Download Adaptive Control of Systems with Actuator Failures by Gang Tao PhD, Shuhao Chen, Xidong Tang, Suresh M. Joshi PhD PDF

By Gang Tao PhD, Shuhao Chen, Xidong Tang, Suresh M. Joshi PhD (auth.)

When an actuator fails, chaos or calamity can usually ensue.

It is as the actuator is the ultimate step within the keep watch over chain, whilst the keep an eye on system’s directions are made bodily actual that failure may be so vital and tough to atone for. whilst the character or situation of the failure is unknown, the offsetting of consequent method uncertainties turns into much more awkward.

Adaptive keep an eye on of structures with Actuator Failures facilities on counteracting occasions during which unknown regulate inputs develop into indeterminately unresponsive over an doubtful time period via adapting the responses of ultimate useful actuators. either "lock-in-place" and varying-value mess ups are handled. the consequences awarded demonstrate:

• the lifestyles of nominal plant-model matching controller constructions with linked matching stipulations for all attainable failure patterns;

• the alternative of a fascinating adaptive controller structure;

• derivation of novel mistakes versions within the presence of failures;

• the layout of adaptive legislation permitting controllers to reply to combos of uncertainties stemming from activator mess ups and method parameters.

Adaptive regulate of platforms with Actuator Failures could be of value to regulate engineers normally and particularly to either teachers and business practitioners engaged on safety-critical structures or these within which full-blown fault id and prognosis is both too time eating or too expensive.

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Extra resources for Adaptive Control of Systems with Actuator Failures

Example text

Iq . 33) with arbitrary k1i (0)? 55) is equivalent to either of T ∗T bi k¯1i = bj ks1j , j = 1, . . ,iq T k ∗T k¯1i ¯∗T = s1j , j = 1, . . , m. 33) we have ∗ ]Γ1i k1i (t) = k1i (0) − sign[ks2i t 0 x(τ )eT (τ )P bM dτ, i = i1 , . . 58) ∗ k¯1i = lim k1i (t) = k1i (0) − sign[ks2i ]Γ1i k¯s1 , i = i1 , . . , iq t→∞ where k¯s1 = limt→∞ t 0 x(τ )eT (τ )P bM dτ . ,iq k1i (0) ¯∗ ⎠ − ks1 . 55), even if arbitrary initial conditions k1i (0), i = 1, . . 33) (it would be an interesting topic to study when this matching condition is met).

Since u ¯j , j = j1 , . . 160). 160). 163) means rank(Ba ) = rank([Ba |Bf u ¯f ]). 160). 165) ∇ Plant-Model Matching with up to m − q Actuator Failures. Now we consider the case of up to m − q actuator failures. 160) are satisfied. Hence, we have the following conditions for plant-model matching in the presence of up to m − q actuator failures. 2. 167) rank(Ba ) = rank(B). 163). 1. 168). 167). 167) may considerably reduce the number of times the matching conditions need to be checked.

2, while the parameters in k3∗ are defined by m i=1 ∗ k3i = 0. 24) ∗ One choice is k3i = 0, i = 1, . . 8). 6). 1. For actuator failure compensation, it is necessary and ∗ ∈ Rn and nonzero constant sufficient that there exist constant vectors ks1i ∗ ∗T ∗ = AM , bi ks2i = bM . scalars ks2i ∈ R, i = 1, . . 21). Such desired controller parameters may not be unique. It is important to note that those parameters depend on the system parameters A and B as well as on the knowledge of the actuator failures.

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