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This paper studies the problem of adaptive fuzzy asymptotical quantized tracking control of non-strict-feedback systems with unmodeled dynamics. A dynamic signal is used to cope with the unmodeled dynamics and fuzzy systems are introduced to approximate the packaged unknown nonlinearities. Based on backstepping technique and fuzzy approximation property, a systemic fuzzy adaptive control scheme is proposed. By the utilization of Lyapunov theory, the semi-globally uniformly ultimate boundedness of all closed-loop system signals and asymptotical tracking performance are guaranteed. The main contributions of this work are two aspects: (i) a backstepping-based quantized control algorithm is firstly extended to nonlinear systems with unmodeled dynamics and non-strict-feedback structure; (ii) the semi-globally asymptotic tracking control scheme is independent of the quantized parameter. Simulation results verify the presented control approach.

An adaptive finite-time consensus control method is investigated for nonlinear strict-feedback multi-agent systems that contain unknown parameters and a dynamic leader with both input and output. By using command filters, the “explosion of complexity” phenomenon in traditional backstepping technique can be avoided. The filter errors can be compensated by introducing compensating signals. Adaptive finite-time controllers are constructed by a backstepping technique, command filters, and compensating signals. In addition, the consensus performance and stability of closed-loop systems can be guaranteed in finite time basd on a practical finite-time stability criterion. Finally, a simulation example demonstrates the feasibility and effectiveness of the proposed adaptive finite-time consensus control method.

This work studies the issue of finite-time tracking control for a class of nonlinear systems with state constraints. By means of command filtered backstepping technique, finite-time theory and Barrier Lyapunov Functions (BLF), a novel finite-time command filtered backstepping approach is presented to guarantee the finite-time convergence of tracking errors. The new proposed method can not only reduce the complexity of computation of the conventional backstepping control and compensate filtered errors by Dynamic Surface Control (DSC), but also can guarantee that the state variables are restricted in compact bounding sets. Moreover, the proposed controller is designed and applied to Transmission Control Protocol/Active Queue Management (TCP/AQM) network systems, which guarantees the practical boundedness of all the signals in the closed-loop system. Finally, the effectiveness and practicability of the developed control strategy are validated by a TCP network simulation example.

An Artificial Moment Method (AMM) is proposed for conflict resolution of swarm robots with path planning tasks, where some robots are in narrow passages and close to their targets while many other robots need to pass through the passages. In the AMM, an algorithm based on key companion candidates and their weight times is presented first for key companions of the robots close to their targets. As such, other robots, even with different motion directions, can bypass the robots in a more reasonable manner. Then, two new algorithms are presented for attractive points and attractive angles of robots or to modify the obtained attractive angles. The existing artificial moment motion controller is also improved. Consequently, the negative effects of robots on each other are decreased, and conflicts between robots can be resolved more easily. Simulations indicate that compared with existing AMMs, the proposed one can yield better solutions in complex situations.

In this paper, an event-triggered strategy and an optimality scheme are presented to design an output-feedback controller for nonlinear discrete-time systems. Firstly, owing to the unavailability of the system states, a coordinate transformation is executed instead of state observers. Then, an action neural network and a specified critic neural network are presented to obtain the optimal controller and estimate the novel long-term cost function, respectively. The adaptation laws are designed on the basis of the gradient descent rule and event-triggered mechanism. The control signals are transmitted only when the event is triggered. Based on the Lyapunov analysis theory, the stability and the tracking performance of the closed-loop system are proven. The effectiveness of the proposed strategy is verified via simulation examples.

This paper focuses on the tracking problem for a class of nonlinear time-delay systems with external disturbances in a non-strict-feedback structure, and an event-trigger-based finite-time adaptive neural controller is designed. Suitable Lyapunov-Krasovskii functionals are constructed to deal with the time-delay terms in this system. Combining the structural property of radial basis function neural networks and backstepping methodology, the design difficulty from the non-strict-feedback structure of the system is solved. A finite-time prescribed performance function is introduced to drive the tracking error to a small neighborhood of the origin in a finite time. The proposed event-triggered control scheme has a larger threshold than that of the fixed-threshold scheme to reduce the communication burden and ensure that all of the signals of the closed-loop system are semi-globally uniformly ultimately bounded. Simulation results demonstrate the effectiveness of the proposed scheme.

This paper focuses on the issue of adaptive finite-time tracking control for uncertain strict-feedback nonlinear systems with full state constraints. For the first time, a new finite-time adjustable barrier function is introduced, whose design parameters can be regulated dynamically in real time with the change of tracking error. It cannot only converge to a preset region in a finite settling time with a faster convergent barrier, but it further optimizes the system transient performance. By embedding the proposed barrier function, a novel barrier Lyapunov function (BLF) is used for the design of an adaptive finite-time tracking controller, which guarantees that the output tracking error converges to a small region in finite time without violation of the constraint, and then tends to zero. All of the signals in the closed-loop system are bounded. A simulation verifies the effectiveness of the proposed control scheme.

This paper focuses on the fixed-time almost disturbance decoupling problem of nonlinear time-varying systems with multiple disturbances and dead-zone input. Less restrictive assumption for unknown nonlinear functions is provided. Via the recursive approach, a state feedback controller is designed by guaranteeing the fixed-time stability of the closed-loop system without external disturbances and a prescribed degree for the influence of the external disturbances on the output, in which the upper bound of the settling time only depends the control design parameters. Finally, a numerical example is given to show the effectiveness and advantages of the proposed design method.

In this paper, the problem of adaptive fuzzy fault-tolerant tracking control is studied for a class of non-lower-triangular nonlinear systems with actuator failures. According to the same structure of virtual control inputs and the monotonously increasing property of the bounding functions, a variable separation approach is proposed. By applying the backstepping technique, an adaptive fuzzy control approach is presented which can guarantee the semi-global boundedness of all signals within the closed-loop system. The tracking error using the proposed control scheme eventually converges to a small neighborhood of the origin.

A function segment artificial moment method is proposed for sensor-based path planning of a single robot in complex environments. Similar to the existing artificial moment method, an attractive point is obtained first at each step for guiding the robot to move along a shorter path. Then, the robot moves one step to the next position under the guidance of the attractive point and the control of an artificial moment motion controller. Different from the existing one, attractive function segments are used for attractive points and the artificial moment motion controller is improved. As each attractive function segment can be used for several steps, the computational burden for attractive points is reduced considerably. Furthermore, positive and negative key obstacle function segment sets are proposed to determine whether the target is reachable when a new attractive function segment is required. If the target is reachable, then, a motion direction for the robot is determined by using the key obstacle function segment sets. Simulation results indicate that the proposed method is effective and can yield better solutions in complex environments.

In this paper, a neural learning mechanism is presented for a class of single-input–single-output (SISO) uncertain nonlinear systems, which can achieve knowledge acquisition, storage and reuse of the unknown system dynamics as well as the predefined tracking error behavior bound. Using the novel transformed function, the constrained tracking control problem of the original nonlinear system is transformed into the stabilization problem of an augmented system. By combining a filter tracking error with the universal approximation capabilities of radial basis function (RBF) neural networks (NNs), a stable adaptive neural control (ANC) scheme is proposed to guarantee the ultimate boundedness of all the signals in the closed-loop system and the prescribed transient and steady tracking control performance. In the steady control process, a partial persistent excitation (PE) condition of RBF NNs is satisfied during tracking control to recurrent reference orbits. Consequently, it is shown that the proposed ANC scheme can acquire and store knowledge of the unknown system dynamics. The stored knowledge is reused to develop neural learning control, so that the improved control performance with the faster tracking convergence rate and the less computational burden is achieved, while guaranteeing the prescribed transient and steady tracking performance when the initial condition satisfies the prescribed performance bound. Simulation studies are performed to demonstrate and verify the effectiveness of the proposed scheme.

In this paper, the problem of adaptive neural tracking control is considered for a class of single-input/single-output (SISO) strict-feedback stochastic nonlinear systems with input saturation. To deal with the non-smooth input saturation nonlinearity, a smooth nonaffine function of the control input signal is used to approximate the input saturation function. Classical adaptive technique and backstepping are used for control synthesis. Based on the mean-value theorem, a novel adaptive neural control scheme is systematically derived without requiring the prior knowledge of bound of input saturation. It is shown that under the action of the proposed adaptive controller all the signals of the closed-loop system remain bounded in probability and the tracking error converges to a small neighborhood around the origin in the sense of mean quartic value. Two simulation examples are provided to demonstrate the effectiveness of the presented results.

This paper focuses on the problem of adaptive control for a class of nonlinear multi-input and multi-output (MIMO) systems with time delays. A state feedback adaptive controller is constructed by backstepping technique. Fuzzy logic systems are used to approximate unknown nonlinear functions in the process of the controller design. The main advantages of the proposed approach are twofold: (1) the controller design is independent of the knowledge of the basis functions of fuzzy logic systems, and (2) the suggested approach requires only m adaptive laws to control a nonlinear time-delay system with m inputs. Simulation results illustrate the effectiveness of the proposed approach.

This paper focuses on the problem of direct adaptive fuzzy control for nonlinear strict-feedback systems with time-varying delays. Based on the Razumikhin function approach, a novel adaptive fuzzy controller is designed. The proposed controller guarantees that the system output converges to a small neighborhood of the reference signal and all the signals in the closed-loop system remain bounded. Different from the existing adaptive fuzzy control methodology, the fuzzy logic systems are used to model the desired but unknown control signals rather than the unknown nonlinear functions in the systems. As a result, the proposed adaptive controller has a simpler form and requires fewer adaptation parameters.

In this paper, a novel adaptive fuzzy control scheme is proposed for a class of uncertain single-input and single-output (SISO) nonlinear time-delay systems with the lower triangular form. Fuzzy logic systems are used to approximate unknown nonlinear functions, then the adaptive fuzzy tracking controller is constructed by combining Lyapunov-Krasovskii functionals and the backstepping approach. The proposed controller guarantees uniform ultimate boundedness of all the signals in the closed-loop system, while the tracking error converges to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters is not more than the order of the systems under consideration. Finally, simulation studies are given to demonstrate the effectiveness of the proposed design scheme.