Real-Time Discrete Adaptive Control of Robot Arm Based on Digital Signal Processing

A discrete model reference adaptive controller of robot arm is obtained by integrating the reduced dynamic model of robot, model reference adaptive control （MRAC） and digital signal processing （DSP） computer system into an electromechanical system. With the DSP computer system, the control signal of each joint of the robot arm can be processed in real time and independently. The simulation and experiment results show that with the control strategy, the robot achieved a good trajectory following precision, a good decoupling performance and a high real-time adaptivity.

Adaptive predictor-based control for a helicopter system with input delays:Design and experiments

In this paper,we consider a 2-degrees-of-freedom(DOF)helicopter system subject to long input delays and uncertain system parameters.To address the challenges including unknown system parameters and input delays in control design,we develop an adaptive predictor-feedback control law to achieve trajectory tracking.Stability of the closed-loop system is further established,where the tracking errors are shown to converge towards zero.Through simulation and experiments on the helicopter system,we illustrate that tracking of a desired trajectory is achieved with the proposed control scheme.

Cooperative Adaptive Cruise Control Platoon Fleet Formation Model Considering Efficiency and Stability

In the existing formation model,vehicles in the same lane or adjacent lane are regarded as the structure,and the driving behavior of vehicles is studied from the perspectives of safety,speed consistency,and stability,and the speed control model is proposed from the perspective of vehicles themselves,to obtain a stable fleet with the same distance and speed.However,in this process,the initial condition of the vehicle,the traffic flow environment,and the efficiency of the fleet formation are less considered.Therefore,based on summarizing the existing fleet building model,this paper puts forward the rapid construction model and algorithm of a cooperative adaptive cruise control platoon fleet.One of the important goals of forming a team is to enter the team with the smoothest trajectory in the shortest time.Therefore,this chapter studies the trajectory optimization of the vehicle formation process from the perspective of vehicle dynamics.

Finite-time Adaptive Fault-tolerant Control for Nonlinear Systems With Multiple Faults

This paper focuses on the problem of adaptive finitetime fault-tolerant control for a class of non-lower-triangular nonlinear systems.The faults encountered in the control system include the actuator faults and the abrupt system fault.By applying backstepping design and neural networks approximation,an adaptive finite-time fault-tolerant control scheme is developed.It is shown that the proposed controller ensures that all signals in the closed-loop system are semi-globally practically finite-time stable and the track-ing error converges to a small neighborhood around the origin within finite time.The simulation is carried out to explain the validity of the developed strategy.

Adaptive output feedback control for nonlinear time-delay systems using neural network

This paper extends the adaptive neural network （NN） control approaches to a class of unknown output feedback nonlinear time-delay systems. An adaptive output feedback NN tracking controller is designed by backstepping technique. NNs are used to approximate unknown functions dependent on time delay, Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the NN approximation errors. Based on Lyapunov- Krasovskii functional, the semi-global uniform ultimate boundedness of all the signals in the closed-loop system is proved, The feasibility is investigated by two illustrative simulation examples.

Adaptive Neural Network Dynamic Surface Control for Perturbed Nonlinear Time-delay Systems

This paper proposes an adaptive neural network control method for a class of perturbed strict-feedback nonlinear systems with unknown time delays. Radial basis function neural networks are used to approximate unknown intermediate control signals. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown time delay terms have been compensated. Dynamic surface control technique is used to overcome the problem of ＂explosion of complexity＂ in backstepping design procedure. In addition, the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system is proved. A main advantage of the proposed controller is that both problems of ＂curse of dimensionality＂ and ＂explosion of complexity＂ are avoided simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the approach.

Adaptive sliding mode control of modular self-reconfigurable spacecraft with time-delay estimation

The reconstruction control of modular self-reconfigurable spacecraft (MSRS) is addressed using an adaptive sliding mode control (ASMC) scheme based on time-delay estimation (TDE) technology. In contrast to the ground, the base of the MSRS is floating when assembled in orbit, resulting in a strong dynamic coupling effect. A TED-based ASMC technique with exponential reaching law is designed to achieve high-precision coordinated control between the spacecraft base and the robotic arm. TDE technology is used by the controller to compensate for coupling terms and uncertainties, while ASMC can augment and improve TDE’s robustness. To suppress TDE errors and eliminate chattering, a new adaptive law is created to modify gain parameters online, ensuring quick dynamic response and high tracking accuracy. The Lyapunov approach shows that the tracking errors are uniformly ultimately bounded (UUB). Finally, the on-orbit assembly process of MSRS is simulated to validate the efficacy of the proposed control scheme. The simulation results show that the proposed control method can accurately complete the target module’s on-orbit assembly, with minimal perturbations to the spacecraft’s attitude. Meanwhile, it has a high level of robustness and can effectively eliminate chattering.

Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism

Helicopter systems present numerous benefits over fixed-wing aircraft in several fields of application.Developing control schemes for improving the tracking accuracy of such systems is crucial.This paper proposes a neural-network(NN)-based adaptive finite-time control for a two-degree-of-freedom helicopter system.In particular,a radial basis function NN is adopted to solve uncertainty in the helicopter system.Furthermore,an event-triggering mechanism(ETM)with a switching threshold is proposed to alleviate the communication burden on the system.By proposing an adaptive parameter,a bounded estimation,and a smooth function approach,the effect of network measurement errors is effectively compensated for while simultaneously avoiding the Zeno phenomenon.Additionally,the developed adaptive finite-time control technique based on an NN guarantees finitetime convergence of the tracking error,thus enhancing the control accuracy of the system.In addition,the Lyapunov direct method demonstrates that the closed-loop system is semiglobally finite-time stable.Finally,simulation and experimental results show the effectiveness of the control strategy.

Finite-time adaptive sliding mode control for heavyweight airdrop operations

This paper investigates the problem of designing a fast convergent sliding mode flight controller of a transport aircraft for heavyweight airdrop operations in the presence of bounded uncertainties without the prior knowledge of the bounds. On the basis of feedback linearization of the aircraft-cargo motion system, a novel integral sliding mode flight control law with gains adaptation is proposed. It contains a nominal control law used to achieve finite-time stabilization performance and a compensated control law used to reject the uncertainties. The switching gains of the compensated control law are tuned using adaptation algorithms, and the knowledge of the bounds of the uncertainties is not required to be known in advance. Meanwhile, the severe chattering of the sliding mode control that caused by high switching gains is effectively reduced. The controller and its performance are evaluated on a transport aircraft performing a maximum load airdrop task in a number of simulation scenarios.

Adaptive neural network tracking control for a class of unknown nonlinear time-delay systems

For a class of unknown nonlinear time-delay systems, an adaptive neural network （NN） control design approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct a robust memoryless adaptive NN tracking controller. Unknown time-delay functions are approximated by NNs, such that the requirement on the nonlinear time-delay functions is relaxed. Based on Lyapunov-Krasoviskii functional, the sem-global uniformly ultimately boundedness （UUB） of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters. The feasibility is investigated by an illustrative simulation example.

Robust adaptive control for interval time-delay systems

This paper focuses on the robust adaptive control problems for a class of interval time-delay systems and a class of large-scale interconnected systems. The nonlinear uncertainties of the systems under study are bounded by high- order polynomial functions with unknown gains. Firstly, the adaptive feedback controller which can guarantee the stability of the closed-loop system in the sense of uniform ultimate boundedness is proposed. Then the proposed adaptive idea is extended to robust stabilizing designing method for a class of large-scale interconnected systems. Here, another problem we address is to design a decentralized feedback adaptive controller such that the closed-loop system is stable in the sense of uniform ultimate boundedness for all admissible uncertainties and time-delay. Finally, an illustrative example is given to show the validity of the proposed approach.

Adaptive Variable Structure Control of MIMO Nonlinear Systems with Time-varying Delays and Unknown Dead-zones

In this paper, adaptive variable structure neural control is presented for a class of uncertain multi-input multi-output （MIMO） nonlinear systems with state time-varying delays and unknown nonlinear dead-zones. The unknown time-varying delay uncer- tainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The approach removes the assumption of linear function outside the deadband without necessarily constructing a dead-zone inverse as an added contribution. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the residual and optimal approximation error as well as the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. In addition, a modified adaptive control algorithm is given in order to avoid the high-frequency chattering phenomenon. Simulation results demonstrate the effectiveness of the approach.

Adaptive Fuzzy Observer Backstepping Control for a Class of Uncertain Nonlinear Systems with Unknown Time-delay

In this paper, a new adaptive fuzzy backstepping control approach is developed for a class of nonlinear systems with unknown time-delay and unmeasured states. Using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is designed for estimating the unmeasured states. On the basis of the state observer and applying the backstepping technique, an adaptive fuzzy observer control approach is developed. The main features of the proposed adaptive fuzzy control approach not only guarantees that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded, but also contain less adaptation parameters to be updated on-line. Finally, simulation results are provided to show the effectiveness of the proposed approach.

Adaptive Iterative Learning Control for Nonlinear Time-delay Systems with Periodic Disturbances Using FSE-neural Network

An adaptive iterative learning control scheme is presented for a class of strict-feedback nonlinear time-delay systems, with unknown nonlinearly parameterised and time-varying disturbed functions of known periods. Radial basis function neural network and Fourier series expansion （FSE） are combined into a new function approximator to model each suitable disturbed function in systems. The requirement of the traditional iterative learning control algorithm on the nonlinear functions （such as global Lipschitz condition） is relaxed. Furthermore, by using appropriate Lyapunov-Krasovskii functionals, all signs in the closed loop system are guaranteed to be semiglobally uniformly ultimately bounded, and the output of the system is proved to converge to the desired trajectory. A simulation example is provided to illustrate the effectiveness of the control scheme.

Observer-based Adaptive Iterative Learning Control for Nonlinear Systems with Time-varying Delays

An observer-based adaptive iterative learning control （AILC） scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays. The linear matrix inequality （LMI） method is employed to design the nonlinear observer. The designed controller contains a proportional-integral-derivative （PID） feedback term in time domain. The learning law of unknown constant parameter is differential-difference-type, and the learning law of unknown time-varying parameter is difference-type. It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized. By constructing a Lyapunov-Krasovskii-like composite energy function （CEF）, we prove the boundedness of all closed-loop signals and the convergence of tracking error. A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.

Robustness Analysis of Discrete-time Indirect Model Reference Adaptive Control with Normalized Adaptive Laws

For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter are also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.

Adaptive control for a class of discrete-time time-delay systems with regard to delay parameter

The memory state feedback control problem for a class of discrete-time systems with input delay and unknown state delay is addressed based on LMIs and Lyapunov-Krasovskii functional method. Under the action of our designed adaptive control law, the unknown time-delay parameter is included in memory state feedback controller. Using LMI technique, delay-dependent sufficient conditions for the existence of the feedback controller are obtained. Finally, the effectiveness of the proposed design method is demonstrated by a numerical example.

A Systematic Analysis Approach to Discrete-time Indirect Model Reference Adaptive Control

这份报纸论述间接模型引用的设计和分析有为分离时间的系统的一个类的规范的适应法律的适应控制(MRAC ) 。主要工作包括三部分。第一，构造工厂参数评价算法不仅拥有传统的评价算法的象那些的一样的性质而且在零避免分割的可能性。第二，由发现在工厂参数之间的关系，估计和控制器参数估计并且用工厂参数的性质估计，控制器参数估计的类似的性质也被建立。第三，基于关系，在使正常化之间的性质发信号并且在靠近环的系统并且在分离时间的系统上的一些重要数学工具上的所有信号作为在连续时间的盒子中，到分离间接 MRAC 计划的系统的稳定性和集中分析途径严厉地被开发。

Adaptive control of uncertain time-delay chaotic systems

This work investigates adaptive control of a large class of uncertain time-delay chaotic systems （UTCSs） with unknown general perturbation terms bounded by a polynomial （unknown gains）, Associated with the different cases of known and unknowl system matrices, two corresponding adaptive controllers are proposed to stabilize unstable fixed points of the systems by means of Lyapunov stability theory and linear matrix inequafities （LMI） which can be solved easily by convex optimization algorithms, Two examples are used for examining the effectiveness of the proposed methods.