optimal control problems for large-scale nonlinear systems subject to mismatched interconnections with unknown time-delay by adaptive dynamic programming. To eliminate the effects of mismatched interconnections with unknown time-delay, a novel local value function is constructed to transform the decentralised control problem into an optimal control problem. A local robust observer is established to identify the bound of the unknown interconnections. Then, based on the observer-critic architecture, the decentralised optimal control policy is achieved by solving local Hamiltonian–Jacobi–Bellman equation via local policy iteration algorithm. The stability of the closed-loop large-scale nonlinear system is guaranteed to be uniformly ultimately bounded by implementing a set of decentralised control policies. Simulation examples demonstrate the effectiveness of the proposed scheme.

Adaptive Implicit Inverse Control for a Class of Discrete-Time Hysteretic Nonlinear Systems and Its Application

This article proposes an adaptive implicit inverse control scheme for a class of discrete-time hysteretic nonlinear systems. The Prandtl–Ishlinskii model is employed to characterize the hysteresis loop in piezoelectric actuator. The main contributions are as follows: 1) by using the dynamic surface control technique, which introduces the digital first-order low-pass filter, the original control system are not required to be transformed into an unknown special form; 2) the hysteresis implicit inverse compensator is constructed to overcome the hysteresis, which implies that the hysteresis item coupled with control signal is treated as the temporary control signal from which the method of searching the approximately control signal is designed; and 3) by employing the experimental platform of the piezoelectric positioning stage, the experimental verifications of the designed discrete-time adaptive controller are implemented. It is proved that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded and the experimental results show the effectiveness of the proposed adaptive dynamic surface discrete-time motion control (ADSDMC) scheme.

Hierarchical control strategy for networked DC microgrid based on adaptive dynamic program and event-triggered consensus algorithm considering economy and actuator fault

At present, the hierarchical control strategy which includes cyber and physical layers is one of the most widely used control strategies in networked direct current microgrid. In general, through the controller and actuator, the communication data in cyber layer can be used to accomplish the control missions in physical layer, e.g. achieving stable operation or economic operation etc. However, when actuator fault occurs in communication, the normal operation of cyber layer will be affected. Thus, the control effect of physical layer will be also affected. Considering with the above problems, a hierarchical control strategy based on adaptive dynamic program and event-triggered consensus algorithms is proposed to realize the economic operation of microgrid. In this strategy, the main designs are as follows: 1. To deal with the actuator fault problem in the process of consensus control, the fault observer and adaptive dynamic program-based controller are designed in the cyber layer; 2. Also in the cyber layer, an event-triggered consensus algorithm is proposed to adjust distributed energy resources to output their optimal power and make microgrid run economically; 3. To maintain the voltage stable while the power is adjusted, a secondary control strategy is proposed in the physical layer. Finally, the experimental results have verified the effectiveness of the designed strategies.

Continuous shared control in prosthetic hand grasp tasks by Deep Deterministic Policy Gradient with Hindsight Experience Replay

Grasp using a prosthetic hand in real life can be a difficult task. The amputee users are often capable of planning the reaching trajectory and hand grasp location selection, however, failed in precise finger movements, such as adapting the fingers to the surface of the object without excessive force. It is much efficient to leave that part to the machine autonomy. In order to combine the intention and planning ability of users with robotic control, the shared control is introduced in which users’ inputs and robot control methods are combined to achieve a goal. The shared control problem can be formulated as a Partially Observable Markov Decision Process. To find the optimal control policy, we adopt an adaptive dynamic programming and reinforcement learning-based control algorithm-Deep Deterministic Policy Gradient combined with Hindsight Experience Replay. We proposed the algorithm with a prediction layer using the reparameterization technique. The system was tested in a modified simulation environment for the ability to follow the user’s intention and keep the contact force in boundary for safety.

Adaptive Cross-Coupled Control of Cable-Driven Parallel Robots With Model Uncertainties

Cable-driven parallel robots (CDPRs) are robots with novel structures, wherein flexible cables, instead of rigid links, are employed to pull mobile platforms. This structural change enables CDPRs to not only offer potential advantages, but also introduces control challenges with regard to frictional uncertainties, dynamic modeling errors, and multi-cable motion coordination. To address these concerns, a novel adaptive cross-coupled control (ACCC) scheme, capable of achieving high-accuracy trajectory tracking control, has been proposed in this study. In the proposed ACCC scheme, a new synchronization error variable has been defined to adjust the coordination motion of adjacent cables; additionally, an adaptive dynamic control strategy has been designed to compensate for the inertia and friction of winches linked to cables. Lastly, an adaptive robust control has been used to restrain un-modeled dynamics and external disturbances. The proposed ACCC scheme was compared against the synchronization control (SC) scheme and the well-known adaptive control (AC) scheme of Slotine-Li by applying them to an actual non-redundant CDPR during trajectory tracking experiments. Corresponding results demonstrated that compared to the SC and the AC scheme, the proposed ACCC scheme serves to improve both tracking and synchronization accuracies of cables, thereby ultimately enhancing the control performance of mobile platforms. Robustness of the ACCC scheme against external disturbance has also been verified.

Adaptive tracking control of uncertain nonlinear system with time delays, external disturbances and dead-zone input

In this work, the tracking control problem of nonlinear system which contains unknown parameters, multiple state delays, external disturbances and the dead-zone input is studied. By introducing a dynamic gain-based adaptive control strategy and employing a new Lyapunov–Krasovskii (L–K) functional, we construct an adaptive tracking controller to ensure the boundedness of all the states and guarantee the tracking error small enough. Two examples are given to verify the correctness of the control theory.

Adaptive distributed observer design for containment control of heterogeneous discrete-time swarm systems

This paper develops both adaptive distributed dynamic state feedback control law and adaptive distributed measurement output feedback control law for heterogeneous discrete-time swarm systems with multiple leaders. The convex hull formed by the leaders and the system matrix of leaders is estimated via an adaptive distributed containment observer. Such estimations will feed the followers so that every follower can update the system matrix of the corresponding adaptive distributed containment observer and the system state of their neighbors. The followers cooperate with each other to achieve leader–follower consensus and thus solve the containment control problem over the network. Numerical results demonstrate the effectiveness and computational feasibility of the proposed control laws.

Output Feedback Adaptive Dynamic Surface Sliding-Mode Control for Quadrotor UAVs with Tracking Error Constraints

In this paper, a fuzzy adaptive output feedback dynamic surface sliding-mode control scheme is presented for a class of quadrotor unmanned aerial vehicles (UAVs). The framework of the controller design process is divided into two stages: the attitude control process and the position control process. The main features of this work are (1) a nonlinear observer is employed to predict the motion velocities of the quadrotor UAV; therefore, only the position signals are needed for the position tracking controller design; (2) by using the minimum learning technology, there is only one parameter which needs to be updated online at each design step and the computational burden can be greatly reduced; (3) a performance function is introduced to transform the tracking error into a new variable which can make the tracking error of the system satisfy the prescribed performance indicators; (4) the sliding-mode surface is introduced in the process of the controller design, and the robustness of the system is improved. Stability analysis proved that all signals of the closed-loop system are uniformly ultimately bounded. The results of the hardware-in-the-loop simulation validate the effectiveness of the proposed control scheme.

Dynamic Modelling and Adaptive Control of Automobile Active Suspension System

Dynamic Modelling and Adaptive Control of Automobile Active Suspension System

From Trajectory Tracking Control to Leader–Follower Formation Control

This work investigates the leader–follower formation control of multiple nonholonomic mobile robots. First, the formation control problem is converted into a trajectory tracking problem and a tracking controller based on the dynamic feedback linearization technique drives each follower robot toward its corresponding reference trajectory in order to achieve the formation. The desired orientation for each follower is selected such that the nonholonomic constraint of the robot is respected, and thus the tracking of the reference trajectory for each follower is feasible. An adaptive dynamic controller that considers the actuators dynamics in the design procedure is proposed. The dynamic model of the robots includes the actuators dynamics in order to obtain the velocities as control inputs instead of torques or voltages. Using Lyapunov control theory, the tracking errors are proven to be asymptotically stable and the formation is achieved despite the uncertainty of the dynamic model parameters. In order to assess the proposed control laws, a ROS-framework is developed to conduct real experiments using four ROS-enabled mobile robots TURTLEBOTs. Moreover, the leader fault problem, which is considered as the main drawback of the leader–follower approach, is solved under ROS. An experiment is conducted where in order to overcome this problem, the desired formation and the leader role are modified dynamically during the experiment.

Tracking control of a class of nonlinear systems with output delay based on adaptive fuzzy dynamic surface control

ABSTRACTThis paper concerns with the tracking control problem of a class of unknown nonlinear time-delay systems in the presence of output delay and inaccessible states. To deal with the difficulties arising from unmeasurable states, a fuzzy logic observer based on delayed outputs is firstly employed. Then, based on the delayed output, observed states and filtered virtual control signals, proper change of coordinates is defined for the purpose of designing dynamic surface control. By introducing appropriate Lyapunov-Krasovskii functionals in the design procedures, a controller and adaptation laws are obtained for the control strategy of nonlinear systems subject to output delay. Moreover, by selecting design parameters and matrices based on the acquired stability criteria, it is proved that the proposed adaptive fuzzy dynamic surface output feedback control approach is able to guarantee the uniformly ultimately boundedness of all the signals in the closed loop unknown nonlinear output-delay systems. Finally, the theoretic achievements are applied to three well-known benchmark systems to illustrate the effectiveness and efficiency of the proposed approach.

Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction

The problem of optimal tracking control for robot–environment interaction is studied in this article. The environment is regarded as a linear system and an admittance control with iterative linear quadratic regulator method is obtained to guarantee the compliant behaviour. Meanwhile, an adaptive dynamic programming-based controller is proposed. Under adaptive dynamic programming frame, the critic network is performed with radial basis function neural network to approximate the optimal cost, and the neural network weight updating law is incorporated with an additional stabilizing term to eliminate the requirement for the initial admissible control. The stability of the system is proved by Lyapunov theorem. The simulation results demonstrate the effectiveness of the proposed control scheme.

Influence of parameters of adaptive control system on vibratory drilling efficiency

One of the necessary conditions of ensuring the quality of deep hole drilling during manufacturing pieces from hard-to-machine metals is chip control. It can be ensured by applying harmonic oscillations to a drill bit in the direction of rotation axis. One possible way of maintaining such vibrations is to use a vibratory drilling head which contains an elastic element. This element allows axial displacements of the tool. If stiffness of the elastic element and the machining parameters are chosen correctly, self-actuating of axial vibrations of a drill bit in compliance with regenerative effect may occur. It is advisable to add control response, which is determined in the feedback loop, to this mechanism because of the significant damping in the cutting zone. The control response maintains the required process characteristics to ensure chip control. The algorithm of vibratory drilling dynamics adaptive control has been examined in this paper. The additional impact, which is proportional to axial speed of the drill bit, is supposed to be made on the vibration system according to this algorithm. The feedback gain is determined in the adaptation loop comparing the current value of peak-to-peak displacement and its target value. The dynamics modeling of closed loop non-linear system “elastic system-machining process-control system” has been carried out. The graphs of vibratory drilling integral characteristics plotted against processing characteristics and vibratory head parameters have been presented in this paper. These graphs are based on the multivariant modeling. The influence of the adaptation algorithm parameters on the quality of vibratory processing has been also studied.